From 461f267746a9aa9245d5e54f254e1331d656362e Mon Sep 17 00:00:00 2001 From: Cherif Date: Sun, 19 Oct 2025 14:06:45 +0200 Subject: [PATCH] 19/10/2025 - 14h Signed-off-by: Cherif --- .idea/workspace.xml | 18 +- Inscription_mgt.py | 55 +- Job_Cron_Common.py | 2 - Log/log_file.log | 36031 ++++++++++++++++ Session_Formation.py | 205 +- .../invoice_RIB_PortCities_perso_tpl.html | 57 +- apprenant_mgt.py | 6 +- base_class_calcul_note.py | 81 +- email_mgt.py | 10 +- internal_email_mgt.py | 141 +- main.py | 11 +- partner_invoice.py | 105 +- prj_common.py | 33 +- temp_directAttestation_.pdf | 148 - 14 files changed, 36653 insertions(+), 250 deletions(-) delete mode 100644 temp_directAttestation_.pdf diff --git a/.idea/workspace.xml b/.idea/workspace.xml index b2fea48..0ef9310 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -4,11 +4,21 @@ - @@ -518,6 +527,7 @@ - \ No newline at end of file diff --git a/Inscription_mgt.py b/Inscription_mgt.py index 6102f52..2deb682 100644 --- a/Inscription_mgt.py +++ b/Inscription_mgt.py @@ -82,7 +82,7 @@ def AddStagiairetoClass(diction): 'tuteur2_nom', 'tuteur2_prenom', 'tuteur2_email', 'tuteur2_telephone', 'tuteur2_adresse', 'tuteur2_cp', 'tuteur2_ville', 'tuteur2_pays', 'tuteur2_include_com','date_naissance', 'financeur_rattachement_id', 'tuteur1_civilite', 'tuteur2_civilite', 'quotation_id', - 'facture_client_rattachement_id', 'tab_ue_ids', + 'facture_client_rattachement_id', 'tab_ue_ids','memo', 'comment' ] incom_keys = diction.keys() @@ -175,6 +175,19 @@ def AddStagiairetoClass(diction): session_id = str(diction['session_id']).strip() mydata['session_id'] = session_id + memo = "" + if ("memo" in diction.keys()): + if diction['memo']: + memo = str(diction['memo']).strip() + mydata['memo'] = memo + + comment = "" + if ("comment" in diction.keys()): + if diction['comment']: + comment = str(diction['comment']).strip() + mydata['comment'] = comment + + """ Verififier l'existance et la valididĂ© de la session @@ -917,7 +930,7 @@ def UpdateStagiairetoClass(diction): 'tuteur1_cp', 'tuteur1_ville', 'tuteur1_pays', 'tuteur1_include_com', 'tuteur2_nom', 'tuteur2_prenom', 'tuteur2_email', 'tuteur2_telephone', 'tuteur2_adresse', 'tuteur2_cp', 'tuteur2_ville', 'tuteur2_pays', 'tuteur2_include_com', 'type_apprenant', 'civilite', - 'date_naissance', 'financeur_rattachement_id', 'facture_client_rattachement_id' + 'date_naissance', 'financeur_rattachement_id', 'facture_client_rattachement_id', 'memo' ] incom_keys = diction.keys() @@ -1149,6 +1162,9 @@ def UpdateStagiairetoClass(diction): if ("comment" in diction.keys()): mydata['comment'] = str(diction['comment']).strip() + if ("memo" in diction.keys()): + mydata['memo'] = str(diction['memo']).strip() + if ("client_rattachement_id" in diction.keys()): mydata['client_rattachement_id'] = str(diction['client_rattachement_id']).strip() @@ -1300,6 +1316,11 @@ def UpdateStagiairetoClass(diction): if diction['comment']: email_data['comment'] = diction['comment'] + if ("memo" in diction.keys()): + if diction['memo']: + email_data['memo'] = diction['memo'] + + if ("code_session" in local_tmp_session_data.keys()): if local_tmp_session_data['code_session']: email_data['code_session'] = local_tmp_session_data['code_session'] @@ -8022,11 +8043,6 @@ def Get_Statgaire_List_Partner_with_filter(diction): - #print(" #### recyclage_warning = ", str(recyclage_warning), " ### recyclage_warning_lead_time = ", str(recyclage_warning_lead_time)) - - - - """ Etape 1 : si on a le champ 'code session' saisie par l'utilisateur, alors on va commencer par aller cherche toutes les session avec un regex de la valeur saisie filter sur le partner_recid @@ -8071,20 +8087,20 @@ def Get_Statgaire_List_Partner_with_filter(diction): filt_class_title = {} if ("class_title" in diction.keys()): - filt_class_title = {'title': {'$regex': str(diction['class_title']), "$options": "i"}} + filt_class_title = {'title': {'$regex': mycommon.regex_replace_cartere(str(diction['class_title'])), "$options": "i"}} filt_class_internal_url = {} if ("class_internal_url" in diction.keys()): filt_class_internal_url = { - 'class_internal_url': {'$regex': str(diction['class_internal_url']), "$options": "i"}} + 'class_internal_url': {'$regex': mycommon.regex_replace_cartere(str(diction['class_internal_url'])), "$options": "i"}} filt_email = {} if ("email" in diction.keys()): - filt_email = {'email': {'$regex': str(diction['email']), "$options": "i"}} + filt_email = {'email': {'$regex': mycommon.regex_replace_cartere(str(diction['email'])), "$options": "i"}} filt_nom = {} if ("nom" in diction.keys()): - filt_nom = {'nom': {'$regex': str(diction['nom']), "$options": "i"}} + filt_nom = {'nom': {'$regex': mycommon.regex_replace_cartere(str(diction['nom'])), "$options": "i"}} filt_class_partner_recid = {'partner_owner_recid': str(partner_recid)} @@ -8804,6 +8820,19 @@ def GetAttendeeDetail_perSession_from_line_id(diction): local_employeur = local_Insc_retval['employeur'] my_retrun_dict['employeur'] = local_employeur + memo = "" + if ("memo" in local_Insc_retval.keys()): + memo = local_Insc_retval['memo'] + my_retrun_dict['memo'] = memo + + + comment = "" + if ("comment" in local_Insc_retval.keys()): + comment = local_Insc_retval['comment'] + my_retrun_dict['comment'] = comment + + + local_telephone = "" if ("telephone" in local_Insc_retval.keys()): local_telephone = local_Insc_retval['telephone'] @@ -12532,12 +12561,12 @@ def Export_Inscription_To_Excel_From_from_List_Id(diction): tab_exported_fields_header = ["apprenant_id", "nom", "email", "prenom", "civilite", "date_naissance", "telephone", "employeur", "client_rattachement_id", "adresse", "code_postal", "ville", "pays", "tuteur1_nom", "tuteur1_prenom", "tuteur1_email", "tuteur1_telephone", "tuteur2_nom", "tuteur2_prenom", "tuteur2_email", "tuteur2_telephone", "opco", "comment", "tuteur1_adresse", "tuteur1_cp", "tuteur1_ville", "tuteur1_pays", "tuteur1_include_com", "tuteur2_adresse", "tuteur2_cp", "tuteur2_ville", "tuteur2_pays", "tuteur2_include_com", - "client_nom", "client_raison_sociale", "Session_titre", "code_session", "session_date_debut", "session_date_fin"] + "client_nom", "client_raison_sociale", "Session_titre", "code_session", "session_date_debut", "session_date_fin", "memo"] tab_exported_fields = ["nom", "email", "prenom", "civilite", "date_naissance", "telephone", "employeur", "client_rattachement_id", "adresse", "code_postal", "ville", "pays", "tuteur1_nom", "tuteur1_prenom", "tuteur1_email", "tuteur1_telephone", "tuteur2_nom", "tuteur2_prenom", "tuteur2_email", "tuteur2_telephone", "opco", "comment", "tuteur1_adresse", "tuteur1_cp", "tuteur1_ville", "tuteur1_pays", - "tuteur1_include_com", "tuteur2_adresse", "tuteur2_cp", "tuteur2_ville", "tuteur2_pays", "tuteur2_include_com"] + "tuteur1_include_com", "tuteur2_adresse", "tuteur2_cp", "tuteur2_ville", "tuteur2_pays", "tuteur2_include_com", "memo"] # Create a workbook and add a worksheet. workbook = xlsxwriter.Workbook(outputFilename) diff --git a/Job_Cron_Common.py b/Job_Cron_Common.py index 53e88d1..380652b 100644 --- a/Job_Cron_Common.py +++ b/Job_Cron_Common.py @@ -858,8 +858,6 @@ def Sent_Convocation_Stagiaire_By_Email(tab_files, Folder, diction): new_file['object_owner_collection'] = "partner_client" new_file['object_owner_id'] = str(inscription_data['client_rattachement_id']) - - new_file['file_name_to_store'] = outputFilename # print(" ### new_file new_file = ", new_file) diff --git a/Log/log_file.log b/Log/log_file.log index 59a7be6..12e4391 100644 --- a/Log/log_file.log +++ b/Log/log_file.log @@ -321183,3 +321183,36034 @@ INFO:werkzeug:127.0.0.1 - - [14/Oct/2025 23:12:18] "POST /myclass/api/Get_List_U INFO:werkzeug:127.0.0.1 - - [14/Oct/2025 23:12:18] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - INFO:root:2025-10-14 23:12:25.865651 : Security check : IP adresse '127.0.0.1' connected INFO:werkzeug:127.0.0.1 - - [14/Oct/2025 23:12:25] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:17:10.718300 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:17:10.718300 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:17:10.718300 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:17:10.718300 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:17:10.718300 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:17:19.326407 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:17:19.326407 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:17:19.326407 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:17:19.326407 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:17:19.326407 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-15 20:20:41.833128 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:41] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:20:43.055009 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:20:43.057009 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:20:43.059011 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:20:43.062534 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:43] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:20:43.077056 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:43] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:43] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:20:43.165959 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:20:43.167958 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:20:43.170467 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:20:43.173477 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:20:43.175476 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:43] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:20:43.180478 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:43] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:43] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:43] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:43] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:43] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:43] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:20:45.710364 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:20:45.711372 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:45] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:45] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:20:49.820919 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:20:49] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:21:01.099542 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:21:01.100542 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:21:01.103544 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:21:01.104544 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:21:01] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:21:01.113543 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:21:01] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:21:01] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:21:01] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:21:01.157609 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:21:01.159612 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:21:01.161615 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:21:01.165128 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:21:01.169135 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:21:01] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:21:01] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:21:01.174648 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:21:01] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:21:01] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:21:01] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:21:01] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:26:14.403270 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:26:14.406248 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:26:14] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:26:14] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:26:44.520227 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:26:44.523627 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:26:44] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:26:44] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:28:44.285613 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:28:44.288610 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:28:44.289611 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:28:44.292610 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:44] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:28:44.302724 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:44] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:44] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:44] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:28:44.340723 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:28:44.342726 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:28:44.343724 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:28:44.347724 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:44] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:28:44.349727 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:28:44.350724 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:44] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:44] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:44] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:44] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:44] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:28:46.678784 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:28:46.680786 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:46] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:46] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:28:50.212429 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:28:50] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:33:11.298073 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:33:11.300073 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:11] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:11] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:33:15.673292 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:33:15.675290 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:33:15.678291 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:33:15.680415 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:15] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:33:15.690416 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:15] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:15] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:15] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:33:15.733413 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:33:15.735412 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:33:15.737415 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:33:15.738412 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:33:15.740412 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:33:15.743415 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:15] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:15] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:15] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:15] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:15] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:15] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:33:17.304479 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:33:17.306467 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:17] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:17] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:33:19.612859 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:33:19] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:36:54.037276 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:36:54.041782 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:36:54] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:36:54] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:37:14.027948 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:37:14.030946 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:37:14.034458 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:14] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:37:14.039776 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:37:14.046203 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:14] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:14] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:14] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:37:14.093699 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:37:14.095702 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:37:14.097701 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:37:14.100702 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:14] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:37:14.103700 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:14] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:37:14.108905 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:14] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:14] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:14] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:14] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:37:19.950922 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:37:19.951921 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:19] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:19] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:37:21.959337 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:37:21] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:39:27.011143 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:39:27.012144 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:39:27] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:39:27] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:41:18.390980 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:41:18.393979 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:41:18] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:41:18] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:46:46.791626 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:46:46.793620 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:46:46] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:46:46] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:47:26.339182 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:47:26] "POST /myclass/api/Store_User_Downloaded_File/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:47:26.385511 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 20:47:26.386516 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:47:26] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:47:26] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:47:35.097934 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:47:35] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:47:35.135901 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:47:35] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:47:45.177750 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:47:45] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:47:54.518762 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:47:54] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:49:56.260716 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:49:56.260716 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:49:56.260716 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:49:56.261616 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:49:56.261616 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:51:31.167304 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:51:31.167304 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:51:31.167304 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:51:31.167304 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:51:31.167304 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:51:43.593342 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:51:43.593342 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:51:43.593342 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:51:43.593342 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:51:43.593342 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-15 20:52:09.682176 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:52:09] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:52:11.355099 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:52:11] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:55:09.755398 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:55:09] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:55:17.679794 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:55:17] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:55:52.545785 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:55:52.546682 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:55:52.546682 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:55:52.546682 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:55:52.546682 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-15 20:55:52.742547 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:55:52] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:55:56.040863 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:55:56] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:56:23.782689 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:56:23.782689 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:56:23.782689 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:56:23.782689 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:56:23.782689 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-15 20:56:23.961905 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:56:23] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:56:27.224329 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:56:27] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:56:53.678397 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:56:53.678397 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:56:53.678397 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:56:53.678397 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:56:53.678397 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-15 20:56:54.727835 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:56:54] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:56:55.798069 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:56:55] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:58:27.043281 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:58:27.044280 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:58:27.044280 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:58:27.044280 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:58:27.044280 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:58:55.107303 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:58:55.107303 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:58:55.107303 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:58:55.107303 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:58:55.107303 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:59:29.248239 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:59:29.248239 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:59:29.249232 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:59:29.249232 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:59:29.249232 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-15 20:59:29.708845 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:59:29] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:59:37.848865 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:59:37] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-15 20:59:53.130149 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-15 20:59:53.130149 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-15 20:59:53.130149 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-15 20:59:53.130149 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-15 20:59:53.130149 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-15 20:59:56.514376 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:59:56] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 20:59:58.396400 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 20:59:58] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:00:06.436647 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:00:06] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:00:54.545009 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:00:54.551008 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:00:54] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:00:54] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:01:12.124342 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:01:12] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:01:14.159563 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:01:14] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:01:15.924349 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:01:15] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:01:18.581050 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:01:18] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:01:20.706420 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:01:20] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:01:25.765981 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:01:25] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:02:54.163611 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:02:54] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:02:59.717503 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:02:59] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:10.571393 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:10] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:34.345757 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:34.346757 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:34.348757 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:34.350757 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:34.351761 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:34.353761 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:34.356762 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:34.359762 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:34.361763 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:34.363762 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:34.368435 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:34.370436 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:34] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:37.164756 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:37.166762 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:37.167800 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:37.169788 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:37.171122 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:37] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:37] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:37] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:37.175578 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:37.176579 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:37] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:37] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:37] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:37] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:39.875962 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:39.877962 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:39.878962 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:39.880964 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:39.882963 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:39] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:39] "POST /myclass/api/Get_Given_SessionFormation_List_Automatic_Traitement_From/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:39] "POST /myclass/api/Get_Given_SessionFormation_From_Id/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:40] "POST /myclass/api/Get_Editable_Document_By_Partner_By_Collection/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:40] "POST /myclass/api/Audit_Session_Action_Inscrit/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:44.725869 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:03:44.726846 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:44] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:45] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:03:56.532115 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:03:56] "POST /myclass/api/Get_List_Conventions_Stagiaire_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:06:08.663588 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:06:08] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:06:09.115582 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:06:09] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:06:46.217781 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:06:46.221759 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:06:46] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:06:46] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:07:54.862176 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:07:54] "POST /myclass/api/Delete_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:07:54.905334 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:07:54] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:07:58.186211 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:07:58] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:10:56.884224 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:10:56.886230 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:10:56] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:10:56] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:11:01.864429 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:11:01] "POST /myclass/api/Store_User_Downloaded_File/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:11:01.914632 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:11:01.915633 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:11:01] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:11:01] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:11:12.645099 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:11:12] "POST /myclass/api/Store_User_Downloaded_File/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:11:12.696442 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:11:12.697438 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:11:12] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:11:12] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:11:41.104202 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:11:41.106104 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:11:41] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:11:41] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:12:59.633209 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:12:59.634946 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:12:59] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:12:59] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:13:07.120919 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:13:07.122918 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:13:07.124918 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:07] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:13:07.128918 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:13:07.135919 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:07] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:07] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:07] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:13:07.169189 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:13:07.171217 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:13:07.172188 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:13:07.173189 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:07] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:13:07.176190 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:13:07.177189 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:07] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:07] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:07] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:07] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:07] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:13:09.096751 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:13:09.098750 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:09] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:09] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:13:11.425829 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:13:11] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:16:04.974104 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:16:04.976122 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:16:04] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:16:04] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:17:40.832736 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:17:40.835738 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:17:40.837741 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:17:40.838738 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:40] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:17:40.846259 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:40] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:40] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:40] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:17:40.878254 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:17:40.879255 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:17:40.881261 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:17:40.883256 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:17:40.884254 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:40] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:40] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:17:40.889260 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:40] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:40] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:40] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:40] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:17:42.543634 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:17:42.546634 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:42] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:42] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:17:44.354965 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:17:44] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:18:31.843661 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:18:31.846659 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:18:31.847660 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:18:31.849660 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:31] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:18:31.857661 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:31] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:31] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:18:31.889661 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:18:31.891662 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:31] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:31] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:31] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:18:31.900661 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:18:31.904662 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:18:31.906661 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:31] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:18:31.908661 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:31] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:31] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:31] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:18:33.588800 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:18:33.590799 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:33] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:33] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:18:35.478401 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:18:35] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:19:36.796892 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:19:36.798864 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:19:36.802389 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:36] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:19:36.808413 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:19:36.814432 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:36] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:36] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:36] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:19:36.849501 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:19:36.853486 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:36] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:19:36.856487 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:19:36.858488 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:36] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:36] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:19:36.863485 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:36] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:19:36.869521 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:36] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:36] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:19:39.441630 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:19:39.443635 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:39] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:39] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:19:41.224189 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:19:41] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:20:03.056583 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:20:03.058581 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:20:03.060581 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:20:03.062583 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:03] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:20:03.074582 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:03] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:03] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:03] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:20:03.116109 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:20:03.118111 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:20:03.119108 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:20:03.121108 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:20:03.124111 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:20:03.126110 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:03] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:03] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:03] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:03] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:03] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:03] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:20:05.587205 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:20:05.588203 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:05] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:05] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:20:07.265869 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:20:07] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:21:05.881615 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:21:05.883616 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:21:05.886616 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:05] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:21:05.893140 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:21:05.895644 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:05] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:05] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:21:05.922162 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:21:05.924168 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:21:05.925168 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:21:05.928163 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:21:05.930163 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:05] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:05] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:05] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:05] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:05] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:21:05.939162 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:05] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:05] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:21:21.070440 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:21:21.072432 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:21] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:21] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:21:25.720526 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:25] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-15 21:21:45.682498 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:21:46.179089 : Get_Stored_Downloaded_File -myprint() takes from 0 to 1 positional arguments but 2 were given - ERRORRRR AT Line : 437 +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:21:46] "GET /myclass/api/Get_Stored_Downloaded_File/g-WQ3wdf8aOoa8KtHK3AKXPlAXQgtzFPmg/pdf_exemple_02.pdf HTTP/1.1" 200 - +INFO:root:2025-10-15 21:22:00.611997 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-15 21:22:00.614997 : Delete_Stored_Downloaded_File -bad operand type for unary +: 'str' - ERRORRRR AT Line : 914 +INFO:werkzeug:127.0.0.1 - - [15/Oct/2025 21:22:00] "POST /myclass/api/Delete_Stored_Downloaded_File/ HTTP/1.1" 200 - +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 20:40:03.096526 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 20:40:03.096526 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 20:40:03.097527 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 20:40:03.097527 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 20:40:03.097527 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 20:40:33.343072 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 20:40:33.343994 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 20:40:33.345029 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 20:40:33.345029 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 20:40:33.345029 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 20:41:48.871105 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:49] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:41:50.882082 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:41:50.886193 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:41:50.892082 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:41:50.902083 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:50] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:50] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:41:50.965640 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:50] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:41:50.981728 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:41:50.991062 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:41:50.994953 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:41:51.010982 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:51] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:41:51.023602 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:41:51.041487 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:51] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:51] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:51] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:51] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:51] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:51] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:41:59.032547 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:41:59.041091 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:59] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:41:59] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:42:29.107005 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:42:29] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:47:05.299856 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:47:05.303384 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:47:05] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:47:05] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:49:05.937870 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:49:05.943201 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:49:05] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:49:05] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:51:04.698445 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:51:04.703972 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:51:04] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:51:04] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:52:10.409143 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:52:10.415349 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:52:10] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:52:10] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:52:46.484236 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:52:46.488224 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:52:46] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:52:46] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:53:03.161862 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:53:03.165868 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:53:03] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:53:03] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:53:46.894209 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:53:46.897293 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:53:46] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:53:46] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:54:44.920839 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:54:44.926872 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:54:44] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:54:44] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:55:05.697295 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:05.699603 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:05.704017 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:05.708020 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:05] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:55:05.724021 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:05] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:05] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:05] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:55:05.790900 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:05.794005 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:05.798523 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:05.802524 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:05] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:05] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:05] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:55:05.814534 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:05.815527 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:05] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:05] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:05] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:55:26.594670 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:26.597982 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:26.600992 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:26.604255 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:26.609784 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:26.615788 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:26.618783 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:26] "POST /myclass/api/Get_Partner_All_Class_Few_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:26] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:26] "POST /myclass/api/Get_Partner_Session_Ftion_Reduice_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:26] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:26] "POST /myclass/api/Get_List_Conseil_Classe/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:26] "POST /myclass/api/Get_List_Unite_Enseignement_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:26] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:55:28.614107 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:28.620530 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:28.625534 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:28.630054 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:55:28.635055 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:28] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:28] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class_From_Session_Id/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:28] "POST /myclass/api/Get_Given_Jury_With_Members/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:28] "POST /myclass/api/Get_Given_Jury_Apprenant_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:28] "POST /myclass/api/Get_List_Jury_Soutenenace/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:55:28.717052 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:28] "POST /myclass/api/Get_List_Groupe_Inscrit_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:55:31.808859 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:55:31] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:56:49.052985 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:56:49.054984 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:56:49.058985 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:56:49.063721 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:49] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:56:49.069746 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:56:49.071750 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:49] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:49] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:56:49.082126 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:56:49.084541 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:49] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:56:49.094099 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:56:49.096100 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:49] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:49] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:56:49.103924 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:56:49.113501 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:49] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:49] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:49] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - 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- [16/Oct/2025 20:56:52] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:52] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:52] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:52] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:52] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:52] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:56:58.057953 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:56:58.061314 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:56:58.065323 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:56:58.071322 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:58] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:56:58.076328 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:58] "POST /myclass/api/Get_Given_SessionFormation_List_Automatic_Traitement_From/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:58] "POST /myclass/api/Get_Given_SessionFormation_From_Id/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:58] "POST /myclass/api/Get_Editable_Document_By_Partner_By_Collection/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:56:58] "POST /myclass/api/Audit_Session_Action_Inscrit/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:57:02.956228 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 20:57:02.962674 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:57:02] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - 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N° de déclaration d\'activité :  NDA-001254 

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 Est conclue la convention suivante entre
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L\'Organisme de formation  Colas Rail   ci-après nommé l\'Organisme de formation
Situé : rue de la république 77 paris 
Déclation d\'activité :  NDA-001254  Représenté par : [PDG]
Signataire dûment habilité aux fins des présentes : prenom nonn 
Contact :   
Téléphone :  
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Et

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Et le bénéciaire :
 qsdsq qsdqs 
(Ci-aprés nommé le client)
Situé :     

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PRÉAMBULE
EZUS Lyon   Colas Rail filiale de Valorisation de l\'Université Claude Bernard Lyon I, s\'est vue confier la gestion des activités industrielles et commerciales des centres et services de l\'UCBL, ainsi que les actions de formation continue par la convention cadre signée le 23 janvier 2008.

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A cet effet, la société Colas Rail  fait appel au : Docteur Eric J. VOIGLIO, MD, PhD, FACS, FRCS Directeur ATLS France.

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Est conclu un contrat de formation professionnelle en application de l\'article L6353-3 du Code du Travail.

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Article 1 : Objet, nature, et durée de la formation
Le bénéciaire entend faire participer une partie de son personnel à l\'action de formation suivante organisée par l\'organisme de formation

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 Colas Rail 

\nFormation : CONDUIRE UN PROJET 
Durée : 15.0   heure (s) 
Lieu de formation :   vile sit1 
Dates de formation :  13/10/2025  14/10/2025 
Nom formateur : nom_Enseignant_2 Prenom_2 
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Article 2 : Programme de la formation
La description detaillée du programme (méthodes pédagogiques, évaluation) est fourni au client

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Article 3 : Engagement de participation à la formation
Le bénéciaire s\'engage à assurer la présence du / des stagiaire/s aux dates et lieux prévus ci-dessus.

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Article 4 : Prix de la formation
La formation est réglée en totalité par le client
Coût pédagogique par stagiaire :  1500.0 €

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Article 5 : Modalités de réglement
orem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

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Article 6 : Moyes pédagogiques et techniques mises en oeuvre
orem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

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Article x : Litiges
orem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

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Paris , 16/10/2025 

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Pour l\'organisme de formation prenom   nonn 

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Pour le client :
Nom & Prénom du Signataire

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 Colas Rail 
Téléphone : 07 69 20 39 45 
Email :  mysytraining+dev@gmail.com 
Site :  https://colasrail.com/ 

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' + dest = <_io.BufferedRandom name='./temp_direct/Convention_43598_00326.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:58:06] "POST /myclass/api/Prepare_and_Send_Convention_From_Session_For_Selected_Inscrit_By_Email/ HTTP/1.1" 200 - +INFO:root:2025-10-16 20:58:06.897530 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 20:58:07] "POST /myclass/api/Get_Editable_Document_By_Partner_By_Collection/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\main.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:02:05.230071 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:02:05.231070 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:02:05.231070 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:02:05.231070 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:02:05.231070 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:04:27.376370 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:04:27.376370 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:04:27.377362 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:04:27.377362 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:04:27.377362 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:04:50.781665 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:04:50.781665 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:04:50.781665 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:04:50.781665 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:04:50.781665 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:05:18.757432 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:05:18.757432 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:05:18.757432 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:05:18.757432 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:05:18.757432 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 21:06:10.627540 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:06:10.629547 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:06:10.631550 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:06:10.634909 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:06:10.637920 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:06:10.640160 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:06:10.651407 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:10] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:06:10.655900 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:10] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:10] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:06:10.665245 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:10] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:10] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:10] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:06:10.675309 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:06:10.677302 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:10] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:10] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:10] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:10] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:06:12.737834 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:06:12.739835 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:12] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:12] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:06:14.358162 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:14] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:06:50.078419 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:50] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:06:50.173789 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:06:50] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:08:14.286792 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:08:14.290792 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:08:14] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:08:14] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:08:14.420869 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:08:14.423869 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:08:14] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:08:14] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:08:16.003349 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:08:16] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:10:02.808140 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:10:02.810145 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:10:02] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:10:02] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:10:17.556907 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:10:17] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:10:17.650719 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:10:17] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:11:15.414622 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:11:15] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:11:32.243872 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:11:32] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:11:32.304586 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:11:32] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:12:34.799112 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:12:34.803613 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:12:34] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:12:34] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:13:17.194193 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:13:17.197192 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:13:17.205216 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:13:17.209196 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:17] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:13:17.243314 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:13:17.249871 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:13:17.258974 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:13:17.259950 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:17] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:17] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:17] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:13:17.281948 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:13:17.299862 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:13:17.304864 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:17] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:17] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:17] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:17] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:17] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:13:39.299992 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:13:39.302999 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:39] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:39] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:13:41.640281 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:41] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:13:43.649578 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:13:43] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:15:02.496381 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:15:02.496381 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:15:02.496381 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:15:02.496381 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:15:02.496381 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 21:16:46.069676 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:16:46.072733 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:16:46] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:16:46] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:16:51.326982 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:16:51] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:17:40.228827 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:17:40.230829 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:17:40] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:17:40] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:17:56.399751 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:17:56] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:19:06.274745 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:19:06] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:19:07.518470 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:19:07] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:19:12.473905 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:19:12] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:20:39.041400 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:20:39.045411 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:20:39] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:20:39] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:20:52.831328 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:20:52] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:20:58.148418 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:20:58] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:21:00.443628 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:21:00] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:21:02.768626 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:21:02] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:22:54.215076 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:22:54.223977 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:22:54] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:22:54] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:23:28.723750 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:23:28] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:23:31.239209 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:23:31] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:23:44.556775 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:23:44] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:23:51.613138 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:23:51] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:24:15.381266 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:24:15] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:24:18.840116 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:24:18] "GET /myclass/api/Get_Stored_Downloaded_File_From_Id/L1EJ8zRyR6fONWewXteNqHLcCCykXCdI-Q/68eff1d0d980c4ab7268ba81 HTTP/1.1" 200 - +INFO:root:2025-10-16 21:24:26.491457 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:24:26.534461 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:24:26.538744 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:24:26.542908 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:24:26.545917 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:24:26.552470 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:24:26] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:24:26.561333 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:24:26] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:24:26] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:24:26] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:24:26] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:24:26] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:24:26] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:24:32.266536 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:24:32] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:25:33.820643 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:25:33] "GET /myclass/api/Get_Stored_Downloaded_File_From_Id/L1EJ8zRyR6fONWewXteNqHLcCCykXCdI-Q/68eff1d0d980c4ab7268ba81 HTTP/1.1" 304 - +INFO:root:2025-10-16 21:29:16.239206 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:16] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:16.324059 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:16.326068 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:16.327067 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:16.331067 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:16.334070 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:16.337069 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:16] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:16] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:16] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:16] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:16] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:16] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:17.528933 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:17] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:18.652151 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:18.654685 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:18.657748 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:18.659274 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:18.664289 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:18.667294 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:18.672644 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:18.680170 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_List_Class_Niveau_Formation/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:18.684216 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:18.686441 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_List_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:18.695460 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:18.699458 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:18.703502 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/getRecodedClassImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:18] "POST /myclass/api/Get_List_Unite_Enseignement_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:19.022703 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:19.025238 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:19.027219 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:19] "POST /myclass/api/getRecodedClassImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:19] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:19] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:34.362088 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:34] "POST /myclass/api/Store_User_Downloaded_File/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:41.308478 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:41.309479 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:41.314479 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:41] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:41.325487 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:41] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:41] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:41.376723 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:41.378723 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:41.380721 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:41.382722 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:41.384722 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:41.388725 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:41] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:41] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:41] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:41] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:41] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:41] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:44.221683 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:44] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:45.428967 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:45] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:46.561573 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:46.563570 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:46.566572 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:46.568574 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:46.574641 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:46.579575 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:46.587578 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:46.593588 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:46.598598 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:46.602601 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_List_Class_Niveau_Formation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:46.614145 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:46.630145 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:46.635149 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_List_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/getRecodedClassImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/Get_List_Unite_Enseignement_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:46.938740 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:46] "POST /myclass/api/getRecodedClassImage/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:46.982493 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:29:46.999520 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:47] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:47] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:29:57.579650 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:29:57] "GET /myclass/api/Get_Stored_Downloaded_File/L1EJ8zRyR6fONWewXteNqHLcCCykXCdI-Q/pdfexemple01_20251016_212934_43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89.pdf HTTP/1.1" 200 - +INFO:root:2025-10-16 21:30:48.809519 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:30:48] "POST /myclass/api/Delete_Stored_Downloaded_File/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:30:48.908627 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:30:48.912628 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:30:48] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:30:48] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:31:00.970365 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:31:00] "POST /myclass/api/Store_User_Downloaded_File/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:36:10.779984 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:36:10.779984 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:36:10.779984 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:36:10.780989 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:36:10.780989 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:37:08.974095 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:37:08.974095 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:37:08.975093 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:37:08.975093 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:37:08.975093 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 21:37:13.172618 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:37:13] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:37:38.248588 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:37:38] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:37:38.323254 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:37:38] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:40:25.619121 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:40:25.620158 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:40:25.620158 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:40:25.620158 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:40:25.620158 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 21:40:25.839100 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:40:25] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:40:45.991702 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:40:46] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:40:46.055800 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:40:46] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\main.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:43:37.427600 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:43:37.427600 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:43:37.427600 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:43:37.427600 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:43:37.427600 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 21:43:38.157802 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:43:38] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:43:47.988643 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:43:48] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:43:48.058440 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:43:48] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:45:09.437211 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:45:09.438192 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:45:09.438192 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:45:09.438192 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:45:09.438192 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:47:47.780347 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:47:47.780347 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:47:47.780347 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:47:47.780347 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:47:47.780347 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:49:58.228473 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:49:58.229372 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:49:58.229372 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:49:58.229372 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:49:58.229372 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 21:50:26.401590 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:50:26] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:50:38.520749 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:50:38] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:50:38.616144 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:50:38] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:52:26.133117 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:52:26] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:52:28.629045 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:52:28] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:52:29.890883 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:52:29] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:53:40.203110 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:53:40.203110 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:53:40.203110 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:53:40.203110 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:53:40.203110 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:54:02.904732 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:54:02.905733 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:54:02.905733 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:54:02.905733 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:54:02.905733 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:56:39.340932 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:56:39.340932 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:56:39.340932 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:56:39.340932 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:56:39.340932 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:56:57.721553 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:56:57.722546 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:56:57.722546 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:56:57.722546 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:56:57.722546 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 21:56:58.502253 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:56:58] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:57:05.953822 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:57:05] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 21:57:08.499331 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:57:08] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:58:53.719716 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:58:53.719716 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:58:53.719716 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:58:53.719716 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:58:53.719716 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:59:04.704959 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:59:04.704959 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:59:04.704959 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:59:04.704959 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:59:04.704959 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 21:59:10.085752 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 21:59:14.152869 : Get_Given_Internal_Mail -skip must be an instance of int - Line : 711 +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:59:14] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 21:59:58.791488 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 21:59:58.792478 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 21:59:58.792478 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 21:59:58.792478 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 21:59:58.792478 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 21:59:58.996899 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 21:59:59] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 22:01:19.833514 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 22:01:19.838512 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 22:01:19.838512 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 22:01:19.838512 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 22:01:19.839517 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 22:01:58.902540 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:01:58.906538 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:01:58] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:01:58] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:02:30.277190 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:02:30] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:02:30.294727 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:02:30] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:02:50.997722 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:02:51] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:02:51.880695 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:02:51] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:02:54.353000 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:02:54] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:04:11.887394 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:04:11.890402 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:04:11] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:04:11] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:06:17.478838 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:17] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:06:17.516944 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:17] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:06:57.031556 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:06:57.034545 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:06:57.035545 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:06:57.038604 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:57] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:06:57.052136 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:57] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:57] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:57] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:06:57.107558 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:06:57.110607 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:06:57.112614 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:06:57.114614 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:06:57.117616 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:06:57.121618 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:57] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:57] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:57] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:57] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:57] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:57] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:06:59.637759 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:06:59.639761 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:59] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:06:59] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:07:01.449978 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:07:01] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:07:26.750049 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:07:26] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:07:26.821222 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:07:26] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:07:29.816898 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:07:29] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:07:31.447046 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:07:31] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:10:31.276413 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:10:31.284507 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:10:31.292514 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:10:31.308427 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:10:31] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:10:31.333390 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:10:31] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:10:31] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:10:31] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:10:31.696458 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:10:31.706462 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:10:31.714357 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:10:31] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:10:31] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:10:31] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:10:31.764595 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:10:31.771593 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:10:31.780727 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:10:31] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:10:31] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:10:31] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 22:12:16.921699 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 22:12:16.921699 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 22:12:16.921699 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 22:12:16.921699 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 22:12:16.921699 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 22:13:14.839068 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 22:13:14.839068 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 22:13:14.839068 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 22:13:14.839068 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 22:13:14.839068 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 22:13:47.314054 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:13:47.315051 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:13:47] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:13:47] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:13:48.740829 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:13:48] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:13:50.256319 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:13:50] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:14:26.639439 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:14:26.642894 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:14:26.643888 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:14:26.648836 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:26] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:26] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:14:26.673352 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:26] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:26] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:14:26.718407 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:14:26.720407 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:14:26.723409 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:14:26.726408 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:14:26.729408 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:14:26.730408 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:26] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:26] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:26] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:26] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:26] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:26] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:14:28.244529 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-16 22:14:28.246529 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:28] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:28] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:14:30.455749 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:30] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:14:33.740718 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:33] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:14:42.734278 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:42] "GET /myclass/api/Get_Stored_Downloaded_File/L1EJ8zRyR6fONWewXteNqHLcCCykXCdI-Q/CorrigeTD01_20251016220726_20251016_220726_43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89.pdf HTTP/1.1" 200 - +INFO:root:2025-10-16 22:14:46.278193 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:14:46] "GET /myclass/api/Get_Stored_Downloaded_File/L1EJ8zRyR6fONWewXteNqHLcCCykXCdI-Q/pdfexemple02_20251016220726_20251016_220726_43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89.pdf HTTP/1.1" 200 - +INFO:root:2025-10-16 22:15:23.033009 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:15:23] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:15:51.646823 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:15:51] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:15:51.722985 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:15:51] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:15:55.768724 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:15:55] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:15:56.767501 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:15:56] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:15:58.120248 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:15:58] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:16:03.627042 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:16:03] "GET /myclass/api/Get_Stored_Downloaded_File/L1EJ8zRyR6fONWewXteNqHLcCCykXCdI-Q/CorrigeTD01_20251016221551_20251016_221551_43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89.pdf HTTP/1.1" 200 - +INFO:root:2025-10-16 22:16:06.842688 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:16:06] "GET /myclass/api/Get_Stored_Downloaded_File/L1EJ8zRyR6fONWewXteNqHLcCCykXCdI-Q/pdfexemple01_20251016221551_20251016_221551_43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89.pdf HTTP/1.1" 200 - +INFO:root:2025-10-16 22:16:10.430797 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:16:10] "GET /myclass/api/Get_Stored_Downloaded_File/L1EJ8zRyR6fONWewXteNqHLcCCykXCdI-Q/pdfexemple02_20251016221551_20251016_221551_43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89.pdf HTTP/1.1" 200 - +INFO:root:2025-10-16 22:22:04.153496 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:22:04] "GET /myclass/api/Cron_Send_Mail_Queue_Message_With_Filter HTTP/1.1" 308 - +INFO:root:2025-10-16 22:22:04.162496 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:22:09] "GET /myclass/api/Cron_Send_Mail_Queue_Message_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 22:37:51.360117 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 22:37:51.360117 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 22:37:51.360117 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 22:37:51.361116 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 22:37:51.361116 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-16 22:38:00.374631 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-16 22:38:00.374631 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-16 22:38:00.374631 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-16 22:38:00.374631 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-16 22:38:00.374631 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-16 22:38:13.371028 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:38:13] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:38:47.280345 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:38:47] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:38:49.965728 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:38:49] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:39:19.189877 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:39:19] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-16 22:39:21.707771 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [16/Oct/2025 22:39:21] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:09:00.208881 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:09:00.208881 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:09:00.209882 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:09:00.209882 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:09:00.209882 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:09:09.249563 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:09:09.249563 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:09:09.250568 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:09:09.250568 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:09:09.250568 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 10:13:05.385259 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:05] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:07.008323 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:07.010324 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:07.012331 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:07.015330 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:07] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:07] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:07.028841 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:07] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:07.056841 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:07.058840 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:07.060840 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:07] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:07.062839 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:07] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:07.066842 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:07.068843 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:07] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:07] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:07] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:07] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:07] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:09.168945 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:09.170951 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:09] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:09] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:12.073090 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:12] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:21.126729 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:21.129729 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:21.131727 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:21.134733 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:21] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:21.146733 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:21] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:21] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:21] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:21.202352 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:21.203350 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:21.205352 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:21.206349 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:21.208350 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:21.210349 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:21] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:21] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:21] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:21] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:21] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:21] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:30.750085 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:30.752083 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:30.754115 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:30.755082 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:30.757083 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:30.760083 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:13:30.762084 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:30] "POST /myclass/api/Get_List_Manager_Ressource_Humaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:30.763085 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:30] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:30] "POST /myclass/api/Get_List_Profil_Ressource_Humaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:30] "POST /myclass/api/Get_Competence_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:30] "POST /myclass/api/Get_Competence_Level/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:30] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:30] "POST /myclass/api/Get_Related_Target_Collection_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:30] "POST /myclass/api/Get_List_Survey_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:13:38.996381 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:13:39] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:01.464174 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:01] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:02.941005 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:02] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:03.247940 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:15:03.249945 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:15:03.249945 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:03] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:03.257939 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:03] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:03] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:03.325724 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:15:03.329733 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:15:03.332731 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:03] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:03] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:03] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:03.562228 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:15:03.563210 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:15:03.567193 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:03] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:03] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:03] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:05.295399 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:15:05.296403 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:05] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:05] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:50.483811 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:50] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:50.523482 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:50] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:55.597105 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:55] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:57.706199 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:57] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:15:59.755229 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:15:59] "GET /myclass/api/Get_Stored_Downloaded_File/n3GBA0ftE_1pndv91Wyit0ee3i-Ecudsrw/CorrigeTD01_20251017101550_20251017_101550_43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89.pdf HTTP/1.1" 200 - +INFO:root:2025-10-17 10:16:12.750021 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:16:12] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:16:13.240041 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:16:13] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:22:26.050817 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:22:26.050817 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:22:26.050817 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:22:26.050817 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:22:26.050817 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:23:00.187151 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:23:00.187151 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:23:00.187151 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:23:00.187151 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:23:00.187151 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:24:32.778127 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:24:32.779126 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:24:32.779126 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:24:32.779126 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:24:32.779126 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:25:07.517358 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:25:07.518359 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:25:07.518359 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:25:07.518359 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:25:07.518359 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:25:48.509224 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:25:48.509224 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:25:48.509224 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:25:48.509224 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:25:48.509224 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:26:29.605230 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:26:29.605230 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:26:29.606233 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:26:29.606233 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:26:29.606233 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:27:23.722683 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:27:23.722683 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:27:23.722683 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:27:23.722683 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:27:23.722683 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:27:55.301085 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:27:55.301085 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:27:55.301085 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:27:55.301085 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:27:55.302085 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:29:27.839076 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:29:27.839076 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:29:27.839076 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:29:27.839076 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:29:27.839076 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 10:29:41.428476 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:29:41] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:29:42.934732 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:29:43.526241 : Get_List_User_Internal_Mail -filter must be an instance of dict, bson.son.SON, or any other type that inherits from collections.Mapping - Line : 502 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:29:43] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\prj_common.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:38:47.056057 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:38:47.057057 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:38:47.057057 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:38:47.057057 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:38:47.057057 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\prj_common.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:42:18.482774 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:42:18.482774 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:42:18.482774 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:42:18.482774 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:42:18.483392 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\prj_common.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:44:54.079250 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:44:54.080266 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:44:54.080266 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:44:54.080266 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:44:54.080266 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:45:07.885182 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:45:07.886181 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:45:07.886181 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:45:07.886181 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:45:07.886181 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 10:45:41.620138 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:45:41] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:45:42.456816 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:45:42.926159 : Get_List_User_Internal_Mail -filter must be an instance of dict, bson.son.SON, or any other type that inherits from collections.Mapping - Line : 502 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:45:42] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:47:32.879788 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:47:32.879788 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:47:32.880805 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:47:32.880805 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:47:32.880805 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 10:49:28.555310 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:49:28] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:49:29.151571 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:49:31.625906 : Get_List_User_Internal_Mail -pipeline must be a list - Line : 502 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:49:31] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:52:04.965158 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:52:04.965158 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:52:04.965158 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:52:04.965158 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:52:04.965158 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 10:52:06.400539 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:52:06] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:52:07.128791 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:52:09.485513 : Get_List_User_Internal_Mail -'CommandCursor' object has no attribute 'sort' - Line : 502 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:52:09] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:53:29.640383 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:53:29.640383 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:53:29.640383 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:53:29.640383 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:53:29.640383 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:53:44.609462 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:53:44.609462 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:53:44.609462 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:53:44.609462 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:53:44.609462 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 10:53:48.591654 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:53:48] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:53:49.256237 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 10:53:49.728188 : Get_List_User_Internal_Mail -'CommandCursor' object has no attribute 'sort' - Line : 500 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:53:49] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:54:13.954592 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:54:13.954592 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:54:13.954592 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:54:13.954592 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:54:13.954592 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 10:54:14.279918 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:54:14] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:54:14.991123 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:54:14] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 10:55:28.308443 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 10:55:28.308443 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 10:55:28.308443 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 10:55:28.308443 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 10:55:28.308443 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 10:55:31.142165 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:55:31] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:55:31.912361 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:55:31] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:55:33.648483 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:55:33] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:55:34.744149 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:55:34] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 10:55:37.745284 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 10:55:37] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:06:02.256287 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:06:02] "GET /myclass/api/Get_Stored_Downloaded_File/hRA3m6YCKiCkTbAx3DN0hX7Yy8CIDNB3PA/CorrigeTD01_20251017101550_20251017_101550_43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89.pdf HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:08:49.892968 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:08:49.893968 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:08:49.893968 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:08:49.893968 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:08:49.894981 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:09:36.581644 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:09:36.581644 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:09:36.581644 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:09:36.581644 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:09:36.581644 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:17:18.686786 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:17:18.686786 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:17:18.686786 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:17:18.686786 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:17:18.686786 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:18:14.895226 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:18:14.895226 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:18:14.895226 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:18:14.895226 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:18:14.895226 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:21:49.200277 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:21:49.200277 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:21:49.200277 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:21:49.200277 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:21:49.201276 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:21:56.753561 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:21:56.753561 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:21:56.753561 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:21:56.753561 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:21:56.753561 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:22:35.946544 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:22:35.946544 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:22:35.946544 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:22:35.946544 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:22:35.946544 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 11:34:01.970971 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:34:01.975533 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:34:01.979536 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:01] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:01] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:02] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:34:02.458151 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:34:02.461150 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:34:02.464252 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:02] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:02] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:02] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:34:51.448054 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:51] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:34:51.759721 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:34:51.762917 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:34:51.768933 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:34:51.775932 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:51] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:34:51.784935 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:51] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:51] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:51] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:51] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:34:56.978354 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:57] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:34:57.749000 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:57] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:34:58.054846 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:34:58.056860 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:58] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:34:58] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:37.769867 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:37.770871 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:37.771893 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:37.772871 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:37.774872 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:37] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:37.788869 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:37] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:37] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:37] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:37] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:37.894104 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:37] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.100073 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.101076 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.101076 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.102073 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.130110 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.183177 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.187189 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.192171 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.194173 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.196173 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.202172 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.205177 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.205177 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.213172 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.216173 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.222174 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.545096 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.547096 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.550117 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.553096 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.558175 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.561178 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.777644 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.780633 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.782641 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.785631 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.788633 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.790632 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_List_Manager_Ressource_Humaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_List_Profil_Ressource_Humaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:38.796630 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:38.797632 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_Competence_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_Competence_Level/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_List_Survey_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:38] "POST /myclass/api/Get_Related_Target_Collection_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:43.545579 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:43] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:39:43.854924 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:39:43.855923 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:43] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:39:43] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:44:20.807849 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:44:20.807849 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:44:20.807849 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:44:20.807849 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:44:20.807849 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:44:29.965625 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:44:29.965625 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:44:29.965625 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:44:29.965625 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:44:29.965625 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 11:45:51.818808 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:45:51.819805 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:45:51.821984 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:45:51] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:45:51] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:45:52.467853 : Get_Nb_User_Internal_Mail_Not_Read -the match filter must be an expression in an object, full error: {'ok': 0.0, 'errmsg': 'the match filter must be an expression in an object', 'code': 15959, 'codeName': 'Location15959'} - Line : 650 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:45:52] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:47:01.110835 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:47:01.111835 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:47:01.111835 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:47:01.111835 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:47:01.111835 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:48:07.797012 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:48:07.797012 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:48:07.798018 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:48:07.798018 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:48:07.798018 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:49:00.842732 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:49:00.842732 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:49:00.842732 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:49:00.842732 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:49:00.842732 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:50:02.304907 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:50:02.304907 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:50:02.305907 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:50:02.305907 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:50:02.305907 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 11:51:18.396595 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:18] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:51:19.226110 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:19] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:51:35.914644 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:51:35.915644 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:51:35.917949 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:51:35.921951 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:51:35.923950 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:35] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:51:35.950048 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:35] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:35] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:35] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:51:35.977582 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:35] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:35] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:51:36.258633 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:51:36.264514 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:51:36.268521 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:51:36.270519 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:36] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:36] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:36] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:36] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:51:36.291232 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:51:36] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:53:23.863951 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:53:23.863951 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:53:23.864845 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:53:23.864845 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:53:23.864845 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 11:53:41.051426 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:53:41.053426 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:53:41.054425 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:53:41.054425 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:53:41.057425 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:53:41.059423 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:53:41] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:53:41] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:53:41.072429 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:53:41] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:53:41] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:53:41] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:53:41.372228 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:53:41] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:53:41.378228 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:53:41.379227 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:53:41.379227 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:53:41] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:53:41] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:53:41] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:53:41.631261 : Get_Nb_User_Internal_Mail_Not_Read -'CommandCursor' object is not subscriptable - Line : 656 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:53:41] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:55:24.066354 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:55:24.066354 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:55:24.066354 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:55:24.066354 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:55:24.066354 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 11:55:30.530519 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:55:30.530519 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:55:30.533688 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:55:30.534694 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:55:30.536801 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:55:30.537806 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:30] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:55:30.543317 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:30] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:30] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:30] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:30] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:30] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:55:30.845916 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:30] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:55:30.858914 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:55:30.858914 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:55:30.860913 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:30] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:30] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:30] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:55:57.582323 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:55:57.582323 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:55:57.582323 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:55:57.582323 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:55:57.582323 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 11:55:59.038382 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:55:59.038382 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 11:55:59.040375 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:59] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:59] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:55:59] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:57:49.573548 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:57:49.573548 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:57:49.573548 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:57:49.574566 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:57:49.574566 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 11:57:51.400716 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:57:51] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:57:56.056501 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:57:56] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 11:58:42.528396 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 11:58:42.528396 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 11:58:42.528396 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 11:58:42.528396 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 11:58:42.528396 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 11:58:43.548494 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:58:43] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 11:58:46.872379 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 11:58:46] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 12:00:24.464766 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 12:00:24.464766 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 12:00:24.464766 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 12:00:24.464766 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 12:00:24.465780 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 12:00:25.456184 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:25.459183 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:25.464180 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:25.474184 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:25.479175 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:25.499173 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:25.506176 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:25.801913 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:25.820966 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:25.824946 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:25.838518 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:25.844518 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:25] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:29.101112 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:29.104112 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:29.108113 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:29] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:29] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:29] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:30.832587 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:30] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:40.121627 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:40.125965 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:40.128963 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:40.139963 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:40.141999 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:40.145963 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:40.161966 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:40.461779 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:40.463796 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:40.465781 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:40.480834 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:40.488381 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:40] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:42.205088 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:42] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:00:42.510220 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:00:42.511202 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:42] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:00:42] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:01.856493 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:01.858494 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:01] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:01.868381 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:01.873382 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:01.875433 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:01.884386 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:01] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:01] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:01] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:01] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:02.012567 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:02] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:02.184143 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:02.200451 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:02.205572 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:02] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:02.212506 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:02] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:02] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:02] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:02.230527 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:02] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:03.614213 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:03.620229 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:03] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:03.632228 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:03.637234 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:03] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:03.648614 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:03] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:03] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:03.760886 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:03.764783 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:03] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:03:03.773848 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:03.781785 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:03:03.789780 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:03] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:03] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:03:03] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:05:24.585839 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:05:24.590847 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:05:24.598847 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:24] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:05:24.610801 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:05:24.613799 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:24] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:24] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:24] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:05:24.663026 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:05:24.666041 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:24] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:24] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:05:24.929916 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:05:24.931927 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:05:24.934487 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:24] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:24] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:05:24.947499 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:24] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:24] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:05:26.286738 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:26] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:05:26.591778 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:05:26.594753 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:26] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:26] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:05:28.899770 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:05:28] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:06:53.470796 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:06:53.471809 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:06:53.473809 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:06:53.480820 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:06:53.491834 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:06:53.501836 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:06:53.736800 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:06:53.864790 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:06:53.865812 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:06:53.868823 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:06:53.872832 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:06:53.875837 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:06:53] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:10.032709 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:10.034709 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:10.039712 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:10.044713 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:10.062805 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:10.397984 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:10.404984 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:10.413535 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:10.421549 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:10.430551 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:10.547659 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:10.549647 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:10.561188 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:10.568954 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:10.573955 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:10] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:33.214118 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:33.217120 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:33.221117 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:33] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:33.232125 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:33.241433 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:33] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:33] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:33.250880 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:33] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:33.253881 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:33] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:33] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:33.551693 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:33.555725 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:33.557755 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:33] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:33.572291 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:33] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:33] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:33] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:37.415091 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:37] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:37.732940 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:07:37.738953 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:37] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:37] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:42.827552 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:42] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:43.095620 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:43] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:55.937489 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:55] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:07:56.279503 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:07:56] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 12:08:28.196144 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 12:08:28.196144 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 12:08:28.196144 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 12:08:28.196144 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 12:08:28.196144 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 12:08:47.543602 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 12:08:47.543602 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 12:08:47.543602 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 12:08:47.543602 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 12:08:47.543602 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 12:10:57.822724 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:10:57.822724 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:10:57.825708 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:10:57] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:10:57] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:10:57] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:12:51.032930 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:12:51.037031 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:12:51.039942 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:12:51.040940 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:12:51.041942 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:12:51.055093 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:12:51.072245 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:12:51.370685 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:12:51.371685 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:12:51.372682 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:12:51.375686 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:12:51.386235 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:51] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:12:53.202696 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:53] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:12:53.508995 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:12:53.511005 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:53] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:53] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:12:55.265320 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:55] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:12:55.517654 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:55] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:12:55.597174 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:12:55] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:13:11.131680 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:13:11.133678 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:13:11.134679 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:13:11] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:13:11] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:13:11] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:13:13.708276 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:13:13] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:13:14.453154 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:13:14] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:13:16.407027 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:13:16] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:13:21.214265 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:13:21] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:13:35.539723 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:13:35] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:13:40.227648 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:13:40] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 12:15:07.887222 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 12:15:07.888231 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 12:15:07.888231 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 12:15:07.888231 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 12:15:07.888231 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 12:15:28.915328 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:28.917329 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:28.917329 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:28.918325 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:28.921349 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:28.922596 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:28] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:28.933602 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:28] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:28] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:28] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:28] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:28] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:29.245728 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:29.246727 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:29.250852 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:29] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:29] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:29] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:29.262424 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:29.263407 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:29] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:29] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:31.927985 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:31.930005 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:31.932986 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:31] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:31] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:31] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:34.785706 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:34] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:35.134751 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:35.140765 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:35] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:35] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:41.213536 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:41.532672 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:41.534671 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:41.538672 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:41.539673 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:41.553698 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:41.553698 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:41.572763 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:41.856011 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:41.868014 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:41.869992 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:41.873992 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:41] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:15:44.029427 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:44.033688 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:15:44.037680 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:44] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:44] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:15:44] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:16:01.786384 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:01] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:16:02.100633 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:02.104134 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:02.108150 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:02] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:16:02.115133 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:02.121139 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:02.124155 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:02.128172 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:02] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:02] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:02] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:02] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:02] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:16:02.428570 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:02] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:16:02.442884 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:02.445884 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:02.448882 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:02] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:02] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:02] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:16:06.035828 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:06.036829 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:06.038828 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:06] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:06] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:06] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:16:14.073807 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:14] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:16:14.424388 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:14.425393 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:14] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:14] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 12:16:35.140242 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 12:16:35.141225 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 12:16:35.141225 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 12:16:35.141225 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 12:16:35.141225 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 12:16:42.998440 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:43.000453 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:43.001453 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:43] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:43] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:43] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:16:43.045971 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:43.050981 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:16:43.055969 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:43] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:43] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:16:43] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:00.196769 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:00.500471 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:17:00.504506 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:17:00.506498 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:00.512486 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:17:00.532550 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:00.561535 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:17:00.564079 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:00.831467 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:00.846450 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:17:00.849455 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:17:00.853462 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:00] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:02.167709 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:17:02.170722 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:17:02.173709 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:02] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:02] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:02] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:04.176956 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:04] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:04.520401 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:17:04.524956 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:04] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:04] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:14.639676 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:14] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:17:14.973492 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:17:14.978481 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:14] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:17:14] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:18:13.778369 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:13] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:18:14.089515 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:18:14.092510 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:18:14.095512 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:18:14.099500 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:14] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:18:14.116513 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:14] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:14] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:14] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:18:14.148087 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:18:14.148087 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:14] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:14] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:18:14.430118 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:18:14.434125 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:18:14.437123 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:18:14.442124 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:14] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:14] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:14] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:14] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:18:16.416686 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:18:16.419680 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:18:16.422680 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:16] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:16] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:18:16] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:19:31.986703 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:19:31.990701 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:19:31.998687 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:19:31.999686 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:19:32.017707 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:19:32.037735 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:19:32.044702 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:19:32.331682 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:19:32.346689 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:19:32.354681 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:19:32.357668 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:19:32.367695 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:32] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:19:33.991193 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:33] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:19:34.302049 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:19:34.305046 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:34] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:19:34] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:12.786637 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:12.788652 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:12.791641 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:12] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:12] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:12] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:13.150507 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:13.154520 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:13.161508 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:13] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:13] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:13] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:25.397850 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:25] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:25.701579 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:25.705594 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:25] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:25] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:25.832764 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:25.836757 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:25.840780 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:25] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:25] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:25] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:31.797404 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:31.797404 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:31.798928 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:31.806959 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:31] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:31.819980 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:31.826943 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:31] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:31] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:31] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:31.843944 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:31] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:31] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:32.140996 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:32.142012 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:32.143006 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:32] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:32.158445 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:32] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:32] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:32.166930 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:32] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:32] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:33.292568 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:33] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:33.600881 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:22:33.602913 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:33] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:33] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:38.425968 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:38] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:38.698693 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:38] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:22:38.741805 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:22:38] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 12:24:22.914970 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 12:24:22.914970 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 12:24:22.914970 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 12:24:22.914970 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 12:24:22.915971 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 12:24:57.963460 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:24:57.964460 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:24:57.965461 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:24:57.971460 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:24:57.976578 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:24:57.983461 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:24:57] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:24:58] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:24:58] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:24:58] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:24:58] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:24:58] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:25:06.426851 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:25:06] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:25:08.538151 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:25:08] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:25:15.813522 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:25:15] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:25:28.492087 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:25:28] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:25:29.532362 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:25:29] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:40.070061 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:40.071062 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:40.073061 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:40.074580 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:40.074580 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:40.080579 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:40.100580 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:40.383567 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:40.391566 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:40.392566 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:40.394568 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:40.415861 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:40] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:41.055269 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.057269 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.058268 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.060273 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:41.064275 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.068286 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:41.117959 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.119465 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:41.128054 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.131054 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.137071 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:41.260201 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.264216 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.268217 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.273216 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:41.278388 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:41.286392 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:41] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:45.148747 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:45] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:26:45.459556 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:26:45.462820 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:45] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:26:45] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:27:27.750090 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:27:27.751090 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:27:27] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:27:27] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:22.472353 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:22] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:22.775001 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:22] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:26.207334 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.208357 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.208357 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.209334 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.209334 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/get_List_domaine_formation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/Get_Suggested_Fr_Cities/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/Get_Suggested_Word/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/get_all_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:26.469465 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:26.652179 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.655596 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.657609 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.658594 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.662647 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:26.787610 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:26.977325 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.979626 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.981626 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.984644 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:26.985626 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:26] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:27.098359 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:27.305249 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.306274 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.307252 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.308274 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.309267 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:27.423813 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:27.633978 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.635013 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.636994 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.639012 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:27.643989 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:27.740011 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:27.959450 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.960450 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.960450 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.961449 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:27.961449 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:27] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.741364 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.743367 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.743367 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.744364 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.745363 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/get_List_domaine_formation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/Get_Suggested_Fr_Cities/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/Get_Suggested_Word/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/get_all_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.880957 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.882958 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.883958 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.885958 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.887958 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.889958 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.890958 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.893463 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.896470 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.898468 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.899469 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.902469 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.905469 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.909468 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.912471 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.919469 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.921468 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.924469 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.926468 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.928468 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.931469 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.933472 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.936467 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.940469 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.942470 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.946475 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.948469 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.951469 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:29.952471 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:29.955468 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:29] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:46.917448 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:46] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:46.971034 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:46.973034 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:46.975035 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:46.977034 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:46] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:46.981032 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:46.986034 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:46] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:46] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:46] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:47] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:47.032130 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:47.034133 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:47.036131 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:47.039133 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:47] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:47] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:47.042132 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:47] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:47.046134 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:47] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:47] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:47] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:28:49.876435 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:28:49.877435 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:49] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:28:49] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:29:45.289699 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:29:45.291705 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:29:45] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:29:45] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:29:59.444321 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:29:59.446324 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:29:59] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:29:59] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:30:28.941573 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:28.945575 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:28.948574 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:28.950574 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:28.952573 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:28] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:28] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:30:28.961574 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:28] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:28] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:28] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:30:28.988572 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:28.990573 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:28.991574 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:28.994573 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:28.997575 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:28.999576 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:28] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:29] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:29] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:29] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:29] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:29] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:30:30.751461 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:30.752460 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:30] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:30] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:30:47.394670 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:47.395675 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:47.398163 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:47.403165 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:47] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:47] "POST /myclass/api/getRecodedParnterImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:47] "POST /myclass/api/Get_List_Theme_Catalog_Pub/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:30:56.376182 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:30:56.377181 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:56] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:30:56] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:33:41.955651 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:33:41.960161 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:33:41] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:33:41] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:36:24.627264 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:24.630260 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:24] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:24] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:36:32.637069 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:32.637069 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:32.642072 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:32.643068 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:36:32.648074 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:32.654073 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:36:32.682657 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:32.684655 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:32.685655 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:32.687658 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:32.689694 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:36:32.699655 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:32] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:36:33.909704 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:33.910729 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:33] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:33] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:36:59.648552 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:36:59.650553 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:59] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:36:59] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:37:06.390834 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:37:06] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:37:09.325912 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:37:09] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:38:44.222533 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:38:44.223529 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:38:44] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:38:44] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:38:48.938112 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:38:48] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:38:49.011112 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:38:49.012115 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:38:49] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:38:49] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:38:52.351566 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:38:52] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:38:55.242916 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:38:55] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:39:47.903379 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:39:47] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:39:47.934944 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:39:47] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:39:54.302559 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:39:54] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:39:55.061438 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:39:55] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:39:57.972870 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:39:57] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:39:58.058952 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:39:58.059951 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:39:58] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:39:58] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:40:45.387109 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:45] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:40:45.638963 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:45] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:40:45.796434 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:40:45.798464 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:40:45.800445 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:40:45.801444 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:40:45.806443 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:45] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:45] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:45] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:40:45.821986 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:45] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:45] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:40:45.995496 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:46] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:40:46.117602 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:46] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:40:46.127578 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:40:46.127578 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:40:46.128609 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:46] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:46] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:46] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:40:49.353954 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:40:49.357499 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:49] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:49] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:40:51.881539 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:51] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:40:52.249202 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:40:52.251296 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:52] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:40:52] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:41:43.415364 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:41:43] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:41:43.573165 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:41:43.576161 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:41:43] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:41:43] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:41:43.721105 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:41:43] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:41:45.566538 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:41:45] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:41:46.957691 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:41:46] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:41:49.830099 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:41:49] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:42:00.902910 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:42:00] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:42:45.506181 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:42:45.509696 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:42:45] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:42:45] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:42:45.733896 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:42:45.736896 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:42:45] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:42:45] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:25.420762 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:25] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:25.725433 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:25] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:25.827966 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:43:25.832966 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:25] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:25] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:41.687878 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:41] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:41.731944 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:41] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:50.279069 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:43:50.281086 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:43:50.286075 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:43:50.288085 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:43:50.292617 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:50.313080 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:50.339708 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:50.606757 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:43:50.623221 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:43:50.626775 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:50.631978 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:43:50.650575 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:43:50] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:44:01.475164 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:44:01] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:44:01.781765 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:44:01] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:44:12.671684 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:44:12] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:44:12.917813 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:44:12] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:44:12.996782 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:44:13] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:44:15.072096 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:44:15] "GET /myclass/api/Get_Stored_Downloaded_File/_e2G-Tx9g4FNLJyBhGdlRUL_HZGbD9pQIg/CorrigeTD01_20251017124341_20251017_124341_43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89.pdf HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:15.888969 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:45:15.889964 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:15] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:15] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:16.557644 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:45:16.561641 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:16] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:16] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:20.244754 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:20.550528 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:45:20.556525 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:20.567510 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:45:20.573511 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:45:20.585510 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:20.611549 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:45:20.615062 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:20.877801 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:20.894809 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:45:20.898809 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:45:20.905339 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:20] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:22.314542 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:45:22.317705 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:22] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:22] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:23.257625 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:23] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:45:23.602367 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:45:23.604366 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:23] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:45:23] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:27.410667 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:46:27.415714 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:27] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:27] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:27.750496 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:27] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:27.763496 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:27] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:32.903485 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:32] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:33.243273 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:46:33.250381 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:33] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:33] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:37.097239 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:37] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:37.434639 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:46:37.435638 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:37] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:37] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:39.832937 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:39] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:40.165844 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:46:40.171830 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:40] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:40] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:51.497042 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:51] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:46:51.834767 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:46:51.836767 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:51] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:46:51] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:47:45.841578 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:47:45.846576 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:47:45.850598 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:47:45.855613 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:47:45.858630 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:47:45.880196 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:47:45.903180 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:47:45.907176 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:47:45.912181 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:47:45.917195 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:47:45.927202 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:47:45.937178 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:45] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:47:49.456058 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:47:49.459078 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:47:49.462094 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:49] "POST /myclass/api/getRecodedParnterImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:49] "POST /myclass/api/Get_List_Theme_Catalog_Pub/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:47:49.497189 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:49] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:47:52.429990 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:47:52.431991 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:52] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:47:52] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:10.710359 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:10.711348 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:10] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:10.724389 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:10.728371 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:10.737934 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:10.747924 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:10] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:10] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:10] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:10] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:10.787959 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:10] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:11.040095 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:11.057092 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:11] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:11.063094 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:11] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:11.074084 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:11] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:11.081117 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:11] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:11] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:28.891402 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:28] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:28.936412 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:28.942399 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:28] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:28] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:52.445430 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:52.449942 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:52.452941 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:52.455945 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:52.459945 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:52.474945 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:52.488941 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:52.490940 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:52.496942 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:48:52.500942 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:52.508942 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:48:52.517940 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:48:52] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:00.452900 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:00] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:00.763372 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:00.766889 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:00.769903 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:00] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:00.785489 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:00] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:00] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:00.811490 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:00.814490 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:00.817490 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:00] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:00] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:00] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:01.093640 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:01.096623 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:01] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:01] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:01.109626 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:01] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:04.998439 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:05.003454 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:05.008454 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:05.016456 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:05] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:05] "POST /myclass/api/getRecodedParnterImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:05] "POST /myclass/api/Get_List_Theme_Catalog_Pub/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:09.916784 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:09] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:10.226969 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:10] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:11.762552 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:11] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:12.033203 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:12] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:12.093028 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:12] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:26.957944 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:26.960945 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:26] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:26] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:33.733581 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:33] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:34.046911 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:34.048342 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:34.050349 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:34.053353 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:34] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:34] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:34] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:34.080475 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:34] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:34.084493 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:34] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:34.096487 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:34] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:34.393506 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:34.396520 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:34.398519 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:34.404539 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:34] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:34] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:34] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:34] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:35.611544 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:35.613556 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:35] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:35] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:37.448951 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:37] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:37.793925 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:37.796927 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:37] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:37] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:55.190600 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:55.503976 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:55.505977 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:55.506976 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:55.512478 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:55.526514 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:55.538483 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:root:2025-10-17 12:49:55.545485 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:49:55.850588 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:55.855590 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:55.858589 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:49:55.862146 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:49:55] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:04.299610 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:04.303099 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:04] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:04] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:05.959799 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:05] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:06.304668 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:06.306659 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:06] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:06] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:13.797282 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:13] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:14.102515 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:14.106530 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:14.108530 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:14.114534 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:14] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:14.130525 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:14] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:14] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:14.133953 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:14] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:14] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:14.163293 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:14] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:14.430033 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:14] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:14.446045 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:14.451281 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:14.455296 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:14] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:14] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:14] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:19.351676 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:19.356675 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:19.357675 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:19.362676 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:19.368676 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:19.382681 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:19.397735 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:19.399735 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:19.404764 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:19.410940 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:19.417455 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:19.423451 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:19] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:20.683418 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:20.685383 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:20] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:20] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:22.113558 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:22] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:22.156554 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:22.159538 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:22] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:22] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 12:50:37.329350 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 12:50:37.332380 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:37] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 12:50:37] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 15:06:27.575557 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 15:06:27.575557 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 15:06:27.575557 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 15:06:27.575557 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 15:06:27.575557 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 15:06:33.681666 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 15:06:33.681666 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 15:06:33.681666 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 15:06:33.681666 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 15:06:33.681666 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 15:08:33.551388 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:33] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:08:34.355566 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:08:34.372085 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:08:34.381086 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:08:34.395083 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:08:34.410081 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:08:34.424083 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:08:34.426083 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:08:34.436083 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:08:34.453087 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:08:34.463603 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:08:34.477110 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:08:34.485135 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:34] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:08:37.111303 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:08:37.114284 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:37] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:37] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:08:40.060692 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:40] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:08:40.116303 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:08:40.120273 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:40] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:08:40] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:22:56.606069 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:22:56.610076 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:22:56] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:22:56] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:24:08.330918 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:24:08.334916 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:24:08] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:24:08] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:24:24.627446 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:24:24.632447 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:24:24] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:24:24] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:24:29.307368 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:24:29] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:24:29.366365 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:24:29.369363 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:24:29] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:24:29] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:25:44.345424 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:25:44.346423 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:25:44] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:25:44] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:25:47.707775 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:25:47] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:25:47.777119 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:25:47.780119 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:25:47] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:25:47] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:26:55.813440 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:26:55] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:26:55.873462 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:26:55] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:30:51.247304 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:30:51.254320 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:30:51] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:30:51] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:32:09.588914 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:32:09.593432 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:32:09] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:32:09] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:32:15.120490 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:32:15.124482 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:32:15] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:32:15] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 15:35:03.140150 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 15:35:03.141133 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 15:35:03.141133 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 15:35:03.141133 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 15:35:03.141133 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 15:35:08.753504 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:35:08] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:35:08.783503 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:35:08.786503 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:35:08] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:35:08] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:36:07.908800 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:36:07.912800 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:36:07] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:36:07] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:36:47.484292 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:36:47] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:36:47.561168 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:36:47.562166 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:36:47] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:36:47] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:39:15.437158 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:39:15.442158 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:39:15] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:39:15] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:39:45.976506 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:39:45.981500 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:39:45] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:39:45] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:41:02.669963 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:41:02] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:41:02.760516 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:41:02.761518 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:41:02] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:41:02] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:41:42.918357 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:41:42] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:41:42.959478 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:41:42.960483 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:41:42] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:41:42] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:43:04.240121 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:43:04] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:43:36.715020 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:43:36.719003 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:43:36] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:43:36] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:43:38.334468 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:43:38] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:43:50.246509 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:43:50] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:43:50.286522 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:43:50] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 15:44:50.246131 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 15:44:50.247118 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 15:44:50.247118 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 15:44:50.247118 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 15:44:50.247118 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 15:44:50.710065 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:44:50] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:44:56.487324 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:44:56] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:44:56.528842 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:44:56] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:14.702046 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:14] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:14.758305 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:14.760305 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:14] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:14] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:19.416673 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:19] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:26.767293 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:26] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:26.807787 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:26] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:46.368074 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:46.371072 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:46.374075 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:46.380109 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:46.384075 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:46.391076 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:46.426591 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:46.429592 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:46.432590 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:46.437593 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:46.438593 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:46.444592 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:46] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:48.375976 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:48.378978 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:48] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:48] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:50.317058 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:50] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:45:50.368579 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:45:50.371581 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:50] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:45:50] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:46:34.075866 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:46:34.076896 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:46:34] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:46:34] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:46:45.401345 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:46:47.181115 : Update_Message_To_Internal_Mail Aucun destinataire n'est précisé +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:46:47] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:48:42.749507 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:48:42.754501 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:42] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:42] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:48:51.009842 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:51] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:48:51.097385 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:48:51.100387 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:51] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:51] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:48:59.682195 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:48:59.684700 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:48:59.688706 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:48:59.695710 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:48:59.698707 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:48:59.702706 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:48:59.731707 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:48:59.734706 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:48:59.737707 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:48:59.739708 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:48:59.748709 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:48:59.753706 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:48:59] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:49:01.338422 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:49:01.341398 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:01] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:01] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:49:02.533544 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:02] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:49:02.594044 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:49:02.597044 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:02] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:02] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:49:20.015913 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:49:20.019913 : Update_Message_To_Internal_Mail Aucun destinataire n'est précisé +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:20] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:49:45.179402 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:49:45.184403 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:45] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:45] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:49:54.018536 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:49:54.024556 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:54] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:54] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:49:57.925007 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:57] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:49:57.998063 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:49:58.001032 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:58] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:49:58] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:50:04.978689 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:04] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:50:05.023302 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:05] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:50:22.900862 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:50:22.904864 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:50:22.907381 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:50:22.911381 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:50:22.911381 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:22] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:22] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:22] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:50:22.931386 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:22] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:50:22.953939 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:50:22.956945 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:50:22.960945 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:22] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:50:22.967947 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:50:22.972455 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:50:22.977456 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:22] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:22] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:22] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:22] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:22] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:23] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:50:24.521955 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:50:24.524952 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:24] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:50:24] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:51:01.151312 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:51:01.160334 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:51:01] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:51:01] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:51:55.923903 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:51:55.929892 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:51:55] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:51:55] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:52:03.685456 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:52:03] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:52:03.748516 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:52:03.749514 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:52:03] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:52:03] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:52:06.759910 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:52:06] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:52:26.833500 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:52:26] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:52:26.898015 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:52:26] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:14.536336 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:53:14.542378 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:14.556893 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:53:14.566893 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:53:14.571893 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:14.577892 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:14.592891 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:53:14.601892 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:14.613893 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:14.629890 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:53:14.643897 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:14.656420 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:14] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:16.396158 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:53:16.399160 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:16] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:16] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:24.134962 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:24] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:24.214529 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:53:24.216546 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:24] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:24] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:29.110948 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:29] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:53:29.141932 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:53:29.145457 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:29] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:53:29] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 15:59:31.957282 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 15:59:31.962281 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:59:31] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 15:59:31] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:00:04.600173 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:00:04.608160 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:04] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:04] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:00:20.627442 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:00:20.634148 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:20] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:20] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:00:37.544636 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:00:37.550638 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:00:37.560635 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:00:37.569641 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:00:37.578147 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:00:37.591152 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:00:37.591152 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:00:37.610159 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:00:37.623678 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:00:37.632191 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:00:37.641190 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:00:37.648191 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:00:37] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:01.069270 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:01:01.071270 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:01] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:01] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:05.058413 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:05] "POST /myclass/api/Delete_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:05.085395 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:01:05.088397 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:05] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:05] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:07.909205 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:07] "POST /myclass/api/Delete_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:07.935296 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:01:07.937384 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:07] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:07] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:11.757757 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:11] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:37.735663 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:37] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:37.776528 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:01:37.777555 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:37] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:37] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:41.668854 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:41] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:46.910739 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:46] "POST /myclass/api/Delete_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:46.965256 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:01:46.967306 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:46] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:46] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:50.678302 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:50] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:01:51.837064 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:01:51] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:30.179925 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:30] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:30.984190 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:31.290412 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:31.294009 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:31.298009 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:31.310022 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:31.336082 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:31.342097 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:31.345930 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:31.619064 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:31.648641 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:31.651641 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:31.653641 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:31] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:33.366404 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:33.370426 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:33] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:33] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:35.089592 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:35] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:35.442036 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:35.444020 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:35] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:35] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:38.170819 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:38] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:49.280507 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:49] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:49.547364 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:49] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:49.605642 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:49] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:56.504808 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:56.507807 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:56.511808 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:56.519810 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:56.528811 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:56.530809 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:56.555845 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:56.562819 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:56.567820 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:56.582958 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:56.592962 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:56.598964 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:56] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:02:58.178447 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:02:58.182443 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:58] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:02:58] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:03:00.716187 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:03:00] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:03:00.783197 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:03:00.786214 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:03:00] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:03:00] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:39.210105 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:39.213875 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:39] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:39] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:39.670554 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:39.674554 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:39] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:39] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:42.406622 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:42] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:45.805390 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:45] "POST /myclass/api/Delete_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:45.833017 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:45.834017 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:45] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:45] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:50.791057 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:50] "POST /myclass/api/Delete_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:50.843279 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:50.844279 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:50] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:50] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:57.361615 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:57] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:57.671119 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:57.676143 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:57.680146 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:57] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:57.687138 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:57.703138 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:57] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:57] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:57] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:57.719230 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:57.722272 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:57] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:57] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:58.001077 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:58] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:58.014063 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:58.017093 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:58.020089 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:58] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:58] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:58] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:04:58.786310 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:04:58.791323 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:58] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:04:58] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:05:02.512751 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:05:02] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:05:02.852746 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:05:02.855182 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:05:02] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:05:02] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:05:05.114305 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:05:05] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:05:10.783386 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:05:10] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:05:11.031435 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:05:11] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:05:11.105822 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:05:11] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:05:22.114252 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:05:22] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:05:29.742952 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:05:29] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:01.222470 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:01] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:04.924873 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:04] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:05.240984 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:06:05.245985 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:06:05.250983 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:05] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:05] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:05] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:22.526502 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:22] "POST /myclass/api/Delete_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:22.604017 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:22] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:28.452897 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:28] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:30.948579 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:30] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:31.025786 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:06:31.028802 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:31] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:31] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:33.743135 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:33] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:38.582077 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:38] "POST /myclass/api/Delete_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:38.619070 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:38] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:55.892234 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:55] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:55.964922 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:06:55.964922 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:55] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:55] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:06:59.032267 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:06:59] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:07:19.031373 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:07:19] "POST /myclass/api/Delete_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:07:19.098888 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:07:19] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:14:47.172617 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:14:47.177617 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:14:47] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:14:47] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:17:57.503958 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:17:57.507958 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:17:57] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:17:57] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:18:10.014804 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:18:10] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:18:12.422950 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:18:12] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:18:16.550869 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:18:16] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:18:18.981143 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:18:18] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:18:19.910682 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:18:19] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:18:21.461986 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:18:21] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:18:22.221088 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:18:22] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:18:23.075886 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:18:23] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:18:24.700596 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:18:24] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:19:57.477505 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:19:57.479533 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:19:57] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:19:57] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:21:02.091746 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:21:02] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:21:10.316915 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:21:10.321936 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:21:10] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:21:10] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:17.785345 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:22:17.787344 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:22:17.790345 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:17.797345 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:22:17.795344 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:22:17.805346 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:17.825347 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:22:17.827345 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:22:17.831373 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:17.839389 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:17.843352 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:17.849350 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:17] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:22.114436 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:22:22.115490 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:22] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:22] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:24.028773 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:24] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:25.495079 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:25] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:26.606303 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:26] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:28.685121 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:28] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:31.359938 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:31] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:32.494020 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:32] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:33.505593 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:33] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:34.285987 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:34] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:35.278077 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:35] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:36.620921 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:36] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:38.901632 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:38] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:40.678059 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:40] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:41.645393 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:41] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:22:58.654828 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:22:58.656849 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:58] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:22:58] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:23:43.529496 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:23:43.531498 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:23:43] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:23:43] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\internal_email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 16:25:04.296479 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 16:25:04.296479 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 16:25:04.296479 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 16:25:04.296479 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 16:25:04.296479 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 16:25:10.356857 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:10] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:10.395483 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:10.397481 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:10] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:10] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:12.406400 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:12] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:19.838648 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:19] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:19.880158 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:19.881271 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:19] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:19] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:38.629658 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:38.631614 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:38.635612 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:38] "POST /myclass/api/Get_List_Theme_Catalog_Pub/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:38] "POST /myclass/api/getRecodedParnterImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:38.666907 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:38] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:47.043097 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:47.044121 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:47.045120 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:47.048155 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:47.052152 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:47.078536 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:47.095535 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:47.385819 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:47.386827 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:47.400824 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:47.407839 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:25:47.412867 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:47] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:48.539793 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:48] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:25:48.857599 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:25:48] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:01.307444 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:01] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:01.579575 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:01] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:01.636041 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:01] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:04.418809 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:04] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:18.957898 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:18] "POST /myclass/api/Update_Message_To_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:19.225931 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:19] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:19.284418 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:19] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:32.882086 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:32] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:33.194448 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:26:33.197576 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:26:33.199576 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:26:33.205578 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:33] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:33] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:33.227562 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:33] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:33] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:33.230560 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:33] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:33.236532 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:33] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:33.538653 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:26:33.541654 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:26:33.545646 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:26:33.548648 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:33] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:33] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:33] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:33] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:26:34.642849 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:26:34.644848 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:34] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:26:34] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:27:07.967353 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:27:07] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:27:08.277586 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:27:08] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:27:24.217152 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:27:24.221152 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:27:24] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:27:24] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:27:45.895458 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:27:45] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:27:46.207775 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:27:46] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:27:49.944852 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:27:49] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:28:32.919736 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:28:32] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:28:33.232808 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:28:33] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:28:45.198355 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:28:45.201516 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:28:45] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:28:45] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:28:50.801745 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:28:50] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:28:53.830574 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:28:53] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:28:55.121184 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:28:55] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:28:55.503720 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:28:55.506468 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:28:55] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:28:55] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:31:05.186808 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:31:05.188766 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:31:05] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:31:05] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:31:32.877683 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:31:32] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:31:33.190762 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:31:33] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:31:49.641687 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:31:49.641687 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:31:49] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:31:49] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:32:01.192667 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:32:01.196666 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:32:01] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:32:01] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:32:02.316278 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:32:02] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:32:02.369935 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:32:02.372936 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:32:02] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:32:02] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:33:47.205635 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:33:47.208533 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:33:47] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:33:47] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:33:47.605792 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:33:47.609778 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:33:47] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:33:47] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:33:59.657814 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:33:59] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:33:59.994609 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:33:59.996608 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:34:00] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:34:00] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:34:01.832953 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:34:01] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:34:02.174269 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:34:02.175280 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:34:02] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:34:02] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:38:23.186929 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:38:23.188434 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:38:23] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:38:23] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:38:23.485212 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:38:23] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:38:23.502221 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:38:23] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:38:50.889654 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:38:50] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:38:51.196965 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:38:51] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:38:51.224182 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:38:51.229397 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:38:51] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:38:51] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:40:12.196043 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:40:12.200552 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:40:12] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:40:12] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:40:12.231566 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:40:12.234553 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:40:12] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:40:12] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:41:24.196073 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:41:24.198067 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:41:24] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:41:24] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:41:24.648387 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:41:24.652900 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:41:24] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:41:24] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:42:10.806777 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:42:10.811775 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:10] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:10] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:42:11.220353 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:42:11.221353 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:11] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:11] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:42:13.915689 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:13] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:42:14.242537 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:14] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:42:14.702498 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:42:14.707499 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:14] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:14] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:42:41.741575 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:42:41.744569 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:41] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:41] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:42:41.901620 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:41] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:42:42.205562 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:42] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:42:44.389901 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:44] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:42:44.439255 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:42:44.441269 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:44] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:42:44] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:43:18.222834 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:43:18.225849 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:18] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:18] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:43:18.647725 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:43:18.651726 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:18] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:18] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:43:46.938305 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:46] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:43:47.258186 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:47] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:43:47.533036 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:43:47.537037 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:47] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:47] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:43:49.845743 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:49] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:43:49.902177 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:43:49.904180 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:49] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:49] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:43:55.112642 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:55] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:43:56.118059 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:56] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:43:56.175911 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:43:56.179911 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:56] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:43:56] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:02.102230 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:02] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:02.149224 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:02.152225 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:02] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:02] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:04.598212 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:04] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:04.625195 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:04.628214 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:04] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:04] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:25.261429 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:25.264498 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:25] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:25] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:25.946115 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:25.951021 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:25] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:25] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:28.270337 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:28] "POST /myclass/api/Get_Given_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:28.326337 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:28.328336 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:28] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:28] "POST /myclass/api/Get_List_Internal_Mail_Sent_By_User/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:40.790965 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:40.792960 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:40.794962 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:40.800020 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:40.805021 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:40.819020 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:40.842018 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:40.845019 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:40.849018 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:40.854017 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:40.861018 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:40.872019 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:40] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:44:44.010714 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:44:44.013696 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:44] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:44:44] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:19.877604 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:19.881127 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:19.882120 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:19.884632 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:19.885639 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:19] "POST /myclass/api/get_List_domaine_formation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:19] "POST /myclass/api/Get_Suggested_Fr_Cities/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/Get_Suggested_Word/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/get_all_class/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.150688 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:20.157043 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.158054 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.164591 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:20.166604 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.172679 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.180335 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:20.182845 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.190377 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.195900 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.200412 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:20.203929 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.210452 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.220492 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.231562 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.238049 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:20.242627 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.246635 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.252681 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.257961 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.265005 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.269501 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.274023 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.278024 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.284556 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:20.289564 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:20.291083 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:20.301142 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:20.304666 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:20.307655 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/getRecodedClassImage_no_token/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSession_Cities_And_Distance_Formation_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:20] "POST /myclass/api/GetActiveSessionFormation_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:24.279824 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:24.334048 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:24.338511 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:24.342686 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:24.347642 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:24.350387 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:24.365974 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:24.376225 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:24.379224 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:24.384744 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:24.392788 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:24.399314 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:24.411720 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:24] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:32.025629 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:32.028625 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:32.032659 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:32.037306 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:32] "POST /myclass/api/Get_List_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:32.044826 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:32.046157 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:32] "POST /myclass/api/Get_List_Groupe_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:32] "POST /myclass/api/Get_Client_Type_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:32.059395 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:32.065335 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:32] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:32] "POST /myclass/api/Get_List_Paiement_Condition/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:32] "POST /myclass/api/Get_CRM_List_Opportunite_Etape/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:32.073871 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:55:32.079895 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:32] "POST /myclass/api/Get_List_base_document_automatic_setup/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:32] "POST /myclass/api/Get_Competence_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:32] "POST /myclass/api/Get_List_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:32] "POST /myclass/api/Get_List_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:55:33.623017 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:55:33] "POST /myclass/api/Get_List_Message_To_Mail_Queue/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:56:16.397612 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 16:56:16.401124 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:56:16] "POST /myclass/api/Get_List_User_Internal_Mail/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:56:16] "POST /myclass/api/Get_List_Internal_Destinataire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 16:56:20.555624 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 16:56:20] "POST /myclass/api/Create_Empty_Internal_Mail/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:30:21.433707 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:30:21.438363 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:30:21.440371 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:30:21.447399 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:30:21.452542 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:30:21.463589 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:30:21.475542 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:30:21.481643 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:30:21.485958 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:30:21.495004 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:30:21.502522 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:30:21.508712 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:21] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:30:22] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:31:25.955782 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:31:25.959482 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:31:25.962194 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:31:25.965734 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:31:25.972278 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:31:25.975728 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:31:25] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:31:25.985121 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:31:25] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:31:25] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:31:25] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:31:25] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:31:25] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:31:25] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:31:28.978969 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:31:28.984525 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:31:28] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:31:30] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:33:46.910823 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:33:46.913826 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:33:46.917337 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:33:46.922898 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:33:46.930531 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:33:46.933471 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:46] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:33:46.947140 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:46] "POST /myclass/api/Get_Given_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:33:46.957522 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:33:46.964398 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:46] "POST /myclass/api/Get_List_Paiement_Condition/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:33:46.977449 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:46] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:46] "POST /myclass/api/Get_List_Partner_Produit_Service/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:47] "POST /myclass/api/Get_List_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:47] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:47] "POST /myclass/api/Get_List_Partner_Order_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:47] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:48] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:33:52.649542 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:33:52.653547 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:52] "POST /myclass/api/Get_List_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:52] "POST /myclass/api/Get_List_Paiement_Condition/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:33:58.943884 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:33:58] "POST /myclass/api/Get_Given_Partner_Client_From_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:34:05.540516 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:34:05] "POST /myclass/api/Add_Partner_Quotation/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:34:05.667337 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:34:05] "POST /myclass/api/Get_List_Partner_Order_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:34:07.782415 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:34:07.786418 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:34:07] "POST /myclass/api/Get_Given_Partner_Order/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:34:07] "POST /myclass/api/Get_Given_Partner_Order_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:34:13.016041 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:34:13] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:34:31.255479 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:34:31] "POST /myclass/api/get_Class_From_Internal_Url/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:34:33.873541 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:34:33] "POST /myclass/api/get_Class_From_Internal_Url/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:16.664680 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:16.667187 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:16.671782 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:16.678986 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:16.710398 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:16.755756 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:16.761358 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:16.763413 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:16.773720 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:16.788729 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/Get_Given_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:16.805437 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:16.821538 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/Get_List_Paiement_Condition/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:16.841811 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:16.845813 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:16.872674 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/Get_List_Partner_Produit_Service/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/Get_List_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:16] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:17] "POST /myclass/api/Get_List_Partner_Order_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:17] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:19] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:21.032816 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:21.035824 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:21.039866 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:21.046278 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:21.054805 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:21.062563 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:21.070115 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:21.077154 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:21.083808 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:21.091460 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:21.093470 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:21.097470 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:21] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:22] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:25.207448 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:25.209965 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:25.214600 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:25.215607 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:25.223130 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:25] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:25] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:25] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:25.235451 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:25.240702 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:25] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:25] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:25] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:25] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:36:28.880056 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:36:28.883572 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:28] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:36:30] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:42:12.919931 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 17:42:12.923453 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:42:12] "POST /myclass/api/Get_List_Paiement_Condition/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:42:12] "POST /myclass/api/Get_List_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:42:17.622537 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:42:18] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 17:43:18.872536 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 17:43:18] "POST /myclass/api/get_Class_From_Internal_Url/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:23.390965 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:23] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:24.359512 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:24.366044 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:24.373040 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:24.384054 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:24.392059 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:24.427953 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:24.433467 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:24.442509 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:24.455547 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:24.466001 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:24.480002 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:24.503003 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:24] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:27.112623 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:27.120611 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:27.122616 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:27.135612 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:27.154618 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:27.163619 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:27.177123 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:27.190124 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:27.208127 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:27.221126 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:27.233138 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:27.252142 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:27] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:29] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:43.005532 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:43.020517 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:43] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:43.051509 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:43.064504 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:43] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:43.087562 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:43] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:43.111188 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:43] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:43.143198 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:43] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:43] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:43] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:57.815566 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:57.822595 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:57.825596 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:20:57.847120 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:57] "POST /myclass/api/Get_List_Evaluation_Final_Note_Classement_Detail_With_Filter HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:57.859113 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:57] "POST /myclass/api/Get_List_Evaluation_UE_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:57] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:root:2025-10-17 20:20:57.877126 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:57] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:57] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:20:58] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:14.813949 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:14.820963 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:14.828963 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:14.832040 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:14] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:14] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:14.853032 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:14.861999 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:14] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:14] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:14.875596 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:14] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:14] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:14] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:17.009357 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:17.011440 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:17.016363 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:17.026479 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:17.035383 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:17.043409 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:17] "POST /myclass/api/Get_List_Evaluation_Final_Note_Classement_Detail_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:17] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:17] "POST /myclass/api/Get_List_Evaluation_UE_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:17] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:17] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:17] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:34.173921 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:34.176391 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:34.181549 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:34.188546 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:34.200546 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:34.289747 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:34.292187 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:34.295752 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:34.299761 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:34.305068 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:34.310563 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:34.324581 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:34.328579 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:34.337580 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:34.344095 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:34.349099 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:34.360101 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:34] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:36] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:38.190828 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:38.194314 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:38.198429 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:38.201678 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:38.210587 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:38] "POST /myclass/api/Get_List_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:38.218580 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:38.227582 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:38] "POST /myclass/api/Get_Partner_All_Class_Few_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:38] "POST /myclass/api/Get_Partner_Session_Ftion_Reduice_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:38.236584 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:38] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:38] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:38] "POST /myclass/api/Get_List_Partner_Document_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:38] "POST /myclass/api/Get_List_Evaluation_Planification_No_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:38] "POST /myclass/api/Get_List_Unite_Enseignement_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:44.526274 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:44.532338 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:44.535341 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:44] "POST /myclass/api/Get_Given_Evaluation_Planification/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:44] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:44] "POST /myclass/api/Get_List_Participant_To_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:47.053898 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:47] "POST /myclass/api/Get_List_note_evaluation_Ressource_Affectation/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:48.094195 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:48] "POST /myclass/api/Get_List_Participant_To_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:52.362531 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:52.366791 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:21:52.369905 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:52] "POST /myclass/api/Get_Given_Evaluation_Planification/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:52] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:52] "POST /myclass/api/Get_List_Participant_To_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:21:54.164972 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:21:54] "POST /myclass/api/Get_List_Participant_To_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:22:10.564291 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:10] "POST /myclass/api/GetAllValideSessionPartner_List_filter_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:22:16.655353 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:22:16.658076 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:22:16.662585 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:22:16.666650 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:22:16.669334 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:16] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:22:16.676414 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:16] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:16] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:22:16.685480 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:16] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:16] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:16] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:16] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:22:19.075919 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:22:19.079428 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:22:19.082436 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:22:19.086529 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:22:19.091528 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:22:19.098834 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:19] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:19] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:19] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:19] "POST /myclass/api/Get_List_Evaluation_Final_Note_Classement_Detail_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:19] "POST /myclass/api/Get_List_Evaluation_UE_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:19] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:22:37.462004 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE

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\n\n\n \n\n \n\n \n\n \n\n \n \n\n \n
\n \n Photo
\n
part nom5_client   part 5
\n Né(e) le \n
 
\n\n\n

 

\n

Note par matičre / UE  

\n\n \n \n \n \n Credit\n \n Rang\n \n Moy. El\n \n Moy. Ens.\n \n Validé\n \n \n \n \n \n \n \n \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n \n \n\n \n\n \n\n \n \n \n \n \n
Matičre /\n UE\n Graphe
INITIATION A LA PROGRAMMATION5036.39\n 7.36\n \n Non \n \n
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.0\n 0.0\n \n Non \n \n
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.0\n 0.0\n \n Non \n \n
\n

 

\n

 

\n\n

Note générale  

\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n
RangMoy. ElValidéObservation
3\n 2.13 1Indulgeance du Jury
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Imprimé le : 17/10/2025  

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom5_client_part 5_404.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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0RhuKMU/Zqs5woAbCtKhprveBAHIQQQGjEdhNo44S15MLyMfCwfVuRhT0EydN0YcEVHPLYVJKLESJ5v9wzEYLueXZKvhVCEXveRMAeA/Px8bN68qU1BVgDgt99O4bffTjcKI9we9Ho9fvzxx1Z5tIvFYjz99LN47733MHLkyGaF+Z1apkwmw7Jly7Fly1b07t2n4TeJxAzLlz+NTz/9PwwdOqxTNd/W1gnRvR3ISSAQYOLESQgPH2ky7ZUrl3DixIk29aOioiJ8++23JpdGxGIxFi5cBDc39/taPjGB3laTBsehl42hx7yCCIVqZbufq+J12F6cgoomBu/nrJ0RZPNfr1pbkRmed/ZtZFrhAeytK8dNRXmXdpRJPTzxumsgHJvMxOtBWFt0E6cr8rrNSe5mXSUO1hSj6RRmtJkVJrn4QMgO2+g0wbNnzx5kZma0fXIoFGLjxg0oLi7uUBkKCgpw8OCPrcrv6aefxcqVK9u8vUwkEmHkyJF4//0P4OPjCx8fH3z++edYvXp1qxy/7mfs7e2xbNkyk+vDHCfA9u2RyM3NbXXf++mnn5CUlGgy7ahRYzB27Nj7vi2YQG8HjhJzND2NRglCja793q+xNSU4r6oxuD7LxbeRQxcHYJyzF3ybrJcrQDhQnNqlWrKQ4/CYey8ssHOHWWMFHtm8DmtzopFW1/UBOAjAn5X5KDTyrk+5+MNFYsE6aSeRkpKCPXu+b7emePNmEg4fPtyhEKDR0dFITU0xmW7WrNl49tlnTWrlzQscDqGhofjiiw2IjPwOERFTWhURjQEMGTIU06fPMNlPUlKSceTIEeh0OpPPzMrKwu7du0xq9NbWNli0aCHs7Ozu+3ZgAr0daHjeqJBpr26qIx67i1INDkSZa2GHUCOmY0exBZ519DTIf0d1IdK7OHqZrVCM1Z4DMNnC1qDzXNCq8K+s6yju4m10PBHO1hSj6fTJSyjBKAcpBGDaeWeg1WoRGbkNJSUd07A3bfoKOTk57fs2dDqcP3/epLm7Rw8nLF68uMN7jzmOQ79+/eDl5cWOVG0DVlZWmDdvvkmnL4FAgG3bvjVp8eF5Hvv27WuVZSgiIgJDhgxljcAEevuo1KjQ1JvVHByshe3bKpGsqMBxheG+2kecvI2e7iXkODzo4gP7JuvZubweJ0symvFT7zxczSzxludABIvMDETnobpybM6L7ZT1/ObIV9chpd7wCMiJFrZwkJizDtpJXL58Gfv37+vwc8rLy7B3795WaWVNKSsra5Uz3oIFCxEQEMga7S4SHByMJ56Yb1KjLikpxt69P7QYEjYxMQH79v1gMs8ePZywcOGidltlWp7cARUV5Q2n8rX3Lz8/v83+J+2Febm3VTsnHtdrDDUWO04At3Zs3eJB+LEkDalNBOAEiSVG9fBo9j5vSzu8aNsTa+/w8tYD2FKehWluAW0Kj9vmjg4g1NYZa2XBWJ59HcX035VzFYDPy7PhZW6LJ9x6dcladrlGiXwjE4Yhtq7d7mnf9rr7e2h9tbW1iIzc1qqDSYjIpDa7Y8d2TJ48GSEhIW0uhylzOxFhxIgRXb7Hl9EyYrEYjzzyCH7++RiysjJbtILs3r0LERERGDBggMHvarUae/bsQVFRocl+9cQTT6BPnz5d861yAqxY8Qr0el2HnuPr64fffjvTJZOObhPoZdp6FKg7LwCDABxczCzuujk1X1WLPYoSg+v9Le1bfVb2nWQpa7C/qgAcGpvspzvIWlwLFnMCTHf1x9rqgkbRJG7yOpwty8Z8Wd8uNu1wmOTkhdWqGrxSnNKo9BVE+KgwCd4Wdhjp0PlepyX1ChQZrJ8T3Mys7+0Rjwjf5NyAo7DzYuALOQGe9Q2DuJPPqb969QqOHv3J4Azxpnh5eWPgwIE4cGAfWtqDXVtbi507d6J3795tOme8oCAfdXUKWFg0P1kOCuqNgIAAJlHvAXx9fTFv3jy8++6/W0ynUNRix47vEBQUBAuLxuNcVFQUDh48aFKYe3v7YPbsR7t0371QKOzwRFEg6L6JZhcJdA4v5dyAGdd5Fd1LZIadfSbAWnj3NLA6vQ4bc2OR3WTGZgbgIXt3CNvxvqfLshHbZLvbEJEEU1x8TdeJTQ88Y9UDXynKGq5pAewsy0SEqx96iLt2RigRCLBQGoQMVQ2+qCls9FuiXot3c6KwwcwS/padG0zB6Bo9ocsOPelEiY73aks69Ym9OQGepsGdOxkvK8WGDRtMCnOe57F8+XIMGzYcly5dQn5+y+cPHD16BFOnTsXo0aNbXZbS0lKIRC1/84GBgd2i/TBaI7wEeOihh/Dzzz8jKup6i2mPHTuGGTNmYNSo//aHuro67N69GzU11Sb3tS9duhRyuZxVeiNFq4vI0muRrFN32l+9TtPla8MtoeJ12J2fgA3VBYZailCM0T082mw7KFTXYUd5lsF9D9r2hIeRuO1NsRCIMMvFz3CSoFHiXHlOt9SLg8gMKzxDEWFuWN5f1XX4JCcaFdrOjX1dayTMKzgOTmLmkdxReJ7HkSNHceWK6UAeffr0xcSJk+Dj49OqEKB1dQps2vQVampa77hZV6cEZ2Ki7OHh2Satn9G1uLm5Y9GiRRCLW7ZE1dersG3bNigU/z2O+dKlizh27IjJvhQWNgSTJ0d0W1S8+16g/69AADJU1ViXcRUri5JhTDStdPGDczuOiv29PBfntapG05QAgRCzXP1bPTkYaN8Ts80ar5frAPxYkoG6Dq79tBZPCxu86dEffY2Ent1eU4xv8+I71UnO+EE4BP09HoTj70BOTg42bfrKpJlQr9fjqaeegouLMziOw4wZM+DhYTqi1p9/XsCxY8da//21ok27y+GI0XrGjh2HkSNNB5v5/fffce7cWQBAVdWtMwBMOU9yHLB8+VPsKNv7UaDzAIo0KmTXK1r9l1VfizhFOc5X5OHTzGuYm3QGaytyUGvEQjDD3AaP9AyAoI3OX1VaNXaUZRg0xmRrZwRZt/54RjuRGR4zYp7fVV+Nq910aAsHYJi9G/4h7Qf3JtqUGsDHZRk4UprZaSfS2TSz7FKkVbEvugPodDrs378fhYUFJtOOGzce48aNx+11czc3NyxfvtykZkVE+Pbbb02a529jbW1lMkpYfn4etFota8B7CDs7Oyxa9KTJbYQajRqRkZEoLS3F6dOncfbsGZP9Z8qUhzBixAhWyUbosgXpXgIRLDrRw9lGKGqXh3AdgBezrqKtxth6AHVEBpHbGmnHIjO84z0Yju1Yu71YVYCjTbZeuXIcHnX1b7Nn+IgeHphQlIyTTQTajyXpGNFDDjHX9fM2DsBUFx8kq6rwUWkmFHfUWwnxeCcvDr4Wtgi1delwXs05lZVq6u/tr43jsKdnENwknXeQjYDjIOoks2NycjK2bv26VWkXL17SaM8xx3GYPDkC33+/2+TRqTdvJuHAgR/xwgsvmJwAODk5Q6fTtWi+TU6+CY1Gw9bR7zHCw8MxderD2Lv3+xbTXblyGXv2fI9ffvnFZH+QSCRYsmQpbGxsWAV3hUAnMrayTXjPMxQDjIRIbb8pgYNlO7wFedw66rOzCROZ4VOvQejbjndU6nU4VGoYMGGUhR0G2bm2+XkuEgtMdZDiZElaYy29rhxP1pQitB3PbA8WAiGelwcjW1WL7YpS3Kkzxes1eCv7Or70D4dnB7fUuZlbw47jUN1I4+eQV19zj39uHAY7yuBzDx65Wl9fj61bv4ZSaXpnyvTpMzB0qGEgjx49euDZZ5/Ds88+Y2Jew+Hbb7di8uTJ8Pf3bzGtVCqFlZUVeL75iXViYgJSUlIwaNAgNqLfS8JFJMKCBQtw8uSvqKgob1GGfPLJxyaXV4h4LFiwEH379u22dxCLxR32cpdIJN0WpKjDAp3jjOvNPcUW8DL/35xFPW5hj1Weoehv69KuTXQxNSXYcodneoNA5ATYXZDUrmdW67V/6cl3bh/j8WNJGvrZukDUTR3KXmSGVV4DkJb6B8422bb4s6oGn+VE49++Q2HdgaNebSSWcOMEqCZ9IwvB77UlWKjXwkbIHKTayqVLl3DkyE8m092KCDbPYKvR7bFg7NhxGDduPE6f/q3F55SXl+O777bj7bf/0eLZ1dbW1vD19W9xLzrHCXDu3DmEhoayvej3GL1798YTTzyBL75Yb1IxNIVM5oE5c+Z0WzheIsLbb/8Dw4cP75gyKhB2m9MmCyzTBgaIzPCUkzcecQtAD3H74oVriceh0nSjv0XWVSCyrqJTy7y9uhCLlFXw7UatMNDSHv/2GIDF6ReR2sQj/bOqAnjlJ+IZWT9I2mkqlptbw0tsgZtqxR02IeB4fS1K1UrYWLKYzm1BoVDgq6++bDFy153aef/+oS0K/KVLl+G33061qJVwHIcdO77DlClTjWr7t3F2dkb//v1NBpfZtWsnHnroIbYf/R5DKBRi9uxHceTIT8jKyurQs5544gn4+3df+xIRpFLZ3yoCIfNybyUPmtvgcNB4LPUIbrcwB26Fed1eXdRt5c7l9ThSkt5pDmmtZZi9G95072PUj2JdSSpOdWBbnaVQhCFGHAdreT1Ol2d3+7v+3fn1119x5cplk+lcXFzxxBPzWtSoAWDgwIGYOXOWyefxPI9vv/3G4AzyRhqHSISRI0eZPNylrKwU33yzFXV1HQ9mFR8fh+zsbNYxOgkPDw8sWbKsQ7sR+vULxrRp05kF5n7X0C0BvO3iB3+L1mlt5eo6PFV00+B6oroOefW1kFm0fxlBT4QjJekope7dZrOlIhdz3YPQ08yq2/IUchxmu/ohS1WNf5VnGUwy3smNgZu5Nbza4SDGAZjg6IGvKnJQcofw5gB8VZqBSc7eXRr69n+JwsJCbN68uRWnoXGYO/cxBAUFmXymhYUF5s+fj6NHfzLpfX78+M945JFZmDRpUosTBF9fvxbDiQLA3r170LNnTzz33PMmJx3NER0djVdfXQWxWIznn38BEydONBnYhmFCaxQIEBERgUOHDuL69WttF1KiW2edy2QyVpn3u4YuBjDWQYZHXP1a9bdI3g9rHQ1jqOcQj49ybqCyA0FS8uoV2FmVj+7eNZuk1+BUaVarwvLwnRi+x1IownMewVhiZahNX9fV44PsKOS3MzxwiK0zhjbZf08AonRqHCpOY3vSW8mBAweQlJTQKi1rzpw5rdaQgoODMW/eApNroxzHYePGDSgvb95pSiaT4eGHp5l0LOJ5Hhs3bsTGjRtRU9M2B0mdToerV6/i7bffQnLyTcTHx2HlyhVYv/5zlJSUsI7SQZydnbFs2fJ23Tt06FBMmDCBnX7HNPS2I+EEWCbrhzO1pTjTZBvYr2oFduTH4wWvgW12XCMQfi5JQ3qTMK/2nADP2Uth2UkhbXkiHKkpwhVt4y1cm8rSMa2nH2xELTuUEBE685w0J7EFVnsNRGrKeZy/o0x6AD8qKyHKiQLa4QZoLRTjmZ6BOJd1DdVNpiAflaShv7UTwnvIOz3yv5Z4XKjIx0A7V9iIJH/rvp6eno6dO78zbRHhOCxevKRNGpJEIsHjjz+OI0d+QllZaYtpY2KicfToUcyfP99o5C+O4/DYY3Px88/HkJaW2uKzNBo11q//DElJiVi+/CmEhIS06JDE8zyqqqqwb98+fPnlRlRW/teHpa5OgfXrP8fVq1fx+utr0K9fPxaZrAOEh4cjImJqqyLB/dfaY4nFixfDwcGBVSAT6O2jp7k1Vkv74M+sa40iwykBbCjLxHB7KQbZ92zTM0s19dhZnm0QaW6JjQve9A2DWScF8CcCfPMTsbAgvtG2sQsaFc6X52KKq1+L9ws5rtM7hb+VA972CMXTmVeRfoeTnB7AHmVVu587zFGGCSVp2K+sbHQ9n3isybmBrebWCLRy6DShXqfXYnNuHD4uTcc8O3e87TMYtn9Toa7T6bBz5w7k5+ebHFz9/PwxderUNmtIgYGBWLhwIT755OMW7yUibNr0FcaNG9dsbG53dymWLFmKN99cY3ItVq/X45dfjuPcuXOYOHEiJk16EL17B8HVtScEAgGICDU1NcjNzcWff17A/v37kZubY/S5PM/jwoU/MG/eE1iz5g1Mnz4dlpaWbJBsB7a2tliwYAHOnTuLujpFq+6ZNGkShg+/O0FkOI5DcXERQsOCJAAAIABJREFU0tPTO+2Zjo6OXTo5YQK9GcY4eeHVqkK8W9U42loqr8enuTHYaOUAhzbEDj9dloUYXVMvYg6PuPrDojOP/OSAsc5e8ClORnITa8Dm4hSMdfKEZYvburrGrDXaUYZVymq8XpSEmjtiF3TEMG4rkuBptyCcybiIiibxEC5qVXg+9QI+8QlDSDu3F95JmUaFT7Ki8FFVPgDCF1V50GcQ3vEJ+1tq6lFRUdi1a6dJIc1xHJ555lk4O7cvpsTMmY/g4MGDyMhoeVDMz8/DgQP78eKLLxoNO8txHKZMmYITJ34xGU3sNiqVEocPH8JPPx2GSCSCubk5HB0doVSqUF1dDb1e1wrfgVtUV1dhzZrVuHEjCq+/vgY9evRgg2Q7GDhwIB599FF8++03Jvuera0dnnxy8V2bQHEch7fffqvTrDJEwLp1n2LGjBldVmZmP2oGM4EQS2T9MExkZiAMvldVYV9BEvhWiqNqnQbfl2agrkn6+Zb27QpMYwpniSWecjDUdH6vr8W1qqK7Up9iToD57kF42l6KztqRyQEY7SjFuy4BBi3BA/hNU4elaX/idFk26tsZS17D3zKxP3vzLD6qymuYgqgBbKjKx/sZV6DQ/73CjtbV1WH79kjU15uOrDd06HA88MAD7V6/lMvlmD9/fqvu3759O5KSkpr93d7eHmvWvIGgoN5tHEgJWq0WtbW1yM7ORmlpCTQadauF+Z3PsbOzYxHpOjKumplhzpy5kEpNL988/vjj6Nev310tr16vh1ar7aQ/TZeXlwn0FvCytMNb0r6wbCLSOQCfl6Yjprp1zjIXK/NxSmPo/DXXxQ82os4POCDkOEzr6Q+XJjPLKgB7SlKh4fV3pT6thCI85xGCKZ14nKqIE2CerA9W2bkZ/f26To0HMi7jzZQ/EFdT2upDYlS8HimKCnyYcRnh6Rew30gUOi2AWr0WujbsWrgX3HouXryIQ4cOtkKA8XjmmWcahXhtDw899DD69DEd3au8vAzffvst1OrmHU979eqFd95ZC0fH7tWQiQhTpz6E55573mR8ckbLBAYG4rHHHmsxjUwmw5w5c9gOg7aOh6wKWma8szeWV+bh/2qK//txA0jkdfg8NxbrrUa3uI5ap9dhb3EalE22V0VIrDDEwb3Lyi23sMXTtm5Y22TJ4KiiDE8ryhHcCTHV21UuM2v8y3MgclP/wDVd5xyrai0UY4XXQBSmXsCuJuvpt1vs05oi7FKUYpalA8LtpRhg6wJzkQSWQjEshUKoeR4qvQ4KrQpRNaU4W5mHSGUl1H8Ja2O2mNfspVjjPRj2RhwNtc0I+cNlWXCuLu6SunU2s8TEHh4tpqmsrMTWrVtb5a0+bdoMDBkypMPlcnFxwZIlS7Fq1QqTWvGxY0cxffqMZk/q4jgOYWFh+PjjT/DWW2+26iCZjsLzPGbOfARvv/02c87qDIVDKMSMGTNx/PhxJCYmGKlvPZYsWQovL29WWUygd7KJSCDEsx79cT7pDK7rG5tMtisrMKrwJhbK+jV7oEpibSl+UFU1ES/AbCdv9BB3nelOzAkw1cXXQKDnEuHHolT064R15fbS16YH3pKH4Jns6yjsJGuBm5kV1vmHQ559Ax9WGT/Jq5jXY6OiDBsVZbAVCOEjEMFdIIQ9J0Qd8SgjPa7rNKhvhcb9vpMPnvfo36yFxeiBOMTjleKULqvX52x7tijQeZ7H8ePH8ccf502uCwoEHBYsWGg0xGt7mDRpEvbsGYzLl1s+Z12pVCIychsGDBjQrCYsEAgwfvx4ODk54R//eBsxMdFd9x2JxViwYCFeeOFFODo6sgGxsyb2cjkWLVqEN95YY3BcamjoQEyd+hALItMOmMm9Ffha2uNVtyDYGjG9v1ucglSF8T20WuKxvzgNyiYCYoTIDOOcPLq83H1snLHU0nAQ+qGmECmKirvY6ThEOHniTRd/dKa7i6uZJV73GYwNroFw4wQtTlhqeD2idWr8rFFit7oWhzV1uKCtNynMewnF2O8xAC97hnbJcklHMDVBKygowMaNG0wKcyLC44/PR0hISKeVzdraGs8993yrYlqfOnUSp06dNDHhEKB///744osvMGPGzC4xzbq5ueOTTz7FqlWvMmHe2X2V4zBx4iQMHhxm0PeWLVsGV1dXVklMoHfdQPlwTz8ssnGBsImmncnr8Un2Daj0OoP7UhQV2FFraF59xEHeLZHMLIUizHU1PM0qidfjRFnmXa1TMSfAPPcgLLbr3GUHO5EET3uEYL/fCCy0doJrJwWjkHECvGjnhoO9xmBmT39YCP9exi29Xo+DB39Ebq7pkLv29g54/PHHOv0QjGHDhv11hjpMTii2bt2K4uJik0LBy8sb7733PjZt2oIRI8I75RAMe3sHLFiwELt27cb06dPZmnkX4ejoiMWLl9zhZEiYNOlBjB49hlUOE+jNDA6d9BwLgQjPe4Sgt5EtX7uUFThUlNIoxhoPwvHSTAOTcm+BCBEu3bc2NNDBDbPMbQ2u76/IRV694q62jZ1IglWe/THF3KZT20zIcRju4I5NvcZgl/cQPGZhD6927vPvLRDhFTs3HPAPx8cB4ehl5Qjub9jX09LSsGXLllvHHbfwx/N6LFiwsM2e5K3BzMwMy5cvh0QiMVmOqKhrOHLkp1adwmVtbY0JEyZg06bN+Oyz9Rg9egxsbe1aHTuciCAUChEY2AvPP/8Cdu/ejbff/gf8/Py6NDrZrfrmm/2jLoh2SIQW8+xIvPX2EB4ejgkTJt5SQCytsGDBQtjZdd/hSqbqorP/upoOqxm2IjMscZA3GkwIBEdx92/tcBSbY4W91EgZO2efsK+VPd6Th+BsVaHBbzGqakzUqhvWxau1GlQQb1AeuZk1/Cy7z7HGXmSGBa4BkFcXNhJEBCBNWQWZuXXjwVEkwQJHeaMDTngQLLtor7WnuQ0+8h6CvnesLXMAHDqh/5gJhBjv5IlwRxmSFRW4VF2IazUlSNHUIUWvRSHPAw2BeDmAE6CPQIheQglk5jYYZ++OXjbO8LO0g6CNA7uVxByrHeXoTr92XwvbZgVHcXExli9/yrTlRCzCjBkzu2z9sn//UHzwwUcoKjK9fVIgEKKurg7W1tamrWgcBzs7Ozz00EOYOHEi0tLSEBcXhytXrqCgIB95eXmoqalGfX09hEIhrKys4eTkDC8vT3h7e2PkyFHw8/ODs7Nzt4QYFYlEeOONNw3Wjxu/vwBOTk6dluft6H3Tpk1r2Rolk3VbRDwrKyssXrwEp0//hhkzZiIsLKzbvpeIiAgMGzasW2VUr169uvT5HBELes24f9AToUSrQq1WDZ1OC9xeM+cE4AQCmIskcJJYsDPV/4dQKBSorKyERqOBXq8Hx3EQiUSwtLSEk5MTc766y+h0Oqxfvx4TJ05A3779WIUwgc5gMBiMvyu1tbWwsrJisfKZQGcwGAwGg8GmQwwGg8FgMIHOYDAYDAaDCXQGg8FgMBhMoDMYDAaDwWACncFgMBgMJtAZDAaDwWAwgc5gMBgMBoMJdAaDwWAwGEygMxgMBoPBBDqDwWAwGAwm0BkMBoPBYDCBzmAwGAwGwxARqwLG/QARQaVSged5mJmZQSxmx6MyGIz2odfrUV9fDwAwNze/Z47gZRo6475AoVDgrbfewpNPLsKNGzdYhTAYjHbB8zyOH/8ZTz/9FNat+6RBsDOBzmB0EzU1Nfj552O4cSMK7u5urELu0DTy8/PATlFmMFpHQkIC/vnPf0Kr1WHRoidhZWXFBDqD0Z2kp6ehvLwUwcEhsLOzZxUCIDn5Jj788ANs3rwZPM+zCmEwTFBYWIj3338PcrkcH3zwATw8PO6p8jGBzrgvyM7OgZmZOQYOHAQLCwtWIQAOHTqEr776EsHBwRAI2FDAYLREfX09Nm3ahIqKCnz00Ufw9va+58rInOIY//Oo1WrEx8eBiODv7w+RqPXdnogamaNNCb4707dXSLYlzzvTcRzX6F6O48BxnNH0CoUCN2/ehFgshlQqa/Tb7XuM/Z+IjD73Tg3f2O/tLa+xe1tTnq56bnO09f1bWz9tKWNb3rm179KZfa8tfd1UPbVU562pY2PlNHWfXq/HrFmzsHjxYsjl8ja9W3dNmJlAZ9wHAr0eUVFRUKvr4ePj0+pBLTs7G9euXcOVK5dRUVEBBwdHjB49GsOGDYOTk5NB+sTEBJw+fQapqSkQiyUYP34cRo8eA2tr61blqdfrkZ6ejtjY2L/yrISjowPGjBmLsLAwgzwBYNu2bSgpKcaMGTNRX3/rPePj4/6yRgzE2LFj4ejo2JD+woULOH/+HOrqlLh27Rp4nsePPx7AmTOnAQDTpk1Dnz59UVZWhm+//QZCoRDLly9HYmISzpw5jczMTDz77HMICQkBEaGkpARxcXE4f/48iouLYW5uhpCQEISHj4S/v7/B4BgVdR3nzp3HgAGh8PHxxcmTJxEdHQ2VSgl/f3+MHj0GgwcPNvAa1ul02LRpE+rqFJg9+1FoNBqcPXsGsbGxePDBB/Hww9MAAEqlEjdu3MCFCxeQnp4OgYBD7959MGrUKPTp08dgMpebm4vvvtsOHx9fPPDAA7h27RpiYmKQn58HPz8/hIeHIySkv9FJIM/zKCwsxLVr1xAVdR25ubmwsrLGyJEjER4eDjc3t0bvX1paiu+++w48r8eyZcthb2+49BMfH4ejR4/C3d0dCxYsBMdxqK6uxtdfbwERYdmyZUhPT8eZM2eQlpaGxYsXY/DgMOTkZOP48V8QHx8HgEN4eDjGjx8PZ2fnVk8i8/Pzcf36NVy9eg1FRUWwtrbCyJGjEBYWBplMZjDBOH78Z0RHR2PKlCmwsbHFpUuXEB8fB41Gg5CQ/hgzZozBfXdOsuPi4nD16lUkJyejqqoK7u5ueOCBCRg8eDB++ukwSkpKMXXqVPj7+zfKNy8vr6GcxcVFsLa2xogR4Rg9ejREIhG+++47mJubY8GCBbC0tGy4T6FQIDo6GteuXUVmZiYUCgV8fHwxadIk9OnTG9u2RYKIx9y5jzX61nQ6HZKTkxEVFYUbN26gsrIC7u5SjB49GuHh4Q153Eaj0SAq6jr++OMPJCenQCp1x/jxD2DYsGFtUibaqw0wGP/TpKSkkFTqRoMHD6KSkhKT6Wtra2nz5s00YEAoeXjIKCDAn0JDQ8jDQ0YeHjKaNGkixcREN6TneZ6OHz9OgYEB5OPjTWFhgykgwI+8vDzotddeo6qqKpN5VldX0+bNm6hfv74kk7lTYGAAhYQEk6ennDw95TR16lSKj49vdE9lZSVNnTqFAgL86MknF1FAgD/5+nqTn58vSaVu5OEho2nTHqasrKyGe/bu3Uu+vt7k5eVJcrmUPD3l5OfnQ35+PhQWNohSUlKIiOj69etkZ2dBU6dG0CuvvEy+vt7k5uZKvXsHUWpqCmm1Wjp16hRNmjSRPDxk5O3tSaGh/cnHx5tkMnfq3TuIjhz5ifR6fUPeOp2O1q1bRzKZO82YMZ3CwgaRXC6lwMAAksulJJO5k5+fL3300UekUCgavWtWVhYNHTqEQkP707/+9U8KCupF7u6u5OhoS6dOnSSe5ykhIYFmz55F3t5e5OXlQSEhwdSrVwDJZO4UEOBPn376KdXX1zd67qFDh8jV1Ynmzp1LU6ZEkIeHjAID/cnDQ0YymTv5+/vS5s2bSaPRNLpPpVLRd99tp8GDB5JcLiUfHy8KDu5L3t636nX06FF06dIl4nm+4Z7Y2Fjy9/elmTNnGDzvNl999SXJZO60cePGhnsTExPJxkZCEydOoFWrVpKfnw+5ublSYKA/xcXF0qVLl2jQoIHk6Smn/v1DqG/fPuThIaO5c+dQbm6uyb6nUqlo//79NHJkOMnlUvL19aaQkGDy8vIguVxKI0YMp7Nnz9Adr0I8z9Py5cvI01NOS5YspuDgfuTl5UEBAX4kk7mTXC6l4cOH0dWrVxvlxfM8ZWdn0TPPPEN+fj4klbqRp6ec/P1v9VkvLw+aO3cOjRgxnAIDAyg7O7tROX/4YS+Fh48guVxKHh5SCgrqRV5eHiSTudPYsWNo7dq15OrqTMuXL6O6ujoiItLr9XT9+jWaNu1h8vLyJKnUjXx8vBry9/f3pWXLlpK3txfNmDGdysvLG/IsLS2ld9/9N/Xu3euv79KfgoP7NXw7Tz75ZKPvS6vV0ubNm8nPz4f8/f0oLGwweXl5kI+PF61bt460Wm2XjnVMoDP+5zlx4gS5u/ekpUuXGAiKptTV1dHatWtJLpdSRMRk+u677yguLo6Sk5PpwoULtHz5cpJK3WjatGlUXV1NREQVFRU0YEAo9evXh44fP07p6el05cqVv4TWYDp79qxJYf7GG2+QTOZO06Y9TPv376f4+Hi6efMm/f777zR//nySy6U0f/78hjyJiJKSkqhv3z4kl0tp/PhxFBkZSbGxMRQbG0s//LCXhg8fTnK5lNaseZ10Oh0RERUUFFBcXBz961//JKnUjd566y2Kjo6m2NhYSkhIaBB427dHkru7K8lk7jRp0gT6+ustdOnSJfr999+pqqqKDh48SAEB/jRo0EBat24d3bgRRSkpKXTjxg368MMPycvLgwIDAygmJqahvBqNhubMeZTkcin5+/vRq6+uovPnz1N8fBxFRUXRhx9+QIGBAeTu3pP27t3bqI4uX75M3t5eJJdLaejQIfTRRx/RH3/8QefPn6eysjKKioqioUOHkJ+fD61cuZIuXvyTbt68SfHx8bR161YaNGggeXl50KFDBxsJ2c8//4xkMnfy8JDRiy++QOfOnaP4+Di6evUqrVmzhmQyd/L0lNOVK5cb7qmvr6fPP/+M5HIpjRo1krZs2ULR0dGUlJRE165doxUrVpCnpwc98MD4RgJ1z5495O7ek955551GE507hd0rr7xM3t6edP78uYbr+/fvJze3W20xbtxY+vLLL+nixYv0+++/U35+Ps2dO4fkcint2rWTkpOTKTExkZYvX0aBgf4UGRnZYt+rr6+nDRs2kIeHjEaODKetW7dSTEw03bx5k6KiomjNmtcb8s3JyWm4r6qqiiZOnEByuZQGDhxAX3zxBV2/fp3i4uLoxIkTNHPmDJLLpTRz5kyqrKxsuC8jI4MiIiaTVOpGDz44iXbu3EFXr16luLhYOnfuHL344ovk4SEjuVxKY8eOaeiPKpWKPv10HXl4yCgoKJA++OB9unTpEiUkJFBUVBT93/99SgMG9G+YGH711ZcN7Xzp0kUKDu5HMpk7zZ8/nw4ePEg3btyg2NhYOnr0KD3++OMkl0tJLpfS6tWvNdxXUlJCS5YsJrlcSnPmPEpHjhyhuLg4unnzJp09e5Zmz55FMpk7rVy5oqGcSUlJJJO508SJE+j8+XOUnp5OJ0/+SoMHD6LRo0dRXFwcE+gMRnvR6/W0adMmkkrd6LPPPjOZft++fSSVutHcuXMazbxvU15eTk888QTJ5TLat+8H4nmeoqOjycNDRkuWLG40YUhMTKSUlBSjg/edg/jWrV+TXC6lBQsWNNJI7tQSpk6dQq6uznT48OGG67/+emuiMnPmDKMDxZEjP5G/vy8NGjSQioqK7tCUtfTPf/6DpFI3OnbsmMF9Op2O1q5dSzKZOz399NONBvJbmmYM9enTm8LCBtOZM6cbCcjbgvv9998jqdSNVq9e3TDYFRcXN2iQBw4cIJVKZZDv999/Tx4eMho/fhwVFBQ0/BYZuY1kMnd68MFJFB0d3ahOi4qKaPr0aeTj4027d+8itVpt8E6HDx8iLy8PmjDhgQaLiUKhoMWLnyR3d1fauHGjwWSvtraWli9fRjKZO23Zspl4niee5+nEiRPk4+NNERGTKS4u1iAvhUJBS5cuJXf3nrR58+aG6++88w7J5VI6evSo0b5QUlJCERGTqU+foAZLiV6vp//7v09JJnOnJUsWU0ZGRqP6rq6uosBAf+rXr2+jNs7JyaHo6OhmLQG3+95vv51qsDrduHHD6Lu89tqrJJW60fr16xuux8fHU9++fWjo0CF04cIFgz4QFxdHAwcOoMBAf4qKiiIiIqVSSS+//BLJZO60evVrlJeXZ5CfWq2mf/97LUmlbvTmm2+QVqslnufp119/JblcSmFhg+nkyZMGbazX6+nChQvUr18fcnbuQadOnSIiosLCwgYr0rp164xayyoqKmju3Lnk7t6Tdu3a1fC8zz//jKRSN3r55ZeorKzM4L60tDQaM2Y0+fh40aVLF4mI6JdffiGp1I0++OD9RmmjoqIoOzu7xbGgM2CurYz/aTQaDa5duwatVgMfn5a9UsvLyxEZuQ1isRivvLICnp6eBmkcHR0xZsxo6PU6XL58BTzPw83NDebm5rh+/ToSExMbnHWCgoLg7+/fokNMSUkJvvnmGzg5OWPVqlVGt8E4OTnh0UfnQCKRICYmuuF6TEwMBAIBHn/8CfTt29fgvgEDBkIu94BSqURZWWnD9ZqaWsTExAAgo/nV1NTg2rWr4Hkezz//fCMHII1Gg71796KiohzPP/88xowZa7BGKhaLMXPmI6isLMTJkyegVqsBAJmZGaipqUZYWBgiIiJgbm7e6D6hUIjJkyfDy8sbMTE3EBsb2/BbQkICRCIRXnzxJYSEhDSq099+O4XLly/h0UcfxSOPzIJEIjF4p2HDhsPfPxD5+fnIyclpWG+/dOkSevRwwpw5cwz2E1tbW2P06NHgeR75+QXQ6XRQq9XYuvVrCAQCrFy5En379jPIy8rKCrNnz4JIJEJ8fDx0Oh1qamqQnJwMsVjSbByEysoKZGVlQSaTQyaTNZTx4sWL0Om0ePHFl+Dt7d2ovgUCIfz8/FFSUoQLFy5Ar9cDAORyOUJCQlqMiKjRaPDNN9+A4zi88sor6N+/v9F3mThx4l/r+/FQKusAAEVFRaiursIDD9xaG27aB273faVS2RB4JS4uDt9/vwseHp545plnIZVKDfKTSCSwt3cAAISGhkIkEkGpVGLjxg3geR4vvPAixo0bZ9DGAoEAffv2hYODI4RCAXr16gUA+OOP35GUlIixY8dh2bJlsLOzM8jT1tYWYrHoL3+L3gCArKwsREZGwsnJGa+88gp69OhhcJ+vry9GjBgBrVaLxMREEBEcHBzA8zx+//13pKenN6QNDQ2Fh4dHlzvHMac4xv809fX1iI6+AUtLKwQG9moxbVpaGi5e/AO2tvbYt28fjh49YiQVh5SUZAgEAiQl3RLePXr0wBtvvIk1a1bj2WefwcqVKxERMQW2trYmy5eSkoKiokJYWVljx47vDITcbW4PDpWVldDptOB5QlpaOiQSCWQyqdF7BAIOAoEAYrEYFhaWjQR2XFwsvL19jXrr1tTUIDY2Br169WoQLLdRKGpx7NgxCAQCnDt3HqmpaQAMg9IoFHXo0UOK/Pw8qFQq2NraIj+/ABqNBoGBgc2+p6WlJYYPH4HU1BQUFRUBAKqqqpCamgorKyuDSZZWq8Xly5chEomQkZGJd9/9d7NOjqWlJairq0NtbS0AIDs7G0VFBZg5cxYsLMybqUMhAIKdnR2EQiGSk5ORmJgIgHDo0CGcPXvW6H35+fkgArKzs1BdXYW6OiVSUpLh4OBgdKJ4u40rK8sxZcqUBoGlUCgQExONwMAgowLQysoKzz33PJYvX4o1a15HYWEh5syZY9SBsinZ2dmIiooCxwlw7NjPuHDhgtF0hYVF4DgOCQnxqKtTwtLSCteuXf1r0jjAqNPb7X4nkUgaHMESExMgEokxe/ajzXqJ19fXIzk5GRzHoWdPt7/uS0RsbCyGDBmKyZMnNysUq6urkZSUgCFDhjU4qp05cwYcx2HWrNmwsbExel9tbS3S0tIgk3k0OCpevXoVhYX5cHR0wqZNmyESCY2OBdeuXWuYrBARgoODsXjxEkRGbsOzzz6Dl156GePHj4eZmRnzcmcwOkpWVhZKSorRu3cfODg4tJg2MzMTZmYW4HkeBw7sazGtWCyBg4MjOI6DUCjEo4/OgUgkxtq1/8Krr67CyZMn8eqrryEwMLDFrTfZ2dl/aXDV2L+/5TxFIjGEQhE4ToCysmKkpqbA2toGMpnxwbGmphbFxUVwcXGFi4tLo0lETU01pk59yOhAk5KSAqWyDqGhAwwEb1lZOYqKCiAWS3D69KlWlFcIiUQCnudx+fIl8DyPgQMHmahbMQBqGETLysqQnZ0NV9eeBoKgqqqqYbJz5colXL16uYUnc5BIxBAIuIb2Njc3R0hI/0YTnjs9qktKigFwkEqlEAgEqK6uhkJRC57nm5nw3fn+IlhaWkIkEiMvLxd5eTl48MEIWFsbFyxJSTchFIowaNDgBqGVnp6G6upqTJr0oIE3NXBri9WECROwefPXWLv2Hbz//rs4ffo3vPzyywgPH9li3ysqKkJ9veqOdyET/d0BQqEQGo0GyckpsLCwaHZyUlFRgaKiYtjZ2cPBwQFarRZZWdkg4iGTyZoVyrW1tUhMTICFhQV8fX0bnqXTaREQEGBUU75NamoKRCIxBg0aBFtbW9TV1aGwsBDm5uaQSt2bvS8nJwdVVZUYNGgw3NxuTSIKCvIhEolRV6fAnj27Tfbz2+UyNzfHypWrYG/vgA0b1uOpp5ZizpzHsGLFSri7uzOBzmB0hIKCAhAR+vbta3SbUFNtj+M4PPLIbKxevdqo6bbpYHp7e5WFhQUee+wx9O/fH5s3b8Lhw4eRkZGBr7/+Gn5+/kbvJyKo1bfMkXPnPo7XX3/d5PsIhUIIhUKUl5cjIyMdAwcOanZrUmZmJioqyjF27LhGwXSysrIgFkswYMAAo5pydnY2RCIxgoODDQS+Wq0Gx3Hw8/PH119/DUfHHibLbGdnB51Oh5iYGNjbO7Q4sOl0OkRH34BOp4OX160lkry8PJSWFhvdIqTT6aBQKCASibB79x7RMj2ZAAAgAElEQVQEBgaaLI+lpeVfZtIE6PV6eHp6GhV8t7bARUOjUTcsTRAReJ7HkCFD8dVXm0weysFxHGxsbJCamgqOEyAkpL/Re9RqNdLT0yEUiuDhIW8oT25uLgQCAYKCgpoNiCQSiTB58mQEBwdj584d+Prrr/HKK69g/fr1GD58RLNl43k9iAijRo3BJ5980qr+bmNjg8LCQqSnp8HOzh5ubsbbsqSkBEVFhfD29oZUKgXHcTAzk0AgEECpVDabR0VFBWJibmDSpMkN5vHb6VuqayJCTk4OOI6DTCaDUCiEWCyGWCyGVquFRqNp9t68vDzU1dXB09Ozob/rdDoAwGuvrcbcuY+Z3FMvFAobJikODg54+eWXMXJkOD799FP88MNeZGVlYdOmza2ynDCBzmAYQa/XIyYmBjyvh6+vr8kT1pycekCn0+LmzSRYWlo2a6IzZs4VCAQQCATo06cPPvjgQ7i5uePLLzdg586d+Mc//mlUI+E4Dl5et9ZECwryYWVlZXJQvU1iYiLU6noMGDCw2YEuMTEBHCdopPHV19cjISEBOp3WqHalUqkQExMNrVYDDw/D393d3WFuboEbN66CCCatHv8dNHNRUlICb29v9OzZs9lB+fz584iLi8WQIcMQEBAAAEhIiAeARu9x52TB398fmZkZqKysbHV5FAoFrl+/DqFQ2OwkQKVSITo6Cu7u0ob4BRYWFjA3t0BVVRV0Ol2rB+jy8goIhUK4uroaba+KinLEx8fBwsK8YQKo0Whw48attvD29mm2790OjiKTyfDqq6/By8sbb7zxOj7//HMMHhzWbL93dHSERCJBamoKVCoVXF1dW/ku5cjPz0NY2JBmJ5O3td6goAiYmZlBIBDAyckZt5asUqBWq41ah65fvw6JRIJRo0Y1TDatrKz++kYKoFQqjVoqNBoNrl+/jvp6JYKCggDcWo93de0JrVaLmJhYhIUNMRDMer0eFy/+CZ7nERIS0nDd1tYORDxSU9NgZWXVapO5Xq+HUCiESCRCWNgQfPnlV1izZg1OnDiOkyd/xWOPPd6lYx5zimP8Twv0qKjr0Ot5BAX1Npk+NHQA/P0DER8fh2PHjkGr1RrVAhQKRcP/4+Li8OWXG1FSUtKQzsrKChERERCLxUhISGjx4JOAgAC4urri/PlzOHHiF4OY6jzPIyMjw+BEp4yMW9pcSEiIUQGh0WiQnp4OkUgEX1+fhoFMrVYjLS0VSmV1wxplU00xKioKjo494OfnZ/C7lZUVpk+fCWtrO2zfHom6ujqjA35hYWET824xqqqqoFarUVtba1AnPM8jOTkZ//nPR9Dr9Zg79zE4OztDp9MhLS0NIpEYvr6+BgOypaUlBg0aDL1ej02bvmpYd2/6Tv/f3nlHx1Vdi/u700fSqGtmNCpWteUqG4MLmGLAlICBAAmmOLz3CxB4TkKABMgjgVASQhJqEpKQAAYC5NEMxhhsggFj44JxxUWWbFnF6l1TNeX8/pA8nhmNpJFkmZLzrWXW4mpuOefuu/c5++y9T2VlZdg929ra+OKLnUyePDWs8E4ohw5VcvhwLdOnzwgakaKiIoqKiigr28fy5cuDM7lIV3ZbW1vYseTkZAIBP9u3b+83W/T5fGzYsJEDB8qZPHlKsBCR1+tl69bPSU5OCSuuckT2amtrWbp0aVjwlVqtZt68U8jMtNHQ0NjvOUIpLh7PxImTqK2t4a233ooq7w0NDTQ3N/cbKLpcLkpLpw/oOj8S0Dhz5szgOzvttNNIT8/gueeeZfny5djtdrxeL16vF7vdzooVK3j00UdQFBVW69GiPIWFhZhMibz//mo2bdrYT3b8fj979uzhk08+ITc3n3Hj8oJ/u+SSSzAa43j66adZu3Ytbrc7OGNvbW3l6aef5rXXXiM+PoHi4vHB8+bOnYtWq2P58rf4/PPPo8prZWVl8LvsHRhs4E9/+mPQo6AoCmlpaVxxxRUEAoHgOvtYov7Vr371K6n6Jd9EmpubeeKJxxGi16DX19ezf//+fv8qKyuxWq2kpKRgMBh4//3VrF+/Ho1Gg9VqRQhBe3s7q1at4qc/vZWGhkbmzJmDoij85S9/6fuIXZSUlKAoCnZ7NytXvsvatR9z8cWXMG/evAFddiaTCY1Gy4cfrmHdunXExcWTkpIMKLS2trJixdvcdtutOBxOZs6ciUajoauri6VLn6WmppobbrghqmFubm7iqaeewu128z//syTovuzp6WHZsmW0tbWTk5NLTk4OXq8XlUqFRqOhoqKcJ554jFmzZnP55d/p55LXaDRkZGTw7rsr2bRpIx0dHVgsFnQ6HXa7nc8++4z777+fd99dybx584JejhUr3mbt2o9pbW1h48aNpKSkBF3xTU1NrFy5kjvu+BlVVYdYtOhKrr/+BvR6PS0tLfzjH3/H4XCyZMmSqFHKNpuNLVs+Z/v2bZSVlWGxWDGZTLjdbsrLy/nLX/7Cvffew+zZs4Pu/m3btvHqq69y1llnc/7534pawWvDho2sXr2KSy+9jNNOO63PbawnNTWFlStXsnHjRnw+LxaLBZVKRUdHO2vXfsIdd9zOvn1lzJ07Nziz8/v9vPnmm+zZs4eMjAxsNhter5eamhpeeulFHnjgPoQQLFp0FXPnzkWlUlFVVcUTTzzOpEmTueKKRf1mpsuXL+fuu39BRUUFU6dORa/X43K5WLNmDStXvsO0adP47nevGNCDo9FoSE1NY+XKd9i0aSM+n5+0tDQ0Gg12u51169bxs5/9lN27dzN79uzg/V9//XV27tzBdddd32+gcUTGXnzxn9TUVHPdddcHg/nS0tKIi4sLDl7Xrv2YgwcrWbv2Yx555BGeeeYfOBwOfD4fixcvJi8vL+iFAVi/fh2bNm0mOTmF1NRU/H4/9fX1LFu2jDvvvJ329nZmzpzJpZdeGpTbnJwcGhsbWL/+E95+ezmbN29m7959rF69igcffJAVK5bj9XpJTk7myiuvDK6FJycn43K52bRpAx999BFJSckkJycjhKClpYVXX32FW265GZMpkcmTJ+P1evnFL+7iX/96ORi8KYSgra2NpUuXsm/fXhYvXhw1K+KYIjOVJd9UNm3aKAoK8oPFJrKyMqP+u+qqq4I5yE6nUzzyyCMiJydLZGVlivT0ZFFcXCiyszOFzWYRJSXjxQMP3C+6u7uEEEJUVlaKSy65WNhsVlFQkCcWLrxQzJx5gsjMtIhFi64Iy6UeCLvdLv7whz8IiyVdZGVlitTURDF58kRhs1mEzWYVEyaMF48++kiwqExNTY2YMWO6OPHEE8KKdoSye/dukZeXKy66aGFYdbSenh7xxz8+IdLTk0VWVqZITo4XBQV5Yt++vUIIIVasWCFsNqt44IEHgsVoouWpr169Wpx44kyRlZUpzOY0UVRUIAoK8kRmpllYrRliyZIl4sCBA8F85yVL/kfk548T119/XV+VN6swm9PE1KmTRVaWta+dxeKhh34rWluP5vyWlZWJoqKCfu2I5LPPPhPnnLNA2GxWYbWaxbhxOWLSpBJhtWYIq9UsLrpoodi8eXMwl3zp0qV9eeJ/jXo9r9crHnjgfmGxZIg33ni939+WLl0qiot7q6KlpSWFvC+LyMnJErfffrtoa2sLy69+8MEHgzKXmWkREydOEOnpyeKUU04Wp512qsjOtonly5cHz1mz5oN+hYEi86d/8IMbhMWSIQoLC8S5554jzj77LJGdnSlOO+3UqHnl/d+lVzz//POipGSCyM7OFBkZKaKwME+MG5ctMjMtIjc3W/zv//5cNDU1BgvKLFq0SBQW5ocVDQrl8OHDYv78M8QJJ8zoJ/9ut1u8/PLL4sILLxAmk0YkJRlFamqiuOCCb4lnnnlaXHHFFcJqNQfzyENrMdx0043CbE4TWVmZwmrNEMXFhcJqzRCFhfli/vwzRHa2Tdx33339nqe9vV089tijYs6cWcJgQCQm6oXVmiEWL75GPPvss2L27FmitHSq2Lt3T7/z7rrrLmGzWfp0QUrfPc3CZrOKqVOniL///amgXO7atUvMn3+GsNksYtq0KeLiiy8SU6dOFhZLurj55h+HFYUaKxQh5EbIkm8m69Z9wqpVq4b8XXHxeL73ve+FzTB27drJO++sZNeuXTgcdtLTM5gxYwbz589n0qRJYWvdjY0NLFv2Jp99tpnGxkZyc3OZOnUql112GWZzbOuSHo+HTZs28emn69m5cyddXV1YLFamTJnCggULKCkpCc4i9+3bxz//+QJms5kbb7wp6rr7+vXreffdlRQXF3PNNYvDZmkOh4M1a9awd+9euru7MRj0/PSnP0Or1bJixdts3ryZ008/gwULFgwaN1BeXs4HH3zA1q2fU1dXh8lkYtKkyZx88snMnTs36Dru6OjgyisX0djYwEsv/Qu3283q1b3uU4/Hg9lsYcqUyZx++hmUlpaGzZY3b97E8uXLo7Yjmqv7/fffZ8uWLRw4UIFOp6O4uJhZs2Zz6qmnBiP9fT4fzz23lMrKSi666GJmzZrV71put5snn/wzbW1t/Nd//Xe/5Qe/38+uXbv6atFvo7m5GYvFQmlpKXPnnsyMGTP6zagdDgerV6/mo48+orx8P3l5+UyZMpkFC85h3brevOVrr72W4uLxCCFYvXoVn3yyjtmzZ7Nw4cKobW5vb+f999/nk0/WUlVVRWpqKtOnT2fhwoUUFhbFHAPS25bVbNu2nfb2NjIyzEyePIl5805lxowZwYC8xsZGnnqqd7vdW265NWpqZnV1Nf/4x99JTEziJz/5SVTvR0tLC21tbXR0dJCcnExqagpqtYZFi65g69YtvPvuKk488aSwczo7O1mz5gM+/PDDvowUPVOmTOGcc86ltbWVjz/+iPPOO5+zzz47anzGkaUQt9tNUlISGRkZNDc3s2jRFWg0Gv71r38FAzFD4yjWrl3L2rUfU1ZWht3eTVFRMVOmTOWMM86gqKgo2D4hBFVVVbz99tts3LiBjo4OiouL++rqnx1zfMdokAZd8o0lEPATCAwt3qHR6pFrmw6Hg0AggEajISEhYdAdnBwOB16vF71ej8FgGFERiUAggN1ux+/3o9VqgwFBkb85Egw1kIGL5TdH2njE/XrEUAkhgkF+seBwOOjp6UGtVhEXF99Pge/evZvvfvc75Obm8vrrbxAXF4cQgq6uLgKBwIDtHE47Io2xy+VCURTi4uKiDniOtFOtVg/4To/0zWC/EULQ3d2Nz+dDq9UOKiNHn8+Fy+VGr9cHjX60ew3nXbhcLjweDxqNmvj4hBHttBYICOz2bvx+PxqNhvj4+H73FUIEi9cMtNGIEAH8/gCKAmp17HHXW7du5bLLvk1qahrvvbdqwIC73ra6URQV8fG98ubz+YLPHaucALz11pvcfPOPOfHEk3jhhX8OmEng9XpxOp34/X6MRuOgWzAfkYkjvx2o5sJYIKPcJd9YVCo1oynMpNFooq7ZDjQoiHVXtcGfWTVkQZpYFHysBjlSKQ9HGR4hPj6+X5W1UOrr6+ns7OCEEy4JKkJFUWLq2+EMLI5gMBiGVKKxtDOWnbEURYmpgFD48/VGyg91r+G8i6GMTGyyN3RbFEUZsl8URYVGo+o3OFqx4m3Gjx9PScnEsHcaCATYt28fDz30W7xeL1deedWgs9lobdVoNP2ey+Vy8corr7BgwYJ+O9/19PTw2Wef8cgjvUF4V1yxaND+02q1w9IFw5UJadAlEsnXgq1bt6JSqZg9e86I9+iWfL3ZsuUz7rjjdpKTU5gzZw4nnTSLrKwsmpub2bZtG//+9/s0NjYwf/6ZLF68+JhsM/r228u59957eOGF55k9ezYzZ55IcnIyhw/XsmPHDt577z26u7u48sqruPDCC78R/Sxd7hKJZMzw+bzcdNNNfPTRh7z44stR16sl33x6C6v8lQ8++DdNTY1h6VuKopCRYWbhwoX84Ac3DlinYLhs376dJ598kg0bPqWzs6Of98NqzeTaa6/l6quvibnmhDToEonkPxaPx8POnTvx+31MnDgpZrel5JtHbx2HKqqqqqioqKCrq4vExCRycnIoLCxg3Li8ES35DIbf72ffvn3U1dVRWXkQp9NJamoa+fn55OfnYbNljfmGKdKgSyQSiUQiGRayUpxEIpFIJNKgSyQSiUQikQZdIpFIJBKJNOgSiUQikUikQZdIJBKJRBp0iUQikUgk0qBLJBKJRCKRBl0ikUgkEok06BKJRCKRSIMukUgkEolEGnSJRCKRSCTSoEskEolEIpEGXSKRSCQSadAlEolEIpFIgy6RSCQSiUQadIlEIpFIJNKgSyQSiUQiDbpEIpFIJBJp0CUSiUQikUiDLpFIJBKJRBp0iUQikUikQZdIJBKJRCINukQikUgkEmnQJRKJRCKRxILmy7hpm9dNvdtOmauT9h4XakVFgkZHviGBHEMiaTojakUZ8PweEeCgowN/wD/iZzCqteTHJxN6l2aPk6YeB4ijxyxGE+laQ/j9AwEqHO0IEeg9oECy1kiWIWHI+3oCfiocbWH3MGn15BgTw57FKwIcOAZtLIhPHvX7qnJ2Yvf1jPh8tUpFQVwKOpUKd8BPpaOdgBCjeyhFwWYwkaLVD/vUCkc7Hr8v7FiKzogthvd39N204w8Ewo5nGk2kRsjKUKzvqKelxxX8/3i1ltNSbOhU6pivYfd7aXLbEQzepwoKBkWFolKhV2tJ0RpQhrh2t6+HGlcX4sj7UsCsTyBDZxzyubp8PdQ4O8OOZejjMevjwo45/F6qnJ1H7zECUnVGMmN8f6G0e93UubpHJYpDvfcuXw/17m72Ojtp97oAhQSNFqsujmJjEsk6I4ZhvO8j19vlaKfb50EA8Wod4/Tx5Mclkaw1oB/G9SJlu9njpMzRTr3XSXePGxAYNXqy9fFMjE8lZRjPKxAccHbi8XmDxwxqDflxyagUZcjzy+xt+EJ0oFalpiA+BU3IuQI45OzEGaKjdGoNBXHJg9qRI5Q72ukJ0Qeh+ir0HrWubrq87pHPnhWF/PiUYb3rr7RB7/B5WNVUydKWA7zncfR1kxLSZTBZY+DyhHS+lVFAaaI5qmB2+Xq48cCnfOxxjPhZfmay8OuS09AqR1/axvZaLqreFva75fmzWJiRH6HkPEwuXwshAnS2IZG/j59HnsE05GBmyr4PIUR5/S51HLcWzg4TPoffy7UHN7DZbR9xG28zZfCHiWeO+r39q3YXd3YcHvH5J+niWDHpbMw6Iy1eN1eXf8K2UQwQjhj0jwtP5rTU7GGf+vChLfzV0RZ27Lvxqfx5/Kn9Bm/R+LjtMN+u3IQ9bLAlWJ0/hwUZeTE/R1OPizsqN7PeF6IkFBV7dWdSYkqLfcDl7uaEPR/QIwIx9Bug0rBAa+A0YwoXmAuZZEof0AAccnUxrewjCGnrdYkWfls4l7QhBlPVjnamln0Y8o3DmznTuThzQvjv3HbO2v8xDRGDrOHwkqWEK8eVDvu8HV3NzD+wPuwZh4fg3wUnc1Z6br+/OAM+3m06yKvNlfyfuzOqvrOptVwel8q5abmckpJNkkY34J2cAR//bj7ES80H+D9X5PV6rzlOrePbcSlclJ7HzGQbiYNcL3KStLOriTcaK1juaGG3zxOlTwRZah2XGlO41FzASck24tWDmxCfENxTtZWXupuDx2xqLf8qmMO8FNugvS6E4NaDG1npPjrgutSYxNMTzyQ5pF1+EeCx2p080VEXPJaoUvNG/mzOTMsZ/B4I7jy0hTdC9EGovgp9ltcayri1+cCIZXSqWsuKKeeSq48fMxt73FzujR4nP9u/nkWHd7IqaIiVCE2jsNvn4d6Ow8yuWMed+9dx0NkRdd6hQQFl5P9USnRdp4743UDCkBnxu7Webh6v3o7d7x2yLywRz64MMIrUjbaNI1ZS/ftlNM+hVY5tu1AUElFG3DpVlPu/4mxnWWMF/iFmiY09Lp6s34NdBPpdQxnmA33aVstWX09Q9kEBIXi7qYIAYlhvyBpr36FAwM/7Hge/7KjlhPK1/PrAJtoGmXlYIq7xj+5mXqzfizeWAUSkrEd5a4oCCaOUCWWEwqAoo5fFaPe2+708cnALl9fs4BV354D6rs7v44nuJi44tIUb9q5hU3tdVO9Vt9/LHyo/5+KabX3GnCgGV6HK7+Wx7ibOrNzMkn0fsamjnqHe0mG3nfvKNzCvfB0PdtaxOyiT/d/lYb+XP9qbmH9wE7eWfcyhCA9MVA9dxLutC/h4oGYH1e7uGM4N1z3qAZ4sUm93iQC/qt1BubNj2PfQKmOjB3WKMuZ29rgYdJffxy8PbuQfjpaQselQA1/BY92NPFi1FV8gwFedHuC5rgaer9+Hb7TuZMmXwh+bKyhztA8623i1YT/LXJ2j/yYCPl5pOYgrytfwUlcDDW7H8Wm0ENzfUcsvDmykO4bBaK9iEzzUfIBVLdXDHHj8Z+AXgmdqdvHL9uq+OWBsvOLu4mfVW2mJGFx5RYB/1OzinrYqlGHoln+6OvhV9TZaQ5Z0Itlnb+XG/Z/w687DeMRw9KzgKUcr3y/7mE/b64bdR6s93fyhajudo/XUDcK6Hie/q9pGm9fzHyN7x8XlvqWznlfsrWHHUlCYqDMyXp+ARwSodHdT5e+hPkRg81QafpQ9Fa1q6HGHCchV1DHP2kJd7ceKdiF4vLGciXEpzB+BK7j/JxNOPJD3JbcRwACMU1RoY3ySBEUdZg40itLr4Yg0mEBzhMJKRCFeiT7qP9bj3V2+Hv5yeDe/LZob1ZW4x97Kn1oOHpN77ehsYrW7K+rftvs8rG2rYZFt4oivn4FCiqKEyVAAcCFoFYJIFfdidxPnNFVySeb4mOSyLuDn14d3UhSXREl8yjGXsQwULMOQX/UxnP3kKwrxMc51vIh+krjP2c4TrZURcgwTtQZKDIkIBBVuOzU+D/UiwJGFhnhF4ee2yf3iE6qdXfym9SBKiE6IB6ZqjRTpE9ApCgc8Diq9LupFAG/Qi6hiibVkwHiHSlcXPzqwkX/3OPrNRFMVhSxFTZrWQADo9Lmp8vtpj9BKa3xueg59xpOaU5hqSh9WP7/Q3cDEhjJuzJoS03r6SPg/ewsT6vbwk9zSMdGH4xVVr8cxBhIVFWM9Rx9zg+4XgnfbaukKEwSF32dO5LuZEzD2rd35RIBd3a280VjOsq4GykSAe6wTmJyQHlWh+CIE6xR9PC9MPCvmDtMoqjF5wfsDPu6v2UGWIYHxcaMISBP92zhdH8/rJfPRxPjcY2XQC1VqXi6Zj00XF5sbSFFIUveueaVp9fyx6JSobsVDjjYur90e5lT7QUo2V5iLovq/hopXGAl/627grNZqLjbnhynqbr+XPx/eTVmMs9ihvom3WippHWS29XxLJReYCzHFuAYayZJEK/89bkaYBPlEgE6fh71dTfypqYKNvqNmvQt4sbmC8y2FMQdUbfS6ub9qK48Wn4xZaxy5qAuIDP1cnJTJXfknxTy/PZaBRo/ZpnLKMGIh4tXaMN30RWcjNRHBrHek5bEkd3pwoOgXgjJnOx+2VLO0rYrtfi+3JGczPy23nw7b2FFHS4iX0gjck57PDTmlwesFhKDc2cGqlkO83FbNLr+X7yfbOHeAdjj8Xu6t3MxHEcY8BYXz4pK5MXMi0xIzetsmeoN5N3fW81JjBW84Wgj1Y63zebiv8jP+OnE+acMICu0Ugocb91NkTGbBEGvdI8WO4I/NByiJS+bCjLwRTwNEn4cu8ujL409jnDExRs+WMmiMxNfCoAsEO93dYZ9lqdbAxZZCTCEfgkZRcVKShZmJZq60t7C6tZpLLcVRR94KfWvoocYLhTSNfsD16OPJhz0OnqjezgNFc0nW6Ed2EYV+M2ANKlI1hpg8FmOJGkjR6If18QaVkUrNjAFG8vFRXH65ujhmJmYct7Z5heCR+j2ckGgmty9qOoDgvZZDPNXdeEzucdDZwf91Hb2WHphvMPFeyJriux4727qaRhT0B5Cq1gafP5ITTBlMNZm5qvwTdgeOBqJt97qpdHUNa8b9qqOVaYf38JPc6SOOrFaUvnXMCDlJ1eq/FPlOVGtGJNtHNN4OVydhjmRFxTXWCWHKXKPAtIR0piakc7G5kFcaK7g6c0LUgUlnRPBvhqJwiWV8uHFQYHJCGpMS0rjUXMRz9Xu5LnvKgIP6T9pqecfZHvQOKIAJhfstxfx31hTiQj1UCmhVKs5My2VOso0ZNTu5r/kATSFa/TVPN5c1Vw7bq1QZ8PNg7Q4KjYkUxiWNyfusFQF+fXgXecZEpiakjVQdh0XWHyFZoxuFrBx7jotlcIvw0epOfw9Ob8+As7lppgx+mjcz5gjNryJ/7m5mad1eer4G6/+SCGXX4+Sl+n3Bd1fl6ubh+n3HaIALq1qrqAwxpFPVWn6bdxKlEYO/V5sq6BlF2uJgTEvM4Fvx4cqtQvhxD3O90Qv8vqWSt5sr5Xp63/v1RL4zEaC5xzmgociLS+L2/JlR014DCJyB8Oh/p4A6j33Q691TOIcsffQBnSvg5+mGMlpCZpwCuCU1h+9nTw035hHEqTVcl1vKjSlZ/b1KzQdpHWZalwA+6nHy2+qtwz53OPfY5HXzUPU2mgaJJ/gmMOYGXUEhN8IdJwJ+flm5mY3th49ZwEIwAvFLwhYxslYQPNRUwcdtNcdUzX0FHBDfAK0bPsgap6goVevCPoY/tVWxpasBrwjwUv0+NnnDFcG5UZSlLoYljpYeF8vba8N8L+clWpmSkMb1qeGpTy/am6mIIUp3pMRFzixE8D/hRiXC1Rgp660iwG/qvuCL7tYIY8SIs8G+XDFXRnGmQl7EUpQK+MWhLaxoPECjx8lwhvgqFBIjBnotCH5VtZW3Gito8jiHzMyIZL+9jU0hAwIFOFGj579sk2JautCr1NyUU8psdfiE690ee7++FXMAABcqSURBVL+6A9HIUFSoI9aTX+hu4YX6fUNmTgQzVIbAgEJ8xPf4oqONpw5/gfsYDpIVvloKecxd7ipF4VyThRe7m8LcUM8723m+Yh3n6U3Mj09joimdUlMGmYaEIdd+BaLf+nKd38urDeUxGbx5yZlk6hOOaTtPiUtmoi6Bh9qq8PSpxSYR4N6a7RTEJVE4zPV0IXrdv6E0+3t4rbEipgCgk5OsZI3BGjP0rn2903KI1Bg8KHnGJE5KNH8F51FHmajRc7V1Aotrd6CI3lXbwwE/T9TuYrHXw8NtVWG/X2IyMy/RwqrDu8KOxxKuuK6tlvdDZms2ReHijALUisK56XkkNR+gs0+ptQvBe82VlMSnHvOgIXfAT21EtH6WSoUmZBmsVw4FkdnhVyRn4exx8jd7S/DYNl8Pv636nCcmnB7M5ffFGDUdbQ19j7ub1xrLhxwMKyicl5ZLwjH05n3U1UhzDGbXqNJwdlpumBFUgBmmDNKaKmjte/oAsNrr4r3qLZxYb2RhfDpTEjM4wZRBuj6BhCFyuScmWqC5PGyg8ZHXxUfVW5lRt5sL4lIoTbIyM9GMWZ8wZG74ju5makLejQDON2XEvBYMYNHHc1myjU2th8LexsbOeqYnWQY9N1tr4O7UXO5tPOol6EHw+6YKJselcFZ67oAptxoltlmoW6XiWWsJd9Xvpa6vrQrwcOshpsSlstBcMKxASkF/fQwKK1urMHcPHT+SYzAxO8n69Q+KU4CzMvKY21zOWq874gNVeM9j5z2PHdoOUajWcaExiTNTx3Fyio30AaIzFZR+a+hbfB6uqNka05v5XHfqMTfoCYqaH2RPocxj5xVH76ccANb7PNx/6HP+UHxKTEVLQmfi2giB2+PzcGXNtph6/bPCk/sZ9EpnJ+4hineoVArF8SmD5rBXiQA31u0mlgTE35uLx8SgCwQVjo4hUxo1KhXFkWvCEQNGBYX5qdnc5Wjj123VweNvujpZW72V9r4PWQEma3T8IGsKdVFmIkOl/dj9Xt5sDR8cnGJIYkpfTEGOMZEbTGZ+39UQ/Ptz7TVcYZtI1jEsRuEK+Hm5bm9fsZOjTNboyYyQGeVI/mzIqx6nNbLAOp5dFZ/yacjg5E1XJ0XV2/l5/okYVZpej4UYesIbbQ39DVcHb1RvjUnWG5Osx9Sg39tRCx01Q/7uQl08J6dk9ZvVnpBk5bL4VJ5ytIZ7K1DY4nWzpe/66SoNF+hNLEjJZn5aDrYBBuAzksxcH5/B3x0t/f62zedhW1cDdNVjVmk4X2/irJQszk7PI3MAmamM4q4vTcgYlrFRgJMSzRhbDxHqu7LH4NJ2iwCXmwtp8br5feshnH2apE4EuLt2J1lGE5PiU6Oe2yNEzFUmv5WeR4ffyz2N5XT1hVd2CMG9dV+QazRxwjD0kkJ/fawCftiwLyY9eG9qHrMSLWMe43Vc0tbSdEYeK5jDzQc3stHrpmeALjvg9/K4vYXH7S2c27CX/82eximpOcMYScXwO2Xs1vnMOiN3586govwTPg+JIH7O0cr4w7u5LXf6l+oO/F3NDv7Z3TTob0rUOj4tvQBVTAFOSgy/GBsB9gvBbVWf8+EQLulzdXG8PPW8CK9P/2cyqNRcZ5vMp/ZWPuyL/PUA9SHuOYHCTy0TmGxK43AUgz7UGvLu7haedx6tSBUPLM4oCAaT6VVqFmbk8/uu+uAzlvu9rG+t5ju2icPqyR3uLpY17A8tSEgAQXWPk+32Ft52deAM+aMBuCY9f8jqb0d6YlJ8KndnTeX6qi3BiG4X8Nf2Worjkrk6ohrc8Zb143Fv1QDSHafWcE/BbFwVn/KqqwP3ANdvCfh5ztXBc64OTm7az83WiXzbWtzPQ5mg1nJvwSxcBzbwqrMdzwDXawq53pnNB/ihtYTzzYX9Bhz9iwgJsnTDz1LQa/TEKwquEDmq83rwBALohwjcNag03Jg9hT2uTl53tgf9IRu9Lh6s2sYfik7GrBtdsJlWUfE920R2OTt4vrsp6Gna7vPwYPU2Hiueh00fN+LrB4ajB4/TWqnqeH0apYlmXpl4No9bJjBLoyNpiE5Y1ePkvys383bTga9VsE1JQgr3ZE/rl0P7p5ZKVrYc+lJbEhAB7EP88wv/16avY2mPCMQuPXlGE7fZJmEeYMnnelMGl5gLR1SBr0cEeKc5PDd5gkbPnJSssKtNMZm5RH/U9ekCnms+iDswvLKoT7s6uLRmO5fVHv33ndod3NZUzgvOdjqECCokDfAtQyLnZRQM6x7z03K4NaMQU0gLmkWA39fvZUtnI//J2AwJPFlyBi9nl3K21kjGEAp9g6+Hmw7v5M9V26KuI2caEnhywum8kDWN07RG0pXBh8prvG6ur93BU9Xb+5UE1kYZrI9kb4WAEP2WSowqVdQqnNEGS1ZdHHePO4ETImIE/ulo5ZnDu/Ecg4DiVI2eu8bNYJ4uLqy/XnN18mTtLlx+/zdK7o5bLXcFsOjjuCF3GlfaStjZ2ci6zkbWdNVT7uuhKooQHxQB7qrdycxECzlGU8h4sn+OtkVRmKkb2i3pR2BQj12zVSiclz6O/3V2cHNTefB4gwhwX+1OHlJrY6yU11u0Ikw4FRWzdcaYZr1a1di10URvvWNDDEFgpq9ZpsKZaTl8v7OBB9vDXa6lah03ZU0ecR5pjauLv3XVhx0r1sWxvbspYu1dIVWrI3Qa9pnXyca2OuZHqRc+WuJR+JYxkYeLTo5agESIAO4BlL1OUfH9rMmUOzv4a3dTcICwy+/loaptXG0uimmSHW0NvUilYYLWMOS3ElAUVMe43sIcjY5U9dDvOVNrHHRGlKDRcXHmBBaYC/iiq5l1HfWs625kn9fFvojALAG0CcEDzQc5JdnGScmZUb+ly20lLDDns7e7hU2djazpaqDM62J/lECvViH4bUslc5JtzAq5Xv90QIUdznZOTssZVj81ubtxRMhGukY/ZAxUvKIKxjpNSkjl/uxpXFu1haaQa/2+pZKpcSlEVn0wKKphv+98YxK/yT2BKw98GmZn/txWw7S4FDwxDGai56HDfK0xWEtlMNI0huPicDruu62pUEjS6Dk1LZd5abn8yO/loLOTnZ0NvNFWwzJPd1hFpP0BHxvbasjOmhTsj2h56LN08Sybel5Mxm6s3R9aRcX3siaxzdnBUntzUCB2+r38rHobnbGY9Ch56FN1cSybcm5MBWOitTGWgUQs49V8lZpnJ5xBdgzrumPZ1bFMKsQwfSJGlYbrsiazwdHKR33rwyYUlliKKR1mJayjrjnBhy3VNEUo3WXOdt45uKnf7yOVWLMQvN58gNPSco5pRbQZah0/sYznIkvhgPUSFEVBO9iATa3lttwZ7C1fF1yqAHjD082eut0xuiP7r6F/N8nKA0Unx7QD27EOGPx15hTOsBTGps+GuLdCb+GZ2Sk2ZqXYuKWvAMzuriaWt1XztquDdnFUStsQvNtSxQlJ1gFrcCRr9MxNyWJOShZLRIAKZyf7u5t5o7WKFRHXqxcBVjQfDDPo04z9A3R3dTXhCfhjriXgF4J3O+v7LZ+ajUPnkvcQCH67KhTOSh/HrY527myuCP6mHcGPD++gK+L9+xDD/qYVYFZyJndbJ/Lj+j04+s7vRHBL3Rcxedyi56EL/l58Kvkx5M8ryvGJiP9Stk8N7aQEtZZppnSmmdK52Dqe02p2cktIeU0fsNnVwWVCDGmIVShficIy9H10d46bQVX5Oj7sMwwBYKfPM6oeU6GMWIHdapvMEn/xEH3Ym1ISiyJTfYl9rVYUfpcznd8EBq/cplHUUQtCDEaBMZGfZE6irHor9SLApQlpfMdSPOLNbjq8Hp5uOxRFsUGslaxXOtu40d7CFFNsRXZOV+uYbjAFVV+dz8Nyjz3sfvlaAwvNBUMUP1J6DcsgOrQgLom7c0qprdxMecjSwD5/zygkvVf9fRnfs0phTGT7SHsmxKcwIT6FCyxFnFVfxvfqvggbdK9zdxAQYsjBm9I3eZgYn8LE+BTOMRfwSl0ZP2vYE1ZCudrdjSvgD84kJyVmMF2lYXvIu/qLs43vdTQwJzUrprbs7G7mY0d4mqJeUTE3yTrkuZHR4lpFxfezJlHm6uDZkMyJyigBvD4hRrTNrlpRuMxazA5nO090Hq09Xz/KFDZljGTlK2/Qyx3t7LW3cn5GwYCVzkwaHd+1TuCW1sqw6Zcj8PVc5xgfl8wd2dM4dGgLhwK+Lz0SoOQ4Vlwb+8GgwuSksUuHOyd9HNd0NfBeVyM/zpoStl3jsLwIwKftdewcZdGMyoCfVS1VTDbFFo18eaKVHxbNCf5/a48L774PWe6xB+XwDXcXC+rLuD5n2qhn/vNSsrjVOZ5bG/aGBUn9p9LocbKmrZqFGYUkaKL7OPQqNQvNhcxqOcjmkGwBh9/Xz59W53Gwoe0wZ2fkDbjsY1Rp+HbmeN5oqWS59+j13EL0pRD2GvQ8QyJnJqSxPaRaoRq4r2YbfzOayBkifa25x8XD1dv7uflvSEgn0ziy7KF0rYE7cmewv2I96wcowjNakjQ6fppbSkV5d9iWrN8kjktQXIPHwc8PbuLi6m08cHATe+2tAxZDqPc4+vlSC7VxQ6e+AF+xHP/elL3UbG43F4cFDo12RCgZJSKWHGM1P8yexq9sUyiNcVasV/q7Kz0BP882VRCqovJUar5lSOTCIf7lhcRBKMCyjsPUu+0xDyRCSdMZuSOnFBEhQHc3lbOls2H0MwNF4ZrMCSxJsn0jhoujocvXw32HPuOq2p3cXPYx2zsbBtyBscvXQ0dEwKNZYwj70O1+L48c+pzLa7dxS9ladnQ2DZjj3+BxcDjCa2XWaEkIqS+gUhSuzZxEQYgnzg+82+PkR+Xr+LyjIWogsl8I9jnauLtiAy+6wrNL0hQVi60lGEcRuzM+PplfZk0lS6UeM1WebTBxZ04p49XaY3KP/7jCMh0+D7879Dmvu7tQgPvaa/hnZz2Xm8ycnZJFtjERrUqDXwQ45Ojg4fo9YecnANNMaWHuzmiFZer9XpY1VMTcvXEaHaenZh/TTR2iKzoVV2ZOYJernb91NhCrryFaYZkWfw9vNlTEPJuKU2s5PS3nmLexSwjeaz5EWoyzVpWiYn5azlenlG+M/ZdrSCDLUhRzf0eLTt7Z3cyGiHzvW9LyuTZ7ypDXe7OhnP+q3x000Ot9Hta0VnNN1qQRNXtWso0n0gv5cchaZbMIcF/VVp6PO5M0nXFU3Zqg1vLDnGl84e7mvQF2khtI1iO/izJ3N282VsTsXjXr45mbnHlM3J+fdDXSPoytRPPjU5jeF1/hFQGW1n7BM32z32ecbbxdvo6r49M4IyWb8fEpaNVaBIImt52n6vexP2QLUTVwnik9KHM9gQDP1H7Bw10NgMKzzjbeKV/L1aYMTk3KpCQ+DZ1GQ0AI6l3d/KOhjM8jNhDKi0/rpxenmNJZkp7PL5oPhOWRv+Wxs6liHf8v0co5qTmk64woikJHj5t32mp4q7uR3VE2KLorLY/SUXrMFBTOTMvhTlcnP2oogzHwaSrAKUmZ/DJzIj86vIuOGOVroMIyq1qrsHQZY1Q7CnNSbFh1cWOm2sbUoAtgeeMB/tZXKONIdxwM+PhdZx2/66wjRaXGrKhwiAC1UVzrM3RxTDWZ+734yKC4z3weLoup6Eov5+lNzEq2jrlBP+LquT13BhX71/JvjyOm0o/RCsvs9nn4Tu32mO97oT6Bk1Iyj3kbD4kA1/cZmtgkWUV1YsZXqDZ/7Ep/OK7oyFlNAMHbzZU0hCiCOJWac9LzYoqWPyt9HLOb9rMpRIG+21bDRZaiEfWlWlG42jaJT7oaedVz1OX4YY+DZ2p3cVv+SaM2iLkGE/flTufggQ3sj3V/9ShBca+5OngtpsIyvdyWZGNWspVjsUHl3R210FEbs5Z70jopaNA/ba/jty0Hw3LPm0WAx+zNPGZvJk5Rka1S40Nw0O/vZ7RsKjXzUnOC7djQWc89zRVhgcJNIsCjXY082tVIsqLCqlLjRXAgyprzVJWGC9JyowyyFW7MmUa9x8HjXQ1hgZgNIsBvOuv4TWdd3+BXGdCrpaV3Z7/rckpjKn08FFpFxTWZE9jrbOfJrrFJfVQpCpeYC/nC0c5D7TUxa4xohWVuaoh9j4ckRcUHRtOYGnTVWKvNS63FPGQuIneAl90e8FPm90Y15jmKivuyp2HRH/sOON6u63EGE3flzqBAffziEL/cPdnCO/s/caXgoLOLtzrrw1T2TQkZFMRYBjjLkMD8xPBykS+5O9nR1TTiZ0rVGbgtp5SkkO/RBTzeVs2HrdXH5Js/IdHCLzInkXAcP7IvT77C7zw3OZMnsqYxWaWJ+v05RYD9fi8Ho6yTp6LwG+tESkKqpM1OsvBM1jQKB3Bld4gA+/zeqMY8TVH4iWU84wfYYSxOreWXhXO4O3UcSYO5TwYw5umKwi9Tc7mrYBYmjfaY9WiyRs9tuTNYcIyreUZ6k27OKeUyY/KI9eRws+T1x0FOx1znJ6i13JQ7gzeK5rEk0UJhDKM4LTBHo+eZcSdyamrON8YYnJKUyS+tE0mRC+H/Efy75RD7ItZHz08bhy7G7W8V4NvpeVgj5GVZU8WoNpg4MTmT+9PDi8gcFgF+V7uTeo991O1WKwqXWwq5LSXnP+6d61RqLsssZlnJfH6eks2kGL1jpWotj9om893MkjCvkEGl4ZLMCbxTcga/SslhYoxr1NPVWh61TeGakHTfaCRqdPw0/0ReGXciF+kTiKU2mwE4RxfHs7kzuT3/pGGVtI6VfGMiP8+eRskY1tPI1Mdxe+50pn2Nd/WM5LhMF9WKwsxkK9OSzNxkb2NzZz2fdjVR4bHTIgJ0iAAGFHJUGnJ1Rs5LyeHUtJwB61erUJiki8M2CsOYpo3r556LU2u5KmRUKISIupWgWlE4RxdPIKT4RE4MQq1WFC61FFHl7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Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '10%', '10%', '10%', '10%', 150.0] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:22:40] "GET /myclass/api/Prepare_and_Send_Inscrit_Bulletin_Note_By_PDF/kQpidvNlrbxO_Wetuq2QtF4WMubfrYy-6g/684f106ae266de5f7fd519ea HTTP/1.1" 200 - +INFO:root:2025-10-17 20:53:06.344514 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:53:06.347407 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:53:06.351408 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:53:06.353407 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:53:06.369413 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:53:06.395442 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:53:06.397443 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:53:06.401445 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:53:06.406444 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:53:06.409441 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/Get_Partner_All_Class_Few_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:53:06.415443 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:53:06.422443 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/Get_Partner_Session_Ftion_Reduice_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/Get_List_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:53:06.437445 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/Get_List_Partner_Document_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/Get_List_Evaluation_Planification_No_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:06] "POST /myclass/api/Get_List_Unite_Enseignement_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:53:14.263080 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:14] "POST /myclass/api/Get_Personnalisable_Collection/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:53:14.264204 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:53:14] "POST /myclass/api/Get_List_Partner_Document_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:53:32.950790 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 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'

 

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BULLETIN DE NOTE

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\n\n\n \n\n \n\n \n\n \n\n \n \n\n \n
\n \n Photo
\n
part nom5_client   part 5
\n Né(e) le \n
 
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\n

Note par matičre / UE  

\n\n \n \n \n \n Credit\n \n Rang\n \n Moy. El\n \n Moy. Ens.\n \n Validé\n \n \n \n \n \n \n \n \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n \n \n\n \n\n \n\n \n \n \n \n \n \n \n \n \n \n\n \n\n \n\n \n \n \n \n \n
Matičre /\n UE\n Graphe
INITIATION A LA PROGRAMMATION5036.39\n 7.36\n \n Non \n \n
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.0\n 0.0\n \n Non \n \n
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.0\n 0.0\n \n Non \n \n
\n

 

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Note générale  

\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n
RangMoy. ElValidéObservation
3\n 2.13 1Indulgeance du Jury
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Imprimé le : 17/10/2025  

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0RhuKMU/Zqs5woAbCtKhprveBAHIQQQGjEdhNo44S15MLyMfCwfVuRhT0EydN0YcEVHPLYVJKLESJ5v9wzEYLueXZKvhVCEXveRMAeA/Px8bN68qU1BVgDgt99O4bffTjcKI9we9Ho9fvzxx1Z5tIvFYjz99LN47733MHLkyGaF+Z1apkwmw7Jly7Fly1b07t2n4TeJxAzLlz+NTz/9PwwdOqxTNd/W1gnRvR3ISSAQYOLESQgPH2ky7ZUrl3DixIk29aOioiJ8++23JpdGxGIxFi5cBDc39/taPjGB3laTBsehl42hx7yCCIVqZbufq+J12F6cgoomBu/nrJ0RZPNfr1pbkRmed/ZtZFrhAeytK8dNRXmXdpRJPTzxumsgHJvMxOtBWFt0E6cr8rrNSe5mXSUO1hSj6RRmtJkVJrn4QMgO2+g0wbNnzx5kZma0fXIoFGLjxg0oLi7uUBkKCgpw8OCPrcrv6aefxcqVK9u8vUwkEmHkyJF4//0P4OPjCx8fH3z++edYvXp1qxy/7mfs7e2xbNkyk+vDHCfA9u2RyM3NbXXf++mnn5CUlGgy7ahRYzB27Nj7vi2YQG8HjhJzND2NRglCja793q+xNSU4r6oxuD7LxbeRQxcHYJyzF3ybrJcrQDhQnNqlWrKQ4/CYey8ssHOHWWMFHtm8DmtzopFW1/UBOAjAn5X5KDTyrk+5+MNFYsE6aSeRkpKCPXu+b7emePNmEg4fPtyhEKDR0dFITU0xmW7WrNl49tlnTWrlzQscDqGhofjiiw2IjPwOERFTWhURjQEMGTIU06fPMNlPUlKSceTIEeh0OpPPzMrKwu7du0xq9NbWNli0aCHs7Ozu+3ZgAr0daHjeqJBpr26qIx67i1INDkSZa2GHUCOmY0exBZ519DTIf0d1IdK7OHqZrVCM1Z4DMNnC1qDzXNCq8K+s6yju4m10PBHO1hSj6fTJSyjBKAcpBGDaeWeg1WoRGbkNJSUd07A3bfoKOTk57fs2dDqcP3/epLm7Rw8nLF68uMN7jzmOQ79+/eDl5cWOVG0DVlZWmDdvvkmnL4FAgG3bvjVp8eF5Hvv27WuVZSgiIgJDhgxljcAEevuo1KjQ1JvVHByshe3bKpGsqMBxheG+2kecvI2e7iXkODzo4gP7JuvZubweJ0symvFT7zxczSzxludABIvMDETnobpybM6L7ZT1/ObIV9chpd7wCMiJFrZwkJizDtpJXL58Gfv37+vwc8rLy7B3795WaWVNKSsra5Uz3oIFCxEQEMga7S4SHByMJ56Yb1KjLikpxt69P7QYEjYxMQH79v1gMs8ePZywcOGidltlWp7cARUV5Q2n8rX3Lz8/v83+J+2Febm3VTsnHtdrDDUWO04At3Zs3eJB+LEkDalNBOAEiSVG9fBo9j5vSzu8aNsTa+/w8tYD2FKehWluAW0Kj9vmjg4g1NYZa2XBWJ59HcX035VzFYDPy7PhZW6LJ9x6dcladrlGiXwjE4Yhtq7d7mnf9rr7e2h9tbW1iIzc1qqDSYjIpDa7Y8d2TJ48GSEhIW0uhylzOxFhxIgRXb7Hl9EyYrEYjzzyCH7++RiysjJbtILs3r0LERERGDBggMHvarUae/bsQVFRocl+9cQTT6BPnz5d861yAqxY8Qr0el2HnuPr64fffjvTJZOObhPoZdp6FKg7LwCDABxczCzuujk1X1WLPYoSg+v9Le1bfVb2nWQpa7C/qgAcGpvspzvIWlwLFnMCTHf1x9rqgkbRJG7yOpwty8Z8Wd8uNu1wmOTkhdWqGrxSnNKo9BVE+KgwCd4Wdhjp0PlepyX1ChQZrJ8T3Mys7+0Rjwjf5NyAo7DzYuALOQGe9Q2DuJPPqb969QqOHv3J4Azxpnh5eWPgwIE4cGAfWtqDXVtbi507d6J3795tOme8oCAfdXUKWFg0P1kOCuqNgIAAJlHvAXx9fTFv3jy8++6/W0ynUNRix47vEBQUBAuLxuNcVFQUDh48aFKYe3v7YPbsR7t0371QKOzwRFEg6L6JZhcJdA4v5dyAGdd5Fd1LZIadfSbAWnj3NLA6vQ4bc2OR3WTGZgbgIXt3CNvxvqfLshHbZLvbEJEEU1x8TdeJTQ88Y9UDXynKGq5pAewsy0SEqx96iLt2RigRCLBQGoQMVQ2+qCls9FuiXot3c6KwwcwS/padG0zB6Bo9ocsOPelEiY73aks69Ym9OQGepsGdOxkvK8WGDRtMCnOe57F8+XIMGzYcly5dQn5+y+cPHD16BFOnTsXo0aNbXZbS0lKIRC1/84GBgd2i/TBaI7wEeOihh/Dzzz8jKup6i2mPHTuGGTNmYNSo//aHuro67N69GzU11Sb3tS9duhRyuZxVeiNFq4vI0muRrFN32l+9TtPla8MtoeJ12J2fgA3VBYZailCM0T082mw7KFTXYUd5lsF9D9r2hIeRuO1NsRCIMMvFz3CSoFHiXHlOt9SLg8gMKzxDEWFuWN5f1XX4JCcaFdrOjX1dayTMKzgOTmLmkdxReJ7HkSNHceWK6UAeffr0xcSJk+Dj49OqEKB1dQps2vQVampa77hZV6cEZ2Ki7OHh2Satn9G1uLm5Y9GiRRCLW7ZE1dersG3bNigU/z2O+dKlizh27IjJvhQWNgSTJ0d0W1S8+16g/69AADJU1ViXcRUri5JhTDStdPGDczuOiv29PBfntapG05QAgRCzXP1bPTkYaN8Ts80ar5frAPxYkoG6Dq79tBZPCxu86dEffY2Ent1eU4xv8+I71UnO+EE4BP09HoTj70BOTg42bfrKpJlQr9fjqaeegouLMziOw4wZM+DhYTqi1p9/XsCxY8da//21ok27y+GI0XrGjh2HkSNNB5v5/fffce7cWQBAVdWtMwBMOU9yHLB8+VPsKNv7UaDzAIo0KmTXK1r9l1VfizhFOc5X5OHTzGuYm3QGaytyUGvEQjDD3AaP9AyAoI3OX1VaNXaUZRg0xmRrZwRZt/54RjuRGR4zYp7fVV+Nq910aAsHYJi9G/4h7Qf3JtqUGsDHZRk4UprZaSfS2TSz7FKkVbEvugPodDrs378fhYUFJtOOGzce48aNx+11czc3NyxfvtykZkVE+Pbbb02a529jbW1lMkpYfn4etFota8B7CDs7Oyxa9KTJbYQajRqRkZEoLS3F6dOncfbsGZP9Z8qUhzBixAhWyUbosgXpXgIRLDrRw9lGKGqXh3AdgBezrqKtxth6AHVEBpHbGmnHIjO84z0Yju1Yu71YVYCjTbZeuXIcHnX1b7Nn+IgeHphQlIyTTQTajyXpGNFDDjHX9fM2DsBUFx8kq6rwUWkmFHfUWwnxeCcvDr4Wtgi1delwXs05lZVq6u/tr43jsKdnENwknXeQjYDjIOoks2NycjK2bv26VWkXL17SaM8xx3GYPDkC33+/2+TRqTdvJuHAgR/xwgsvmJwAODk5Q6fTtWi+TU6+CY1Gw9bR7zHCw8MxderD2Lv3+xbTXblyGXv2fI9ffvnFZH+QSCRYsmQpbGxsWAV3hUAnMrayTXjPMxQDjIRIbb8pgYNlO7wFedw66rOzCROZ4VOvQejbjndU6nU4VGoYMGGUhR0G2bm2+XkuEgtMdZDiZElaYy29rhxP1pQitB3PbA8WAiGelwcjW1WL7YpS3Kkzxes1eCv7Or70D4dnB7fUuZlbw47jUN1I4+eQV19zj39uHAY7yuBzDx65Wl9fj61bv4ZSaXpnyvTpMzB0qGEgjx49euDZZ5/Ds88+Y2Jew+Hbb7di8uTJ8Pf3bzGtVCqFlZUVeL75iXViYgJSUlIwaNAgNqLfS8JFJMKCBQtw8uSvqKgob1GGfPLJxyaXV4h4LFiwEH379u22dxCLxR32cpdIJN0WpKjDAp3jjOvNPcUW8DL/35xFPW5hj1Weoehv69KuTXQxNSXYcodneoNA5ATYXZDUrmdW67V/6cl3bh/j8WNJGvrZukDUTR3KXmSGVV4DkJb6B8422bb4s6oGn+VE49++Q2HdgaNebSSWcOMEqCZ9IwvB77UlWKjXwkbIHKTayqVLl3DkyE8m092KCDbPYKvR7bFg7NhxGDduPE6f/q3F55SXl+O777bj7bf/0eLZ1dbW1vD19W9xLzrHCXDu3DmEhoayvej3GL1798YTTzyBL75Yb1IxNIVM5oE5c+Z0WzheIsLbb/8Dw4cP75gyKhB2m9MmCyzTBgaIzPCUkzcecQtAD3H74oVriceh0nSjv0XWVSCyrqJTy7y9uhCLlFXw7UatMNDSHv/2GIDF6ReR2sQj/bOqAnjlJ+IZWT9I2mkqlptbw0tsgZtqxR02IeB4fS1K1UrYWLKYzm1BoVDgq6++bDFy153aef/+oS0K/KVLl+G33061qJVwHIcdO77DlClTjWr7t3F2dkb//v1NBpfZtWsnHnroIbYf/R5DKBRi9uxHceTIT8jKyurQs5544gn4+3df+xIRpFLZ3yoCIfNybyUPmtvgcNB4LPUIbrcwB26Fed1eXdRt5c7l9ThSkt5pDmmtZZi9G95072PUj2JdSSpOdWBbnaVQhCFGHAdreT1Ol2d3+7v+3fn1119x5cplk+lcXFzxxBPzWtSoAWDgwIGYOXOWyefxPI9vv/3G4AzyRhqHSISRI0eZPNylrKwU33yzFXV1HQ9mFR8fh+zsbNYxOgkPDw8sWbKsQ7sR+vULxrRp05kF5n7X0C0BvO3iB3+L1mlt5eo6PFV00+B6oroOefW1kFm0fxlBT4QjJekope7dZrOlIhdz3YPQ08yq2/IUchxmu/ohS1WNf5VnGUwy3smNgZu5Nbza4SDGAZjg6IGvKnJQcofw5gB8VZqBSc7eXRr69n+JwsJCbN68uRWnoXGYO/cxBAUFmXymhYUF5s+fj6NHfzLpfX78+M945JFZmDRpUosTBF9fvxbDiQLA3r170LNnTzz33PMmJx3NER0djVdfXQWxWIznn38BEydONBnYhmFCaxQIEBERgUOHDuL69WttF1KiW2edy2QyVpn3u4YuBjDWQYZHXP1a9bdI3g9rHQ1jqOcQj49ybqCyA0FS8uoV2FmVj+7eNZuk1+BUaVarwvLwnRi+x1IownMewVhiZahNX9fV44PsKOS3MzxwiK0zhjbZf08AonRqHCpOY3vSW8mBAweQlJTQKi1rzpw5rdaQgoODMW/eApNroxzHYePGDSgvb95pSiaT4eGHp5l0LOJ5Hhs3bsTGjRtRU9M2B0mdToerV6/i7bffQnLyTcTHx2HlyhVYv/5zlJSUsI7SQZydnbFs2fJ23Tt06FBMmDCBnX7HNPS2I+EEWCbrhzO1pTjTZBvYr2oFduTH4wWvgW12XCMQfi5JQ3qTMK/2nADP2Uth2UkhbXkiHKkpwhVt4y1cm8rSMa2nH2xELTuUEBE685w0J7EFVnsNRGrKeZy/o0x6AD8qKyHKiQLa4QZoLRTjmZ6BOJd1DdVNpiAflaShv7UTwnvIOz3yv5Z4XKjIx0A7V9iIJH/rvp6eno6dO78zbRHhOCxevKRNGpJEIsHjjz+OI0d+QllZaYtpY2KicfToUcyfP99o5C+O4/DYY3Px88/HkJaW2uKzNBo11q//DElJiVi+/CmEhIS06JDE8zyqqqqwb98+fPnlRlRW/teHpa5OgfXrP8fVq1fx+utr0K9fPxaZrAOEh4cjImJqqyLB/dfaY4nFixfDwcGBVSAT6O2jp7k1Vkv74M+sa40iwykBbCjLxHB7KQbZ92zTM0s19dhZnm0QaW6JjQve9A2DWScF8CcCfPMTsbAgvtG2sQsaFc6X52KKq1+L9ws5rtM7hb+VA972CMXTmVeRfoeTnB7AHmVVu587zFGGCSVp2K+sbHQ9n3isybmBrebWCLRy6DShXqfXYnNuHD4uTcc8O3e87TMYtn9Toa7T6bBz5w7k5+ebHFz9/PwxderUNmtIgYGBWLhwIT755OMW7yUibNr0FcaNG9dsbG53dymWLFmKN99cY3ItVq/X45dfjuPcuXOYOHEiJk16EL17B8HVtScEAgGICDU1NcjNzcWff17A/v37kZubY/S5PM/jwoU/MG/eE1iz5g1Mnz4dlpaWbJBsB7a2tliwYAHOnTuLujpFq+6ZNGkShg+/O0FkOI5DcXERQsOCJAAAIABJREFU0tPTO+2Zjo6OXTo5YQK9GcY4eeHVqkK8W9U42loqr8enuTHYaOUAhzbEDj9dloUYXVMvYg6PuPrDojOP/OSAsc5e8ClORnITa8Dm4hSMdfKEZYvburrGrDXaUYZVymq8XpSEmjtiF3TEMG4rkuBptyCcybiIiibxEC5qVXg+9QI+8QlDSDu3F95JmUaFT7Ki8FFVPgDCF1V50GcQ3vEJ+1tq6lFRUdi1a6dJIc1xHJ555lk4O7cvpsTMmY/g4MGDyMhoeVDMz8/DgQP78eKLLxoNO8txHKZMmYITJ34xGU3sNiqVEocPH8JPPx2GSCSCubk5HB0doVSqUF1dDb1e1wrfgVtUV1dhzZrVuHEjCq+/vgY9evRgg2Q7GDhwIB599FF8++03Jvuera0dnnxy8V2bQHEch7fffqvTrDJEwLp1n2LGjBldVmZmP2oGM4EQS2T9MExkZiAMvldVYV9BEvhWiqNqnQbfl2agrkn6+Zb27QpMYwpniSWecjDUdH6vr8W1qqK7Up9iToD57kF42l6KztqRyQEY7SjFuy4BBi3BA/hNU4elaX/idFk26tsZS17D3zKxP3vzLD6qymuYgqgBbKjKx/sZV6DQ/73CjtbV1WH79kjU15uOrDd06HA88MAD7V6/lMvlmD9/fqvu3759O5KSkpr93d7eHmvWvIGgoN5tHEgJWq0WtbW1yM7ORmlpCTQadauF+Z3PsbOzYxHpOjKumplhzpy5kEpNL988/vjj6Nev310tr16vh1ar7aQ/TZeXlwn0FvCytMNb0r6wbCLSOQCfl6Yjprp1zjIXK/NxSmPo/DXXxQ82os4POCDkOEzr6Q+XJjPLKgB7SlKh4fV3pT6thCI85xGCKZ14nKqIE2CerA9W2bkZ/f26To0HMi7jzZQ/EFdT2upDYlS8HimKCnyYcRnh6Rew30gUOi2AWr0WujbsWrgX3HouXryIQ4cOtkKA8XjmmWcahXhtDw899DD69DEd3au8vAzffvst1OrmHU979eqFd95ZC0fH7tWQiQhTpz6E55573mR8ckbLBAYG4rHHHmsxjUwmw5w5c9gOg7aOh6wKWma8szeWV+bh/2qK//txA0jkdfg8NxbrrUa3uI5ap9dhb3EalE22V0VIrDDEwb3Lyi23sMXTtm5Y22TJ4KiiDE8ryhHcCTHV21UuM2v8y3MgclP/wDVd5xyrai0UY4XXQBSmXsCuJuvpt1vs05oi7FKUYpalA8LtpRhg6wJzkQSWQjEshUKoeR4qvQ4KrQpRNaU4W5mHSGUl1H8Ja2O2mNfspVjjPRj2RhwNtc0I+cNlWXCuLu6SunU2s8TEHh4tpqmsrMTWrVtb5a0+bdoMDBkypMPlcnFxwZIlS7Fq1QqTWvGxY0cxffqMZk/q4jgOYWFh+PjjT/DWW2+26iCZjsLzPGbOfARvv/02c87qDIVDKMSMGTNx/PhxJCYmGKlvPZYsWQovL29WWUygd7KJSCDEsx79cT7pDK7rG5tMtisrMKrwJhbK+jV7oEpibSl+UFU1ES/AbCdv9BB3nelOzAkw1cXXQKDnEuHHolT064R15fbS16YH3pKH4Jns6yjsJGuBm5kV1vmHQ559Ax9WGT/Jq5jXY6OiDBsVZbAVCOEjEMFdIIQ9J0Qd8SgjPa7rNKhvhcb9vpMPnvfo36yFxeiBOMTjleKULqvX52x7tijQeZ7H8ePH8ccf502uCwoEHBYsWGg0xGt7mDRpEvbsGYzLl1s+Z12pVCIychsGDBjQrCYsEAgwfvx4ODk54R//eBsxMdFd9x2JxViwYCFeeOFFODo6sgGxsyb2cjkWLVqEN95YY3BcamjoQEyd+hALItMOmMm9Ffha2uNVtyDYGjG9v1ucglSF8T20WuKxvzgNyiYCYoTIDOOcPLq83H1snLHU0nAQ+qGmECmKirvY6ThEOHniTRd/dKa7i6uZJV73GYwNroFw4wQtTlhqeD2idWr8rFFit7oWhzV1uKCtNynMewnF2O8xAC97hnbJcklHMDVBKygowMaNG0wKcyLC44/PR0hISKeVzdraGs8993yrYlqfOnUSp06dNDHhEKB///744osvMGPGzC4xzbq5ueOTTz7FqlWvMmHe2X2V4zBx4iQMHhxm0PeWLVsGV1dXVklMoHfdQPlwTz8ssnGBsImmncnr8Un2Daj0OoP7UhQV2FFraF59xEHeLZHMLIUizHU1PM0qidfjRFnmXa1TMSfAPPcgLLbr3GUHO5EET3uEYL/fCCy0doJrJwWjkHECvGjnhoO9xmBmT39YCP9exi29Xo+DB39Ebq7pkLv29g54/PHHOv0QjGHDhv11hjpMTii2bt2K4uJik0LBy8sb7733PjZt2oIRI8I75RAMe3sHLFiwELt27cb06dPZmnkX4ejoiMWLl9zhZEiYNOlBjB49hlUOE+jNDA6d9BwLgQjPe4Sgt5EtX7uUFThUlNIoxhoPwvHSTAOTcm+BCBEu3bc2NNDBDbPMbQ2u76/IRV694q62jZ1IglWe/THF3KZT20zIcRju4I5NvcZgl/cQPGZhD6927vPvLRDhFTs3HPAPx8cB4ehl5Qjub9jX09LSsGXLllvHHbfwx/N6LFiwsM2e5K3BzMwMy5cvh0QiMVmOqKhrOHLkp1adwmVtbY0JEyZg06bN+Oyz9Rg9egxsbe1aHTuciCAUChEY2AvPP/8Cdu/ejbff/gf8/Py6NDrZrfrmm/2jLoh2SIQW8+xIvPX2EB4ejgkTJt5SQCytsGDBQtjZdd/hSqbqorP/upoOqxm2IjMscZA3GkwIBEdx92/tcBSbY4W91EgZO2efsK+VPd6Th+BsVaHBbzGqakzUqhvWxau1GlQQb1AeuZk1/Cy7z7HGXmSGBa4BkFcXNhJEBCBNWQWZuXXjwVEkwQJHeaMDTngQLLtor7WnuQ0+8h6CvnesLXMAHDqh/5gJhBjv5IlwRxmSFRW4VF2IazUlSNHUIUWvRSHPAw2BeDmAE6CPQIheQglk5jYYZ++OXjbO8LO0g6CNA7uVxByrHeXoTr92XwvbZgVHcXExli9/yrTlRCzCjBkzu2z9sn//UHzwwUcoKjK9fVIgEKKurg7W1tamrWgcBzs7Ozz00EOYOHEi0tLSEBcXhytXrqCgIB95eXmoqalGfX09hEIhrKys4eTkDC8vT3h7e2PkyFHw8/ODs7Nzt4QYFYlEeOONNw3Wjxu/vwBOTk6dluft6H3Tpk1r2Rolk3VbRDwrKyssXrwEp0//hhkzZiIsLKzbvpeIiAgMGzasW2VUr169uvT5HBELes24f9AToUSrQq1WDZ1OC9xeM+cE4AQCmIskcJJYsDPV/4dQKBSorKyERqOBXq8Hx3EQiUSwtLSEk5MTc766y+h0Oqxfvx4TJ05A3779WIUwgc5gMBiMvyu1tbWwsrJisfKZQGcwGAwGg8GmQwwGg8FgMIHOYDAYDAaDCXQGg8FgMBhMoDMYDAaDwWACncFgMBgMJtAZDAaDwWAwgc5gMBgMBoMJdAaDwWAwGEygMxgMBoPBBDqDwWAwGAwm0BkMBoPBYDCBzmAwGAwGwxARqwLG/QARQaVSged5mJmZQSxmx6MyGIz2odfrUV9fDwAwNze/Z47gZRo6475AoVDgrbfewpNPLsKNGzdYhTAYjHbB8zyOH/8ZTz/9FNat+6RBsDOBzmB0EzU1Nfj552O4cSMK7u5urELu0DTy8/PATlFmMFpHQkIC/vnPf0Kr1WHRoidhZWXFBDqD0Z2kp6ehvLwUwcEhsLOzZxUCIDn5Jj788ANs3rwZPM+zCmEwTFBYWIj3338PcrkcH3zwATw8PO6p8jGBzrgvyM7OgZmZOQYOHAQLCwtWIQAOHTqEr776EsHBwRAI2FDAYLREfX09Nm3ahIqKCnz00Ufw9va+58rInOIY//Oo1WrEx8eBiODv7w+RqPXdnogamaNNCb4707dXSLYlzzvTcRzX6F6O48BxnNH0CoUCN2/ehFgshlQqa/Tb7XuM/Z+IjD73Tg3f2O/tLa+xe1tTnq56bnO09f1bWz9tKWNb3rm179KZfa8tfd1UPbVU562pY2PlNHWfXq/HrFmzsHjxYsjl8ja9W3dNmJlAZ9wHAr0eUVFRUKvr4ePj0+pBLTs7G9euXcOVK5dRUVEBBwdHjB49GsOGDYOTk5NB+sTEBJw+fQapqSkQiyUYP34cRo8eA2tr61blqdfrkZ6ejtjY2L/yrISjowPGjBmLsLAwgzwBYNu2bSgpKcaMGTNRX3/rPePj4/6yRgzE2LFj4ejo2JD+woULOH/+HOrqlLh27Rp4nsePPx7AmTOnAQDTpk1Dnz59UVZWhm+//QZCoRDLly9HYmISzpw5jczMTDz77HMICQkBEaGkpARxcXE4f/48iouLYW5uhpCQEISHj4S/v7/B4BgVdR3nzp3HgAGh8PHxxcmTJxEdHQ2VSgl/f3+MHj0GgwcPNvAa1ul02LRpE+rqFJg9+1FoNBqcPXsGsbGxePDBB/Hww9MAAEqlEjdu3MCFCxeQnp4OgYBD7959MGrUKPTp08dgMpebm4vvvtsOHx9fPPDAA7h27RpiYmKQn58HPz8/hIeHIySkv9FJIM/zKCwsxLVr1xAVdR25ubmwsrLGyJEjER4eDjc3t0bvX1paiu+++w48r8eyZcthb2+49BMfH4ejR4/C3d0dCxYsBMdxqK6uxtdfbwERYdmyZUhPT8eZM2eQlpaGxYsXY/DgMOTkZOP48V8QHx8HgEN4eDjGjx8PZ2fnVk8i8/Pzcf36NVy9eg1FRUWwtrbCyJGjEBYWBplMZjDBOH78Z0RHR2PKlCmwsbHFpUuXEB8fB41Gg5CQ/hgzZozBfXdOsuPi4nD16lUkJyejqqoK7u5ueOCBCRg8eDB++ukwSkpKMXXqVPj7+zfKNy8vr6GcxcVFsLa2xogR4Rg9ejREIhG+++47mJubY8GCBbC0tGy4T6FQIDo6GteuXUVmZiYUCgV8fHwxadIk9OnTG9u2RYKIx9y5jzX61nQ6HZKTkxEVFYUbN26gsrIC7u5SjB49GuHh4Q153Eaj0SAq6jr++OMPJCenQCp1x/jxD2DYsGFtUibaqw0wGP/TpKSkkFTqRoMHD6KSkhKT6Wtra2nz5s00YEAoeXjIKCDAn0JDQ8jDQ0YeHjKaNGkixcREN6TneZ6OHz9OgYEB5OPjTWFhgykgwI+8vDzotddeo6qqKpN5VldX0+bNm6hfv74kk7lTYGAAhYQEk6ennDw95TR16lSKj49vdE9lZSVNnTqFAgL86MknF1FAgD/5+nqTn58vSaVu5OEho2nTHqasrKyGe/bu3Uu+vt7k5eVJcrmUPD3l5OfnQ35+PhQWNohSUlKIiOj69etkZ2dBU6dG0CuvvEy+vt7k5uZKvXsHUWpqCmm1Wjp16hRNmjSRPDxk5O3tSaGh/cnHx5tkMnfq3TuIjhz5ifR6fUPeOp2O1q1bRzKZO82YMZ3CwgaRXC6lwMAAksulJJO5k5+fL3300UekUCgavWtWVhYNHTqEQkP707/+9U8KCupF7u6u5OhoS6dOnSSe5ykhIYFmz55F3t5e5OXlQSEhwdSrVwDJZO4UEOBPn376KdXX1zd67qFDh8jV1Ynmzp1LU6ZEkIeHjAID/cnDQ0YymTv5+/vS5s2bSaPRNLpPpVLRd99tp8GDB5JcLiUfHy8KDu5L3t636nX06FF06dIl4nm+4Z7Y2Fjy9/elmTNnGDzvNl999SXJZO60cePGhnsTExPJxkZCEydOoFWrVpKfnw+5ublSYKA/xcXF0qVLl2jQoIHk6Smn/v1DqG/fPuThIaO5c+dQbm6uyb6nUqlo//79NHJkOMnlUvL19aaQkGDy8vIguVxKI0YMp7Nnz9Adr0I8z9Py5cvI01NOS5YspuDgfuTl5UEBAX4kk7mTXC6l4cOH0dWrVxvlxfM8ZWdn0TPPPEN+fj4klbqRp6ec/P1v9VkvLw+aO3cOjRgxnAIDAyg7O7tROX/4YS+Fh48guVxKHh5SCgrqRV5eHiSTudPYsWNo7dq15OrqTMuXL6O6ujoiItLr9XT9+jWaNu1h8vLyJKnUjXx8vBry9/f3pWXLlpK3txfNmDGdysvLG/IsLS2ld9/9N/Xu3euv79KfgoP7NXw7Tz75ZKPvS6vV0ubNm8nPz4f8/f0oLGwweXl5kI+PF61bt460Wm2XjnVMoDP+5zlx4gS5u/ekpUuXGAiKptTV1dHatWtJLpdSRMRk+u677yguLo6Sk5PpwoULtHz5cpJK3WjatGlUXV1NREQVFRU0YEAo9evXh44fP07p6el05cqVv4TWYDp79qxJYf7GG2+QTOZO06Y9TPv376f4+Hi6efMm/f777zR//nySy6U0f/78hjyJiJKSkqhv3z4kl0tp/PhxFBkZSbGxMRQbG0s//LCXhg8fTnK5lNaseZ10Oh0RERUUFFBcXBz961//JKnUjd566y2Kjo6m2NhYSkhIaBB427dHkru7K8lk7jRp0gT6+ustdOnSJfr999+pqqqKDh48SAEB/jRo0EBat24d3bgRRSkpKXTjxg368MMPycvLgwIDAygmJqahvBqNhubMeZTkcin5+/vRq6+uovPnz1N8fBxFRUXRhx9+QIGBAeTu3pP27t3bqI4uX75M3t5eJJdLaejQIfTRRx/RH3/8QefPn6eysjKKioqioUOHkJ+fD61cuZIuXvyTbt68SfHx8bR161YaNGggeXl50KFDBxsJ2c8//4xkMnfy8JDRiy++QOfOnaP4+Di6evUqrVmzhmQyd/L0lNOVK5cb7qmvr6fPP/+M5HIpjRo1krZs2ULR0dGUlJRE165doxUrVpCnpwc98MD4RgJ1z5495O7ek955551GE507hd0rr7xM3t6edP78uYbr+/fvJze3W20xbtxY+vLLL+nixYv0+++/U35+Ps2dO4fkcint2rWTkpOTKTExkZYvX0aBgf4UGRnZYt+rr6+nDRs2kIeHjEaODKetW7dSTEw03bx5k6KiomjNmtcb8s3JyWm4r6qqiiZOnEByuZQGDhxAX3zxBV2/fp3i4uLoxIkTNHPmDJLLpTRz5kyqrKxsuC8jI4MiIiaTVOpGDz44iXbu3EFXr16luLhYOnfuHL344ovk4SEjuVxKY8eOaeiPKpWKPv10HXl4yCgoKJA++OB9unTpEiUkJFBUVBT93/99SgMG9G+YGH711ZcN7Xzp0kUKDu5HMpk7zZ8/nw4ePEg3btyg2NhYOnr0KD3++OMkl0tJLpfS6tWvNdxXUlJCS5YsJrlcSnPmPEpHjhyhuLg4unnzJp09e5Zmz55FMpk7rVy5oqGcSUlJJJO508SJE+j8+XOUnp5OJ0/+SoMHD6LRo0dRXFwcE+gMRnvR6/W0adMmkkrd6LPPPjOZft++fSSVutHcuXMazbxvU15eTk888QTJ5TLat+8H4nmeoqOjycNDRkuWLG40YUhMTKSUlBSjg/edg/jWrV+TXC6lBQsWNNJI7tQSpk6dQq6uznT48OGG67/+emuiMnPmDKMDxZEjP5G/vy8NGjSQioqK7tCUtfTPf/6DpFI3OnbsmMF9Op2O1q5dSzKZOz399NONBvJbmmYM9enTm8LCBtOZM6cbCcjbgvv9998jqdSNVq9e3TDYFRcXN2iQBw4cIJVKZZDv999/Tx4eMho/fhwVFBQ0/BYZuY1kMnd68MFJFB0d3ahOi4qKaPr0aeTj4027d+8itVpt8E6HDx8iLy8PmjDhgQaLiUKhoMWLnyR3d1fauHGjwWSvtraWli9fRjKZO23Zspl4niee5+nEiRPk4+NNERGTKS4u1iAvhUJBS5cuJXf3nrR58+aG6++88w7J5VI6evSo0b5QUlJCERGTqU+foAZLiV6vp//7v09JJnOnJUsWU0ZGRqP6rq6uosBAf+rXr2+jNs7JyaHo6OhmLQG3+95vv51qsDrduHHD6Lu89tqrJJW60fr16xuux8fHU9++fWjo0CF04cIFgz4QFxdHAwcOoMBAf4qKiiIiIqVSSS+//BLJZO60evVrlJeXZ5CfWq2mf/97LUmlbvTmm2+QVqslnufp119/JblcSmFhg+nkyZMGbazX6+nChQvUr18fcnbuQadOnSIiosLCwgYr0rp164xayyoqKmju3Lnk7t6Tdu3a1fC8zz//jKRSN3r55ZeorKzM4L60tDQaM2Y0+fh40aVLF4mI6JdffiGp1I0++OD9RmmjoqIoOzu7xbGgM2CurYz/aTQaDa5duwatVgMfn5a9UsvLyxEZuQ1isRivvLICnp6eBmkcHR0xZsxo6PU6XL58BTzPw83NDebm5rh+/ToSExMbnHWCgoLg7+/fokNMSUkJvvnmGzg5OWPVqlVGt8E4OTnh0UfnQCKRICYmuuF6TEwMBAIBHn/8CfTt29fgvgEDBkIu94BSqURZWWnD9ZqaWsTExAAgo/nV1NTg2rWr4Hkezz//fCMHII1Gg71796KiohzPP/88xowZa7BGKhaLMXPmI6isLMTJkyegVqsBAJmZGaipqUZYWBgiIiJgbm7e6D6hUIjJkyfDy8sbMTE3EBsb2/BbQkICRCIRXnzxJYSEhDSq099+O4XLly/h0UcfxSOPzIJEIjF4p2HDhsPfPxD5+fnIyclpWG+/dOkSevRwwpw5cwz2E1tbW2P06NHgeR75+QXQ6XRQq9XYuvVrCAQCrFy5En379jPIy8rKCrNnz4JIJEJ8fDx0Oh1qamqQnJwMsVjSbByEysoKZGVlQSaTQyaTNZTx4sWL0Om0ePHFl+Dt7d2ovgUCIfz8/FFSUoQLFy5Ar9cDAORyOUJCQlqMiKjRaPDNN9+A4zi88sor6N+/v9F3mThx4l/r+/FQKusAAEVFRaiursIDD9xaG27aB273faVS2RB4JS4uDt9/vwseHp545plnIZVKDfKTSCSwt3cAAISGhkIkEkGpVGLjxg3geR4vvPAixo0bZ9DGAoEAffv2hYODI4RCAXr16gUA+OOP35GUlIixY8dh2bJlsLOzM8jT1tYWYrHoL3+L3gCArKwsREZGwsnJGa+88gp69OhhcJ+vry9GjBgBrVaLxMREEBEcHBzA8zx+//13pKenN6QNDQ2Fh4dHlzvHMac4xv809fX1iI6+AUtLKwQG9moxbVpaGi5e/AO2tvbYt28fjh49YiQVh5SUZAgEAiQl3RLePXr0wBtvvIk1a1bj2WefwcqVKxERMQW2trYmy5eSkoKiokJYWVljx47vDITcbW4PDpWVldDptOB5QlpaOiQSCWQyqdF7BAIOAoEAYrEYFhaWjQR2XFwsvL19jXrr1tTUIDY2Br169WoQLLdRKGpx7NgxCAQCnDt3HqmpaQAMg9IoFHXo0UOK/Pw8qFQq2NraIj+/ABqNBoGBgc2+p6WlJYYPH4HU1BQUFRUBAKqqqpCamgorKyuDSZZWq8Xly5chEomQkZGJd9/9d7NOjqWlJairq0NtbS0AIDs7G0VFBZg5cxYsLMybqUMhAIKdnR2EQiGSk5ORmJgIgHDo0CGcPXvW6H35+fkgArKzs1BdXYW6OiVSUpLh4OBgdKJ4u40rK8sxZcqUBoGlUCgQExONwMAgowLQysoKzz33PJYvX4o1a15HYWEh5syZY9SBsinZ2dmIiooCxwlw7NjPuHDhgtF0hYVF4DgOCQnxqKtTwtLSCteuXf1r0jjAqNPb7X4nkUgaHMESExMgEokxe/ajzXqJ19fXIzk5GRzHoWdPt7/uS0RsbCyGDBmKyZMnNysUq6urkZSUgCFDhjU4qp05cwYcx2HWrNmwsbExel9tbS3S0tIgk3k0OCpevXoVhYX5cHR0wqZNmyESCY2OBdeuXWuYrBARgoODsXjxEkRGbsOzzz6Dl156GePHj4eZmRnzcmcwOkpWVhZKSorRu3cfODg4tJg2MzMTZmYW4HkeBw7sazGtWCyBg4MjOI6DUCjEo4/OgUgkxtq1/8Krr67CyZMn8eqrryEwMLDFrTfZ2dl/aXDV2L+/5TxFIjGEQhE4ToCysmKkpqbA2toGMpnxwbGmphbFxUVwcXGFi4tLo0lETU01pk59yOhAk5KSAqWyDqGhAwwEb1lZOYqKCiAWS3D69KlWlFcIiUQCnudx+fIl8DyPgQMHmahbMQBqGETLysqQnZ0NV9eeBoKgqqqqYbJz5colXL16uYUnc5BIxBAIuIb2Njc3R0hI/0YTnjs9qktKigFwkEqlEAgEqK6uhkJRC57nm5nw3fn+IlhaWkIkEiMvLxd5eTl48MEIWFsbFyxJSTchFIowaNDgBqGVnp6G6upqTJr0oIE3NXBri9WECROwefPXWLv2Hbz//rs4ffo3vPzyywgPH9li3ysqKkJ9veqOdyET/d0BQqEQGo0GyckpsLCwaHZyUlFRgaKiYtjZ2cPBwQFarRZZWdkg4iGTyZoVyrW1tUhMTICFhQV8fX0bnqXTaREQEGBUU75NamoKRCIxBg0aBFtbW9TV1aGwsBDm5uaQSt2bvS8nJwdVVZUYNGgw3NxuTSIKCvIhEolRV6fAnj27Tfbz2+UyNzfHypWrYG/vgA0b1uOpp5ZizpzHsGLFSri7uzOBzmB0hIKCAhAR+vbta3SbUFNtj+M4PPLIbKxevdqo6bbpYHp7e5WFhQUee+wx9O/fH5s3b8Lhw4eRkZGBr7/+Gn5+/kbvJyKo1bfMkXPnPo7XX3/d5PsIhUIIhUKUl5cjIyMdAwcOanZrUmZmJioqyjF27LhGwXSysrIgFkswYMAAo5pydnY2RCIxgoODDQS+Wq0Gx3Hw8/PH119/DUfHHibLbGdnB51Oh5iYGNjbO7Q4sOl0OkRH34BOp4OX160lkry8PJSWFhvdIqTT6aBQKCASibB79x7RMj2ZAAAgAElEQVQEBgaaLI+lpeVfZtIE6PV6eHp6GhV8t7bARUOjUTcsTRAReJ7HkCFD8dVXm0weysFxHGxsbJCamgqOEyAkpL/Re9RqNdLT0yEUiuDhIW8oT25uLgQCAYKCgpoNiCQSiTB58mQEBwdj584d+Prrr/HKK69g/fr1GD58RLNl43k9iAijRo3BJ5980qr+bmNjg8LCQqSnp8HOzh5ubsbbsqSkBEVFhfD29oZUKgXHcTAzk0AgEECpVDabR0VFBWJibmDSpMkN5vHb6VuqayJCTk4OOI6DTCaDUCiEWCyGWCyGVquFRqNp9t68vDzU1dXB09Ozob/rdDoAwGuvrcbcuY+Z3FMvFAobJikODg54+eWXMXJkOD799FP88MNeZGVlYdOmza2ynDCBzmAYQa/XIyYmBjyvh6+vr8kT1pycekCn0+LmzSRYWlo2a6IzZs4VCAQQCATo06cPPvjgQ7i5uePLLzdg586d+Mc//mlUI+E4Dl5et9ZECwryYWVlZXJQvU1iYiLU6noMGDCw2YEuMTEBHCdopPHV19cjISEBOp3WqHalUqkQExMNrVYDDw/D393d3WFuboEbN66CCCatHv8dNHNRUlICb29v9OzZs9lB+fz584iLi8WQIcMQEBAAAEhIiAeARu9x52TB398fmZkZqKysbHV5FAoFrl+/DqFQ2OwkQKVSITo6Cu7u0ob4BRYWFjA3t0BVVRV0Ol2rB+jy8goIhUK4uroaba+KinLEx8fBwsK8YQKo0Whw48attvD29mm2790OjiKTyfDqq6/By8sbb7zxOj7//HMMHhzWbL93dHSERCJBamoKVCoVXF1dW/ku5cjPz0NY2JBmJ5O3td6goAiYmZlBIBDAyckZt5asUqBWq41ah65fvw6JRIJRo0Y1TDatrKz++kYKoFQqjVoqNBoNrl+/jvp6JYKCggDcWo93de0JrVaLmJhYhIUNMRDMer0eFy/+CZ7nERIS0nDd1tYORDxSU9NgZWXVapO5Xq+HUCiESCRCWNgQfPnlV1izZg1OnDiOkyd/xWOPPd6lYx5zimP8Twv0qKjr0Ot5BAX1Npk+NHQA/P0DER8fh2PHjkGr1RrVAhQKRcP/4+Li8OWXG1FSUtKQzsrKChERERCLxUhISGjx4JOAgAC4urri/PlzOHHiF4OY6jzPIyMjw+BEp4yMW9pcSEiIUQGh0WiQnp4OkUgEX1+fhoFMrVYjLS0VSmV1wxplU00xKioKjo494OfnZ/C7lZUVpk+fCWtrO2zfHom6ujqjA35hYWET824xqqqqoFarUVtba1AnPM8jOTkZ//nPR9Dr9Zg79zE4OztDp9MhLS0NIpEYvr6+BgOypaUlBg0aDL1ej02bvmpYd2/6Tv/f3nlHx1Vdi/u700fSqGtmNCpWteUqG4MLmGLAlICBAAmmOLz3CxB4TkKABMgjgVASQhJqEpKQAAYC5NEMxhhsggFj44JxxUWWbFnF6l1TNeX8/pA8nhmNpJFkmZLzrWXW4mpuOefuu/c5++y9T2VlZdg929ra+OKLnUyePDWs8E4ohw5VcvhwLdOnzwgakaKiIoqKiigr28fy5cuDM7lIV3ZbW1vYseTkZAIBP9u3b+83W/T5fGzYsJEDB8qZPHlKsBCR1+tl69bPSU5OCSuuckT2amtrWbp0aVjwlVqtZt68U8jMtNHQ0NjvOUIpLh7PxImTqK2t4a233ooq7w0NDTQ3N/cbKLpcLkpLpw/oOj8S0Dhz5szgOzvttNNIT8/gueeeZfny5djtdrxeL16vF7vdzooVK3j00UdQFBVW69GiPIWFhZhMibz//mo2bdrYT3b8fj979uzhk08+ITc3n3Hj8oJ/u+SSSzAa43j66adZu3Ytbrc7OGNvbW3l6aef5rXXXiM+PoHi4vHB8+bOnYtWq2P58rf4/PPPo8prZWVl8LvsHRhs4E9/+mPQo6AoCmlpaVxxxRUEAoHgOvtYov7Vr371K6n6Jd9EmpubeeKJxxGi16DX19ezf//+fv8qKyuxWq2kpKRgMBh4//3VrF+/Ho1Gg9VqRQhBe3s7q1at4qc/vZWGhkbmzJmDoij85S9/6fuIXZSUlKAoCnZ7NytXvsvatR9z8cWXMG/evAFddiaTCY1Gy4cfrmHdunXExcWTkpIMKLS2trJixdvcdtutOBxOZs6ciUajoauri6VLn6WmppobbrghqmFubm7iqaeewu128z//syTovuzp6WHZsmW0tbWTk5NLTk4OXq8XlUqFRqOhoqKcJ554jFmzZnP55d/p55LXaDRkZGTw7rsr2bRpIx0dHVgsFnQ6HXa7nc8++4z777+fd99dybx584JejhUr3mbt2o9pbW1h48aNpKSkBF3xTU1NrFy5kjvu+BlVVYdYtOhKrr/+BvR6PS0tLfzjH3/H4XCyZMmSqFHKNpuNLVs+Z/v2bZSVlWGxWDGZTLjdbsrLy/nLX/7Cvffew+zZs4Pu/m3btvHqq69y1llnc/7534pawWvDho2sXr2KSy+9jNNOO63PbawnNTWFlStXsnHjRnw+LxaLBZVKRUdHO2vXfsIdd9zOvn1lzJ07Nziz8/v9vPnmm+zZs4eMjAxsNhter5eamhpeeulFHnjgPoQQLFp0FXPnzkWlUlFVVcUTTzzOpEmTueKKRf1mpsuXL+fuu39BRUUFU6dORa/X43K5WLNmDStXvsO0adP47nevGNCDo9FoSE1NY+XKd9i0aSM+n5+0tDQ0Gg12u51169bxs5/9lN27dzN79uzg/V9//XV27tzBdddd32+gcUTGXnzxn9TUVHPdddcHg/nS0tKIi4sLDl7Xrv2YgwcrWbv2Yx555BGeeeYfOBwOfD4fixcvJi8vL+iFAVi/fh2bNm0mOTmF1NRU/H4/9fX1LFu2jDvvvJ329nZmzpzJpZdeGpTbnJwcGhsbWL/+E95+ezmbN29m7959rF69igcffJAVK5bj9XpJTk7myiuvDK6FJycn43K52bRpAx999BFJSckkJycjhKClpYVXX32FW265GZMpkcmTJ+P1evnFL+7iX/96ORi8KYSgra2NpUuXsm/fXhYvXhw1K+KYIjOVJd9UNm3aKAoK8oPFJrKyMqP+u+qqq4I5yE6nUzzyyCMiJydLZGVlivT0ZFFcXCiyszOFzWYRJSXjxQMP3C+6u7uEEEJUVlaKSy65WNhsVlFQkCcWLrxQzJx5gsjMtIhFi64Iy6UeCLvdLv7whz8IiyVdZGVlitTURDF58kRhs1mEzWYVEyaMF48++kiwqExNTY2YMWO6OPHEE8KKdoSye/dukZeXKy66aGFYdbSenh7xxz8+IdLTk0VWVqZITo4XBQV5Yt++vUIIIVasWCFsNqt44IEHgsVoouWpr169Wpx44kyRlZUpzOY0UVRUIAoK8kRmpllYrRliyZIl4sCBA8F85yVL/kfk548T119/XV+VN6swm9PE1KmTRVaWta+dxeKhh34rWluP5vyWlZWJoqKCfu2I5LPPPhPnnLNA2GxWYbWaxbhxOWLSpBJhtWYIq9UsLrpoodi8eXMwl3zp0qV9eeJ/jXo9r9crHnjgfmGxZIg33ni939+WLl0qiot7q6KlpSWFvC+LyMnJErfffrtoa2sLy69+8MEHgzKXmWkREydOEOnpyeKUU04Wp512qsjOtonly5cHz1mz5oN+hYEi86d/8IMbhMWSIQoLC8S5554jzj77LJGdnSlOO+3UqHnl/d+lVzz//POipGSCyM7OFBkZKaKwME+MG5ctMjMtIjc3W/zv//5cNDU1BgvKLFq0SBQW5ocVDQrl8OHDYv78M8QJJ8zoJ/9ut1u8/PLL4sILLxAmk0YkJRlFamqiuOCCb4lnnnlaXHHFFcJqNQfzyENrMdx0043CbE4TWVmZwmrNEMXFhcJqzRCFhfli/vwzRHa2Tdx33339nqe9vV089tijYs6cWcJgQCQm6oXVmiEWL75GPPvss2L27FmitHSq2Lt3T7/z7rrrLmGzWfp0QUrfPc3CZrOKqVOniL///amgXO7atUvMn3+GsNksYtq0KeLiiy8SU6dOFhZLurj55h+HFYUaKxQh5EbIkm8m69Z9wqpVq4b8XXHxeL73ve+FzTB27drJO++sZNeuXTgcdtLTM5gxYwbz589n0qRJYWvdjY0NLFv2Jp99tpnGxkZyc3OZOnUql112GWZzbOuSHo+HTZs28emn69m5cyddXV1YLFamTJnCggULKCkpCc4i9+3bxz//+QJms5kbb7wp6rr7+vXreffdlRQXF3PNNYvDZmkOh4M1a9awd+9euru7MRj0/PSnP0Or1bJixdts3ryZ008/gwULFgwaN1BeXs4HH3zA1q2fU1dXh8lkYtKkyZx88snMnTs36Dru6OjgyisX0djYwEsv/Qu3283q1b3uU4/Hg9lsYcqUyZx++hmUlpaGzZY3b97E8uXLo7Yjmqv7/fffZ8uWLRw4UIFOp6O4uJhZs2Zz6qmnBiP9fT4fzz23lMrKSi666GJmzZrV71put5snn/wzbW1t/Nd//Xe/5Qe/38+uXbv6atFvo7m5GYvFQmlpKXPnnsyMGTP6zagdDgerV6/mo48+orx8P3l5+UyZMpkFC85h3brevOVrr72W4uLxCCFYvXoVn3yyjtmzZ7Nw4cKobW5vb+f999/nk0/WUlVVRWpqKtOnT2fhwoUUFhbFHAPS25bVbNu2nfb2NjIyzEyePIl5805lxowZwYC8xsZGnnqqd7vdW265NWpqZnV1Nf/4x99JTEziJz/5SVTvR0tLC21tbXR0dJCcnExqagpqtYZFi65g69YtvPvuKk488aSwczo7O1mz5gM+/PDDvowUPVOmTOGcc86ltbWVjz/+iPPOO5+zzz47anzGkaUQt9tNUlISGRkZNDc3s2jRFWg0Gv71r38FAzFD4yjWrl3L2rUfU1ZWht3eTVFRMVOmTOWMM86gqKgo2D4hBFVVVbz99tts3LiBjo4OiouL++rqnx1zfMdokAZd8o0lEPATCAwt3qHR6pFrmw6Hg0AggEajISEhYdAdnBwOB16vF71ej8FgGFERiUAggN1ux+/3o9VqgwFBkb85Egw1kIGL5TdH2njE/XrEUAkhgkF+seBwOOjp6UGtVhEXF99Pge/evZvvfvc75Obm8vrrbxAXF4cQgq6uLgKBwIDtHE47Io2xy+VCURTi4uKiDniOtFOtVg/4To/0zWC/EULQ3d2Nz+dDq9UOKiNHn8+Fy+VGr9cHjX60ew3nXbhcLjweDxqNmvj4hBHttBYICOz2bvx+PxqNhvj4+H73FUIEi9cMtNGIEAH8/gCKAmp17HHXW7du5bLLvk1qahrvvbdqwIC73ra6URQV8fG98ubz+YLPHaucALz11pvcfPOPOfHEk3jhhX8OmEng9XpxOp34/X6MRuOgWzAfkYkjvx2o5sJYIKPcJd9YVCo1oynMpNFooq7ZDjQoiHVXtcGfWTVkQZpYFHysBjlSKQ9HGR4hPj6+X5W1UOrr6+ns7OCEEy4JKkJFUWLq2+EMLI5gMBiGVKKxtDOWnbEURYmpgFD48/VGyg91r+G8i6GMTGyyN3RbFEUZsl8URYVGo+o3OFqx4m3Gjx9PScnEsHcaCATYt28fDz30W7xeL1deedWgs9lobdVoNP2ey+Vy8corr7BgwYJ+O9/19PTw2Wef8cgjvUF4V1yxaND+02q1w9IFw5UJadAlEsnXgq1bt6JSqZg9e86I9+iWfL3ZsuUz7rjjdpKTU5gzZw4nnTSLrKwsmpub2bZtG//+9/s0NjYwf/6ZLF68+JhsM/r228u59957eOGF55k9ezYzZ55IcnIyhw/XsmPHDt577z26u7u48sqruPDCC78R/Sxd7hKJZMzw+bzcdNNNfPTRh7z44stR16sl33x6C6v8lQ8++DdNTY1h6VuKopCRYWbhwoX84Ac3DlinYLhs376dJ598kg0bPqWzs6Of98NqzeTaa6/l6quvibnmhDToEonkPxaPx8POnTvx+31MnDgpZrel5JtHbx2HKqqqqqioqKCrq4vExCRycnIoLCxg3Li8ES35DIbf72ffvn3U1dVRWXkQp9NJamoa+fn55OfnYbNljfmGKdKgSyQSiUQiGRayUpxEIpFIJNKgSyQSiUQikQZdIpFIJBKJNOgSiUQikUikQZdIJBKJRBp0iUQikUgk0qBLJBKJRCKRBl0ikUgkEok06BKJRCKRSIMukUgkEolEGnSJRCKRSCTSoEskEolEIpEGXSKRSCQSadAlEolEIpFIgy6RSCQSiUQadIlEIpFIJNKgSyQSiUQiDbpEIpFIJBJp0CUSiUQikUiDLpFIJBKJRBp0iUQikUikQZdIJBKJRCINukQikUgkEmnQJRKJRCKRxILmy7hpm9dNvdtOmauT9h4XakVFgkZHviGBHEMiaTojakUZ8PweEeCgowN/wD/iZzCqteTHJxN6l2aPk6YeB4ijxyxGE+laQ/j9AwEqHO0IEeg9oECy1kiWIWHI+3oCfiocbWH3MGn15BgTw57FKwIcOAZtLIhPHvX7qnJ2Yvf1jPh8tUpFQVwKOpUKd8BPpaOdgBCjeyhFwWYwkaLVD/vUCkc7Hr8v7FiKzogthvd39N204w8Ewo5nGk2kRsjKUKzvqKelxxX8/3i1ltNSbOhU6pivYfd7aXLbEQzepwoKBkWFolKhV2tJ0RpQhrh2t6+HGlcX4sj7UsCsTyBDZxzyubp8PdQ4O8OOZejjMevjwo45/F6qnJ1H7zECUnVGMmN8f6G0e93UubpHJYpDvfcuXw/17m72Ojtp97oAhQSNFqsujmJjEsk6I4ZhvO8j19vlaKfb50EA8Wod4/Tx5Mclkaw1oB/G9SJlu9njpMzRTr3XSXePGxAYNXqy9fFMjE8lZRjPKxAccHbi8XmDxwxqDflxyagUZcjzy+xt+EJ0oFalpiA+BU3IuQI45OzEGaKjdGoNBXHJg9qRI5Q72ukJ0Qeh+ir0HrWubrq87pHPnhWF/PiUYb3rr7RB7/B5WNVUydKWA7zncfR1kxLSZTBZY+DyhHS+lVFAaaI5qmB2+Xq48cCnfOxxjPhZfmay8OuS09AqR1/axvZaLqreFva75fmzWJiRH6HkPEwuXwshAnS2IZG/j59HnsE05GBmyr4PIUR5/S51HLcWzg4TPoffy7UHN7DZbR9xG28zZfCHiWeO+r39q3YXd3YcHvH5J+niWDHpbMw6Iy1eN1eXf8K2UQwQjhj0jwtP5rTU7GGf+vChLfzV0RZ27Lvxqfx5/Kn9Bm/R+LjtMN+u3IQ9bLAlWJ0/hwUZeTE/R1OPizsqN7PeF6IkFBV7dWdSYkqLfcDl7uaEPR/QIwIx9Bug0rBAa+A0YwoXmAuZZEof0AAccnUxrewjCGnrdYkWfls4l7QhBlPVjnamln0Y8o3DmznTuThzQvjv3HbO2v8xDRGDrOHwkqWEK8eVDvu8HV3NzD+wPuwZh4fg3wUnc1Z6br+/OAM+3m06yKvNlfyfuzOqvrOptVwel8q5abmckpJNkkY34J2cAR//bj7ES80H+D9X5PV6rzlOrePbcSlclJ7HzGQbiYNcL3KStLOriTcaK1juaGG3zxOlTwRZah2XGlO41FzASck24tWDmxCfENxTtZWXupuDx2xqLf8qmMO8FNugvS6E4NaDG1npPjrgutSYxNMTzyQ5pF1+EeCx2p080VEXPJaoUvNG/mzOTMsZ/B4I7jy0hTdC9EGovgp9ltcayri1+cCIZXSqWsuKKeeSq48fMxt73FzujR4nP9u/nkWHd7IqaIiVCE2jsNvn4d6Ow8yuWMed+9dx0NkRdd6hQQFl5P9USnRdp4743UDCkBnxu7Webh6v3o7d7x2yLywRz64MMIrUjbaNI1ZS/ftlNM+hVY5tu1AUElFG3DpVlPu/4mxnWWMF/iFmiY09Lp6s34NdBPpdQxnmA33aVstWX09Q9kEBIXi7qYIAYlhvyBpr36FAwM/7Hge/7KjlhPK1/PrAJtoGmXlYIq7xj+5mXqzfizeWAUSkrEd5a4oCCaOUCWWEwqAoo5fFaPe2+708cnALl9fs4BV354D6rs7v44nuJi44tIUb9q5hU3tdVO9Vt9/LHyo/5+KabX3GnCgGV6HK7+Wx7ibOrNzMkn0fsamjnqHe0mG3nfvKNzCvfB0PdtaxOyiT/d/lYb+XP9qbmH9wE7eWfcyhCA9MVA9dxLutC/h4oGYH1e7uGM4N1z3qAZ4sUm93iQC/qt1BubNj2PfQKmOjB3WKMuZ29rgYdJffxy8PbuQfjpaQselQA1/BY92NPFi1FV8gwFedHuC5rgaer9+Hb7TuZMmXwh+bKyhztA8623i1YT/LXJ2j/yYCPl5pOYgrytfwUlcDDW7H8Wm0ENzfUcsvDmykO4bBaK9iEzzUfIBVLdXDHHj8Z+AXgmdqdvHL9uq+OWBsvOLu4mfVW2mJGFx5RYB/1OzinrYqlGHoln+6OvhV9TZaQ5Z0Itlnb+XG/Z/w687DeMRw9KzgKUcr3y/7mE/b64bdR6s93fyhajudo/XUDcK6Hie/q9pGm9fzHyN7x8XlvqWznlfsrWHHUlCYqDMyXp+ARwSodHdT5e+hPkRg81QafpQ9Fa1q6HGHCchV1DHP2kJd7ceKdiF4vLGciXEpzB+BK7j/JxNOPJD3JbcRwACMU1RoY3ySBEUdZg40itLr4Yg0mEBzhMJKRCFeiT7qP9bj3V2+Hv5yeDe/LZob1ZW4x97Kn1oOHpN77ehsYrW7K+rftvs8rG2rYZFt4oivn4FCiqKEyVAAcCFoFYJIFfdidxPnNFVySeb4mOSyLuDn14d3UhSXREl8yjGXsQwULMOQX/UxnP3kKwrxMc51vIh+krjP2c4TrZURcgwTtQZKDIkIBBVuOzU+D/UiwJGFhnhF4ee2yf3iE6qdXfym9SBKiE6IB6ZqjRTpE9ApCgc8Diq9LupFAG/Qi6hiibVkwHiHSlcXPzqwkX/3OPrNRFMVhSxFTZrWQADo9Lmp8vtpj9BKa3xueg59xpOaU5hqSh9WP7/Q3cDEhjJuzJoS03r6SPg/ewsT6vbwk9zSMdGH4xVVr8cxBhIVFWM9Rx9zg+4XgnfbaukKEwSF32dO5LuZEzD2rd35RIBd3a280VjOsq4GykSAe6wTmJyQHlWh+CIE6xR9PC9MPCvmDtMoqjF5wfsDPu6v2UGWIYHxcaMISBP92zhdH8/rJfPRxPjcY2XQC1VqXi6Zj00XF5sbSFFIUveueaVp9fyx6JSobsVDjjYur90e5lT7QUo2V5iLovq/hopXGAl/627grNZqLjbnhynqbr+XPx/eTVmMs9ihvom3WippHWS29XxLJReYCzHFuAYayZJEK/89bkaYBPlEgE6fh71dTfypqYKNvqNmvQt4sbmC8y2FMQdUbfS6ub9qK48Wn4xZaxy5qAuIDP1cnJTJXfknxTy/PZaBRo/ZpnLKMGIh4tXaMN30RWcjNRHBrHek5bEkd3pwoOgXgjJnOx+2VLO0rYrtfi+3JGczPy23nw7b2FFHS4iX0gjck57PDTmlwesFhKDc2cGqlkO83FbNLr+X7yfbOHeAdjj8Xu6t3MxHEcY8BYXz4pK5MXMi0xIzetsmeoN5N3fW81JjBW84Wgj1Y63zebiv8jP+OnE+acMICu0Ugocb91NkTGbBEGvdI8WO4I/NByiJS+bCjLwRTwNEn4cu8ujL409jnDExRs+WMmiMxNfCoAsEO93dYZ9lqdbAxZZCTCEfgkZRcVKShZmJZq60t7C6tZpLLcVRR94KfWvoocYLhTSNfsD16OPJhz0OnqjezgNFc0nW6Ed2EYV+M2ANKlI1hpg8FmOJGkjR6If18QaVkUrNjAFG8vFRXH65ujhmJmYct7Z5heCR+j2ckGgmty9qOoDgvZZDPNXdeEzucdDZwf91Hb2WHphvMPFeyJriux4727qaRhT0B5Cq1gafP5ITTBlMNZm5qvwTdgeOBqJt97qpdHUNa8b9qqOVaYf38JPc6SOOrFaUvnXMCDlJ1eq/FPlOVGtGJNtHNN4OVydhjmRFxTXWCWHKXKPAtIR0piakc7G5kFcaK7g6c0LUgUlnRPBvhqJwiWV8uHFQYHJCGpMS0rjUXMRz9Xu5LnvKgIP6T9pqecfZHvQOKIAJhfstxfx31hTiQj1UCmhVKs5My2VOso0ZNTu5r/kATSFa/TVPN5c1Vw7bq1QZ8PNg7Q4KjYkUxiWNyfusFQF+fXgXecZEpiakjVQdh0XWHyFZoxuFrBx7jotlcIvw0epOfw9Ob8+As7lppgx+mjcz5gjNryJ/7m5mad1eer4G6/+SCGXX4+Sl+n3Bd1fl6ubh+n3HaIALq1qrqAwxpFPVWn6bdxKlEYO/V5sq6BlF2uJgTEvM4Fvx4cqtQvhxD3O90Qv8vqWSt5sr5Xp63/v1RL4zEaC5xzmgociLS+L2/JlR014DCJyB8Oh/p4A6j33Q691TOIcsffQBnSvg5+mGMlpCZpwCuCU1h+9nTw035hHEqTVcl1vKjSlZ/b1KzQdpHWZalwA+6nHy2+qtwz53OPfY5HXzUPU2mgaJJ/gmMOYGXUEhN8IdJwJ+flm5mY3th49ZwEIwAvFLwhYxslYQPNRUwcdtNcdUzX0FHBDfAK0bPsgap6goVevCPoY/tVWxpasBrwjwUv0+NnnDFcG5UZSlLoYljpYeF8vba8N8L+clWpmSkMb1qeGpTy/am6mIIUp3pMRFzixE8D/hRiXC1Rgp660iwG/qvuCL7tYIY8SIs8G+XDFXRnGmQl7EUpQK+MWhLaxoPECjx8lwhvgqFBIjBnotCH5VtZW3Gito8jiHzMyIZL+9jU0hAwIFOFGj579sk2JautCr1NyUU8psdfiE690ee7++FXMAABcqSURBVL+6A9HIUFSoI9aTX+hu4YX6fUNmTgQzVIbAgEJ8xPf4oqONpw5/gfsYDpIVvloKecxd7ipF4VyThRe7m8LcUM8723m+Yh3n6U3Mj09joimdUlMGmYaEIdd+BaLf+nKd38urDeUxGbx5yZlk6hOOaTtPiUtmoi6Bh9qq8PSpxSYR4N6a7RTEJVE4zPV0IXrdv6E0+3t4rbEipgCgk5OsZI3BGjP0rn2903KI1Bg8KHnGJE5KNH8F51FHmajRc7V1Aotrd6CI3lXbwwE/T9TuYrHXw8NtVWG/X2IyMy/RwqrDu8KOxxKuuK6tlvdDZms2ReHijALUisK56XkkNR+gs0+ptQvBe82VlMSnHvOgIXfAT21EtH6WSoUmZBmsVw4FkdnhVyRn4exx8jd7S/DYNl8Pv636nCcmnB7M5ffFGDUdbQ19j7ub1xrLhxwMKyicl5ZLwjH05n3U1UhzDGbXqNJwdlpumBFUgBmmDNKaKmjte/oAsNrr4r3qLZxYb2RhfDpTEjM4wZRBuj6BhCFyuScmWqC5PGyg8ZHXxUfVW5lRt5sL4lIoTbIyM9GMWZ8wZG74ju5makLejQDON2XEvBYMYNHHc1myjU2th8LexsbOeqYnWQY9N1tr4O7UXO5tPOol6EHw+6YKJselcFZ67oAptxoltlmoW6XiWWsJd9Xvpa6vrQrwcOshpsSlstBcMKxASkF/fQwKK1urMHcPHT+SYzAxO8n69Q+KU4CzMvKY21zOWq874gNVeM9j5z2PHdoOUajWcaExiTNTx3Fyio30AaIzFZR+a+hbfB6uqNka05v5XHfqMTfoCYqaH2RPocxj5xVH76ccANb7PNx/6HP+UHxKTEVLQmfi2giB2+PzcGXNtph6/bPCk/sZ9EpnJ+4hineoVArF8SmD5rBXiQA31u0mlgTE35uLx8SgCwQVjo4hUxo1KhXFkWvCEQNGBYX5qdnc5Wjj123VweNvujpZW72V9r4PWQEma3T8IGsKdVFmIkOl/dj9Xt5sDR8cnGJIYkpfTEGOMZEbTGZ+39UQ/Ptz7TVcYZtI1jEsRuEK+Hm5bm9fsZOjTNboyYyQGeVI/mzIqx6nNbLAOp5dFZ/yacjg5E1XJ0XV2/l5/okYVZpej4UYesIbbQ39DVcHb1RvjUnWG5Osx9Sg39tRCx01Q/7uQl08J6dk9ZvVnpBk5bL4VJ5ytIZ7K1DY4nWzpe/66SoNF+hNLEjJZn5aDrYBBuAzksxcH5/B3x0t/f62zedhW1cDdNVjVmk4X2/irJQszk7PI3MAmamM4q4vTcgYlrFRgJMSzRhbDxHqu7LH4NJ2iwCXmwtp8br5feshnH2apE4EuLt2J1lGE5PiU6Oe2yNEzFUmv5WeR4ffyz2N5XT1hVd2CMG9dV+QazRxwjD0kkJ/fawCftiwLyY9eG9qHrMSLWMe43Vc0tbSdEYeK5jDzQc3stHrpmeALjvg9/K4vYXH7S2c27CX/82eximpOcMYScXwO2Xs1vnMOiN3586govwTPg+JIH7O0cr4w7u5LXf6l+oO/F3NDv7Z3TTob0rUOj4tvQBVTAFOSgy/GBsB9gvBbVWf8+EQLulzdXG8PPW8CK9P/2cyqNRcZ5vMp/ZWPuyL/PUA9SHuOYHCTy0TmGxK43AUgz7UGvLu7haedx6tSBUPLM4oCAaT6VVqFmbk8/uu+uAzlvu9rG+t5ju2icPqyR3uLpY17A8tSEgAQXWPk+32Ft52deAM+aMBuCY9f8jqb0d6YlJ8KndnTeX6qi3BiG4X8Nf2Worjkrk6ohrc8Zb143Fv1QDSHafWcE/BbFwVn/KqqwP3ANdvCfh5ztXBc64OTm7az83WiXzbWtzPQ5mg1nJvwSxcBzbwqrMdzwDXawq53pnNB/ihtYTzzYX9Bhz9iwgJsnTDz1LQa/TEKwquEDmq83rwBALohwjcNag03Jg9hT2uTl53tgf9IRu9Lh6s2sYfik7GrBtdsJlWUfE920R2OTt4vrsp6Gna7vPwYPU2Hiueh00fN+LrB4ajB4/TWqnqeH0apYlmXpl4No9bJjBLoyNpiE5Y1ePkvys383bTga9VsE1JQgr3ZE/rl0P7p5ZKVrYc+lJbEhAB7EP88wv/16avY2mPCMQuPXlGE7fZJmEeYMnnelMGl5gLR1SBr0cEeKc5PDd5gkbPnJSssKtNMZm5RH/U9ekCnms+iDswvLKoT7s6uLRmO5fVHv33ndod3NZUzgvOdjqECCokDfAtQyLnZRQM6x7z03K4NaMQU0gLmkWA39fvZUtnI//J2AwJPFlyBi9nl3K21kjGEAp9g6+Hmw7v5M9V26KuI2caEnhywum8kDWN07RG0pXBh8prvG6ur93BU9Xb+5UE1kYZrI9kb4WAEP2WSowqVdQqnNEGS1ZdHHePO4ETImIE/ulo5ZnDu/Ecg4DiVI2eu8bNYJ4uLqy/XnN18mTtLlx+/zdK7o5bLXcFsOjjuCF3GlfaStjZ2ci6zkbWdNVT7uuhKooQHxQB7qrdycxECzlGU8h4sn+OtkVRmKkb2i3pR2BQj12zVSiclz6O/3V2cHNTefB4gwhwX+1OHlJrY6yU11u0Ikw4FRWzdcaYZr1a1di10URvvWNDDEFgpq9ZpsKZaTl8v7OBB9vDXa6lah03ZU0ecR5pjauLv3XVhx0r1sWxvbspYu1dIVWrI3Qa9pnXyca2OuZHqRc+WuJR+JYxkYeLTo5agESIAO4BlL1OUfH9rMmUOzv4a3dTcICwy+/loaptXG0uimmSHW0NvUilYYLWMOS3ElAUVMe43sIcjY5U9dDvOVNrHHRGlKDRcXHmBBaYC/iiq5l1HfWs625kn9fFvojALAG0CcEDzQc5JdnGScmZUb+ly20lLDDns7e7hU2djazpaqDM62J/lECvViH4bUslc5JtzAq5Xv90QIUdznZOTssZVj81ubtxRMhGukY/ZAxUvKIKxjpNSkjl/uxpXFu1haaQa/2+pZKpcSlEVn0wKKphv+98YxK/yT2BKw98GmZn/txWw7S4FDwxDGai56HDfK0xWEtlMNI0huPicDruu62pUEjS6Dk1LZd5abn8yO/loLOTnZ0NvNFWwzJPd1hFpP0BHxvbasjOmhTsj2h56LN08Sybel5Mxm6s3R9aRcX3siaxzdnBUntzUCB2+r38rHobnbGY9Ch56FN1cSybcm5MBWOitTGWgUQs49V8lZpnJ5xBdgzrumPZ1bFMKsQwfSJGlYbrsiazwdHKR33rwyYUlliKKR1mJayjrjnBhy3VNEUo3WXOdt45uKnf7yOVWLMQvN58gNPSco5pRbQZah0/sYznIkvhgPUSFEVBO9iATa3lttwZ7C1fF1yqAHjD082eut0xuiP7r6F/N8nKA0Unx7QD27EOGPx15hTOsBTGps+GuLdCb+GZ2Sk2ZqXYuKWvAMzuriaWt1XztquDdnFUStsQvNtSxQlJ1gFrcCRr9MxNyWJOShZLRIAKZyf7u5t5o7WKFRHXqxcBVjQfDDPo04z9A3R3dTXhCfhjriXgF4J3O+v7LZ+ajUPnkvcQCH67KhTOSh/HrY527myuCP6mHcGPD++gK+L9+xDD/qYVYFZyJndbJ/Lj+j04+s7vRHBL3Rcxedyi56EL/l58Kvkx5M8ryvGJiP9Stk8N7aQEtZZppnSmmdK52Dqe02p2cktIeU0fsNnVwWVCDGmIVShficIy9H10d46bQVX5Oj7sMwwBYKfPM6oeU6GMWIHdapvMEn/xEH3Ym1ISiyJTfYl9rVYUfpcznd8EBq/cplHUUQtCDEaBMZGfZE6irHor9SLApQlpfMdSPOLNbjq8Hp5uOxRFsUGslaxXOtu40d7CFFNsRXZOV+uYbjAFVV+dz8Nyjz3sfvlaAwvNBUMUP1J6DcsgOrQgLom7c0qprdxMecjSwD5/zygkvVf9fRnfs0phTGT7SHsmxKcwIT6FCyxFnFVfxvfqvggbdK9zdxAQYsjBm9I3eZgYn8LE+BTOMRfwSl0ZP2vYE1ZCudrdjSvgD84kJyVmMF2lYXvIu/qLs43vdTQwJzUrprbs7G7mY0d4mqJeUTE3yTrkuZHR4lpFxfezJlHm6uDZkMyJyigBvD4hRrTNrlpRuMxazA5nO090Hq09Xz/KFDZljGTlK2/Qyx3t7LW3cn5GwYCVzkwaHd+1TuCW1sqw6Zcj8PVc5xgfl8wd2dM4dGgLhwK+Lz0SoOQ4Vlwb+8GgwuSksUuHOyd9HNd0NfBeVyM/zpoStl3jsLwIwKftdewcZdGMyoCfVS1VTDbFFo18eaKVHxbNCf5/a48L774PWe6xB+XwDXcXC+rLuD5n2qhn/vNSsrjVOZ5bG/aGBUn9p9LocbKmrZqFGYUkaKL7OPQqNQvNhcxqOcjmkGwBh9/Xz59W53Gwoe0wZ2fkDbjsY1Rp+HbmeN5oqWS59+j13EL0pRD2GvQ8QyJnJqSxPaRaoRq4r2YbfzOayBkifa25x8XD1dv7uflvSEgn0ziy7KF0rYE7cmewv2I96wcowjNakjQ6fppbSkV5d9iWrN8kjktQXIPHwc8PbuLi6m08cHATe+2tAxZDqPc4+vlSC7VxQ6e+AF+xHP/elL3UbG43F4cFDo12RCgZJSKWHGM1P8yexq9sUyiNcVasV/q7Kz0BP882VRCqovJUar5lSOTCIf7lhcRBKMCyjsPUu+0xDyRCSdMZuSOnFBEhQHc3lbOls2H0MwNF4ZrMCSxJsn0jhoujocvXw32HPuOq2p3cXPYx2zsbBtyBscvXQ0dEwKNZYwj70O1+L48c+pzLa7dxS9ladnQ2DZjj3+BxcDjCa2XWaEkIqS+gUhSuzZxEQYgnzg+82+PkR+Xr+LyjIWogsl8I9jnauLtiAy+6wrNL0hQVi60lGEcRuzM+PplfZk0lS6UeM1WebTBxZ04p49XaY3KP/7jCMh0+D7879Dmvu7tQgPvaa/hnZz2Xm8ycnZJFtjERrUqDXwQ45Ojg4fo9YecnANNMaWHuzmiFZer9XpY1VMTcvXEaHaenZh/TTR2iKzoVV2ZOYJernb91NhCrryFaYZkWfw9vNlTEPJuKU2s5PS3nmLexSwjeaz5EWoyzVpWiYn5azlenlG+M/ZdrSCDLUhRzf0eLTt7Z3cyGiHzvW9LyuTZ7ypDXe7OhnP+q3x000Ot9Hta0VnNN1qQRNXtWso0n0gv5cchaZbMIcF/VVp6PO5M0nXFU3Zqg1vLDnGl84e7mvQF2khtI1iO/izJ3N282VsTsXjXr45mbnHlM3J+fdDXSPoytRPPjU5jeF1/hFQGW1n7BM32z32ecbbxdvo6r49M4IyWb8fEpaNVaBIImt52n6vexP2QLUTVwnik9KHM9gQDP1H7Bw10NgMKzzjbeKV/L1aYMTk3KpCQ+DZ1GQ0AI6l3d/KOhjM8jNhDKi0/rpxenmNJZkp7PL5oPhOWRv+Wxs6liHf8v0co5qTmk64woikJHj5t32mp4q7uR3VE2KLorLY/SUXrMFBTOTMvhTlcnP2oogzHwaSrAKUmZ/DJzIj86vIuOGOVroMIyq1qrsHQZY1Q7CnNSbFh1cWOm2sbUoAtgeeMB/tZXKONIdxwM+PhdZx2/66wjRaXGrKhwiAC1UVzrM3RxTDWZ+734yKC4z3weLoup6Eov5+lNzEq2jrlBP+LquT13BhX71/JvjyOm0o/RCsvs9nn4Tu32mO97oT6Bk1Iyj3kbD4kA1/cZmtgkWUV1YsZXqDZ/7Ep/OK7oyFlNAMHbzZU0hCiCOJWac9LzYoqWPyt9HLOb9rMpRIG+21bDRZaiEfWlWlG42jaJT7oaedVz1OX4YY+DZ2p3cVv+SaM2iLkGE/flTufggQ3sj3V/9ShBca+5OngtpsIyvdyWZGNWspVjsUHl3R210FEbs5Z70jopaNA/ba/jty0Hw3LPm0WAx+zNPGZvJk5Rka1S40Nw0O/vZ7RsKjXzUnOC7djQWc89zRVhgcJNIsCjXY082tVIsqLCqlLjRXAgyprzVJWGC9JyowyyFW7MmUa9x8HjXQ1hgZgNIsBvOuv4TWdd3+BXGdCrpaV3Z7/rckpjKn08FFpFxTWZE9jrbOfJrrFJfVQpCpeYC/nC0c5D7TUxa4xohWVuaoh9j4ckRcUHRtOYGnTVWKvNS63FPGQuIneAl90e8FPm90Y15jmKivuyp2HRH/sOON6u63EGE3flzqBAffziEL/cPdnCO/s/caXgoLOLtzrrw1T2TQkZFMRYBjjLkMD8xPBykS+5O9nR1TTiZ0rVGbgtp5SkkO/RBTzeVs2HrdXH5Js/IdHCLzInkXAcP7IvT77C7zw3OZMnsqYxWaWJ+v05RYD9fi8Ho6yTp6LwG+tESkKqpM1OsvBM1jQKB3Bld4gA+/zeqMY8TVH4iWU84wfYYSxOreWXhXO4O3UcSYO5TwYw5umKwi9Tc7mrYBYmjfaY9WiyRs9tuTNYcIyreUZ6k27OKeUyY/KI9eRws+T1x0FOx1znJ6i13JQ7gzeK5rEk0UJhDKM4LTBHo+eZcSdyamrON8YYnJKUyS+tE0mRC+H/Efy75RD7ItZHz08bhy7G7W8V4NvpeVgj5GVZU8WoNpg4MTmT+9PDi8gcFgF+V7uTeo991O1WKwqXWwq5LSXnP+6d61RqLsssZlnJfH6eks2kGL1jpWotj9om893MkjCvkEGl4ZLMCbxTcga/SslhYoxr1NPVWh61TeGakHTfaCRqdPw0/0ReGXciF+kTiKU2mwE4RxfHs7kzuT3/pGGVtI6VfGMiP8+eRskY1tPI1Mdxe+50pn2Nd/WM5LhMF9WKwsxkK9OSzNxkb2NzZz2fdjVR4bHTIgJ0iAAGFHJUGnJ1Rs5LyeHUtJwB61erUJiki8M2CsOYpo3r556LU2u5KmRUKISIupWgWlE4RxdPIKT4RE4MQq1WFC61FFHl7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200 - +INFO:root:2025-10-17 20:55:05.736421 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:55:05] "POST /myclass/api/Get_List_Partner_Document_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:55:07.209815 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:55:07.210819 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:55:07] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:55:07] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 20:58:02.786102 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 20:58:02.787118 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 20:58:02.787118 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 20:58:02.787118 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 20:58:02.787118 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 20:58:21.744072 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:58:21] "POST /myclass/api/Add_Update_SessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:58:21.803144 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 20:58:21.804177 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:58:21] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:58:21] "POST /myclass/api/GetAllValideSessionPartner_List_filter_like/ HTTP/1.1" 200 - +INFO:root:2025-10-17 20:59:24.949799 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 20:59:24] "POST /myclass/api/Get_Given_Personnalisable_Fields_By_template_ref_interne/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:03:12.097363 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:03:12] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:03:12.144373 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:03:12] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:03:19.002538 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:03:19.004538 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:03:19.007537 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:03:19.010539 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:03:19.021537 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:03:19.027539 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:03:19] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:03:19] 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+DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE

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Entrée Scolaire:

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Promotion :

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Photo
part nom6_client   part 6
Né(e) le 03/06/2025
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGraphe
INITIATION A LA PROGRAMMATION5018.787.36 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non
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Note générale  

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RangMoy. ElValidéObservation
12.931sdfdsfds
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Imprimé le : 17/10/2025  

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[17/Oct/2025 21:04:15] "POST /myclass/api/Get_List_Partner_Document_with_filter_Admin/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:04:22.318313 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:04:22.321330 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:04:22] "POST /myclass/api/Get_Given_Partner_Document_Super_Admin/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:04:22] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:04:30.146239 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:04:30.147227 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:04:30] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:04:30] "POST /myclass/api/Get_Given_Partner_Document_Super_Admin/ HTTP/1.1" 200 - 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BULLETIN DE NOTE

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Entrée Scolaire:

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Promotion :

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Photo
part nom6_client   part 6
Né(e) le 03/06/2025
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGraphe
INITIATION A LA PROGRAMMATION5018.787.36 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non
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Note générale  

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RangMoy. ElValidéObservation
12.931sdfdsfds
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Imprimé le : 17/10/2025  

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BULLETIN DE NOTE

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Entrée Scolaire:

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Promotion :

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Photo
part nom6_client   part 6
Né(e) le 03/06/2025
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGraphe
INITIATION A LA PROGRAMMATION5018.787.36 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non
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Note générale  

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RangMoy. ElValidéObservation
12.931sdfdsfds
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Imprimé le : 17/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire:
Promotion :
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Photo
part nom6_client   part 6
Né(e) le 03/06/2025
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGraphe
INITIATION A LA PROGRAMMATION5018.787.36 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non
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Note générale  

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RangMoy. ElValidéObservation
12.931sdfdsfds
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Imprimé le : 17/10/2025  

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200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 21:08:09.710064 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 21:08:09.710064 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 21:08:09.710064 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 21:08:09.710064 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 21:08:09.710064 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 21:08:29.119483 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
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part nom6_client   part 6
Né(e) le 03/06/2025
 
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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGraphe
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Imprimé le : 17/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom6_client   part 6
Né(e) le 03/06/2025
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGraphe
INITIATION A LA PROGRAMMATION5018.787.36 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non
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Note générale  

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RangMoy. ElValidéObservation
12.931sdfdsfds
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Imprimé le : 17/10/2025  

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', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '10%', '10%', '10%', '10%', 150.0] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:11:54] "GET /myclass/api/Prepare_and_Send_Inscrit_Bulletin_Note_By_PDF/kQpidvNlrbxO_Wetuq2QtF4WMubfrYy-6g/684f106be266de5f7fd519f4 HTTP/1.1" 200 - +INFO:root:2025-10-17 21:12:57.076286 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:12:57.077286 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:12:57.081287 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:12:57.085287 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:12:57] "POST /myclass/api/Get_List_Jury/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:12:57] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:12:57] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:12:57] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:12:59.200376 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:12:59.204375 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:12:59.208376 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:12:59] "POST /myclass/api/Get_Given_Jury_With_Members/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:12:59] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class_From_Session_Id/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:12:59] "POST /myclass/api/Get_List_Jury_Soutenenace/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:10.218825 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:10.221824 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:10.227824 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:10.231824 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:10.238829 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:10.265394 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:10.267411 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:10.270394 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:10.273393 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:10.279412 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:10.285409 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/Get_Partner_All_Class_Few_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:10.292396 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/Get_List_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:10.302394 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/Get_Partner_Session_Ftion_Reduice_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/Get_List_Partner_Document_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/Get_List_Unite_Enseignement_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:10] "POST /myclass/api/Get_List_Evaluation_Planification_No_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:32.295157 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:32.297608 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:32.298608 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:32.302136 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:32] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:32] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:32] "POST /myclass/api/Get_List_Jury/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:32] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:41.531179 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:41.536178 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:41.538176 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:41] "POST /myclass/api/Get_Given_Jury_With_Members/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:41] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class_From_Session_Id/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:41] "POST /myclass/api/Get_List_Jury_Soutenenace/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:49.643138 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:49.648136 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:49.651140 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:49] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:49.661139 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:49.674143 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:49] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:49] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:49] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:49.942344 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:49.946325 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:49.951325 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:49.957331 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:49.962336 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:49] "POST /myclass/api/Get_Partner_All_Class_Few_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:49.977365 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:49] "POST /myclass/api/Get_Partner_Session_Ftion_Reduice_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:49] "POST /myclass/api/Get_List_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:49] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:49.991331 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:49] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:49.996329 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:50] "POST /myclass/api/Get_List_Partner_Document_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:50] "POST /myclass/api/Get_List_Unite_Enseignement_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:13:50] "POST /myclass/api/Get_List_Evaluation_Planification_No_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:13:59.988232 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:59.991233 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:59.992233 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:13:59.997234 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:14:00] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:14:00] "POST /myclass/api/Get_List_Jury/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:14:00] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:14:00] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:14:19.754875 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:14:19] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class_From_Session_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:14:34.632916 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:14:34] "POST /myclass/api/Add_Jury/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:14:34.682454 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:14:34] "POST /myclass/api/Get_List_Jury/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:14:37.527041 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:14:37.531039 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:14:37.534037 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:14:37] "POST /myclass/api/Get_Given_Jury_With_Members/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:14:37] "POST /myclass/api/Get_List_Jury_Soutenenace/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:14:37] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class_From_Session_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:16:50.586472 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:16:50.588478 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:16:50.592203 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:16:50.597223 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:16:50] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:16:50] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:16:50] "POST /myclass/api/Get_List_Jury/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:16:50] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:17:42.167579 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:17:42] "POST /myclass/api/Update_Jury/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:17:42.240692 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:17:42.244570 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:17:42.248567 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:17:42] "POST /myclass/api/Get_Given_Jury_With_Members/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:17:42] "POST /myclass/api/Get_List_Jury_Soutenenace/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:17:42] "POST /myclass/api/Get_List_Jury/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:17:48.612067 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:17:48] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:17:52.003893 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:17:52] "POST /myclass/api/Get_Insription_From_Session_id_Reduice_Fields_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:18:33.837830 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:33] "POST /myclass/api/Add_Update_Apprenant_To_Jury/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:18:33.931351 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:18:33.935350 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:33] "POST /myclass/api/Get_List_Jury_Soutenenace/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:33] "POST /myclass/api/Get_Given_Jury_Apprenant_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:18:41.283404 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:18:41.285404 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:41] "POST /myclass/api/Get_Given_SessionFormation_From_Id/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:41] "POST /myclass/api/Get_Given_Jury_Soutenenace/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:18:45.297204 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:18:45.301221 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:18:45.305239 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:18:45.311204 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:18:45.317204 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:18:45.322205 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:45] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:45] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:45] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:45] "POST /myclass/api/Get_List_Evaluation_Final_Note_Classement_Detail_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:45] "POST /myclass/api/Get_List_Evaluation_UE_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:18:45] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:19:50.414121 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:19:50] "POST /myclass/api/Add_Update_Inscrit_Juy_Promo_Decision/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:19:50.472122 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:19:50] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:root:2025-10-17 21:20:06.649914 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:20:06] "POST /myclass/api/Add_Update_Inscrit_Juy_Promo_Decision/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:20:06.738428 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:20:06] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:root:2025-10-17 21:20:30.421209 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:20:30] "POST /myclass/api/Add_Update_Inscrit_Juy_Promo_Decision/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:20:30.473982 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:20:30] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:root:2025-10-17 21:21:05.216601 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:21:05] "POST /myclass/api/Add_Update_Inscrit_Juy_Promo_Decision/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:21:05.273236 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:21:05] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:root:2025-10-17 21:21:12.766358 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:21:12.769357 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:21:12.773357 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:21:12.774358 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:21:12.780357 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-17 21:21:12.785357 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:21:12] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:21:12] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:21:12] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:21:12] "POST /myclass/api/Get_List_Evaluation_Final_Note_Classement_Detail_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:21:12] "POST /myclass/api/Get_List_Evaluation_UE_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:21:13] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:23:26.141611 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGraphe
INITIATION A LA PROGRAMMATION5036.397.36 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 17/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGraphe
INITIATION A LA PROGRAMMATION5036.397.36 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 17/10/2025  

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+DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +INFO:root:2025-10-17 21:38:07.337580 : Create_Bulletin_By_Inscrit_PDF -unexpected char '&' at 4148 - Line : 3095 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:38:07] "GET /myclass/api/Prepare_and_Send_Inscrit_Bulletin_Note_By_PDF/kQpidvNlrbxO_Wetuq2QtF4WMubfrYy-6g/684f106ae266de5f7fd519ea HTTP/1.1" 500 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-17 21:39:23.655470 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-17 21:39:23.655470 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-17 21:39:23.655470 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-17 21:39:23.656470 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-17 21:39:23.656470 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-17 21:39:29.532497 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:39:29] "POST /myclass/api/Get_Given_Personnalisable_Fields_By_template_ref_interne/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:39:53.731421 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:39:53] "POST /myclass/api/Update_Partner_Document_Super_Admin/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:39:53.778772 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 21:39:53] "POST /myclass/api/Get_Given_Partner_Document_Super_Admin/ HTTP/1.1" 200 - +INFO:root:2025-10-17 21:40:05.648298 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGrapheObservation
INITIATION A LA PROGRAMMATION5036.397.36 Non dddd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non dddd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non dddd
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 17/10/2025  

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom5_client_part 5_502.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGrapheObservation
INITIATION A LA PROGRAMMATION5036.397.36 Non 3
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non 1
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non 1
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 17/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGrapheObservation
INITIATION A LA PROGRAMMATION5036.397.36 Non a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 17/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGrapheObservation
INITIATION A LA PROGRAMMATION5036.397.36 Non a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 17/10/2025  

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', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:xhtml2pdf.tables:Col 7 has width 7.58828% +DEBUG:xhtml2pdf.tables:Col widths: ['34.6356%', '11.6454%', '9.46657%', '9.69196%', '10.5184%', '9.3163%', '7.51315%', '7.58828%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +INFO:werkzeug:127.0.0.1 - - [17/Oct/2025 22:11:25] "GET /myclass/api/Prepare_and_Send_Inscrit_Bulletin_Note_By_PDF/kQpidvNlrbxO_Wetuq2QtF4WMubfrYy-6g/684f106ae266de5f7fd519ea HTTP/1.1" 200 - +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 09:22:52.204901 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 09:22:52.204901 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 09:22:52.204901 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 09:22:52.205901 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 09:22:52.205901 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 09:23:49.789398 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 09:23:49.790396 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 09:23:49.790396 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 09:23:49.790396 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 09:23:49.790396 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 09:27:22.307017 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:22] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:23.303550 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:23.304550 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:23.309550 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:23.313550 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:23.321556 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:23.339063 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:23.350062 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:23.353063 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:23.356063 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:23.361070 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:23.367064 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:23.378229 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:23] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:26.962030 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:26.963030 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:26.972033 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:26.978035 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:26] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:26.982034 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:26.987063 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:26.991576 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:26] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:26.996574 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:27] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:27] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:27] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:27.013616 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:27] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:27] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:27.022584 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:27.026584 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:27.033089 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:27] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:27] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:27] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:27] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:28] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:33.077735 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:33.081258 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:33.083257 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:33.087256 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:33] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:33] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:33.097257 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:33.099263 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:33] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:33.107362 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:33] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:33] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:33] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:33] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:38.621916 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:38] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:40.837682 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:40] "POST /myclass/api/Get_Financial_Caracteristique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:41.516656 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:41.517659 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:41.522670 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:41.526225 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:41.532224 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:41] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:41.536232 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:41] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:41] "POST /myclass/api/Get_List_Survey_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:41] "POST /myclass/api/Get_List_Survey_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:41] "POST /myclass/api/Get_List_Survey_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:41] "POST /myclass/api/Get_List_Specific_Survey_Internal_Code/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:43.202300 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:43.204300 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:43.209299 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:43.212300 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:43.219301 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:27:43.230299 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:43] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:43] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:43] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:43] "POST /myclass/api/Get_List_Evaluation_Final_Note_Classement_Detail_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:43] "POST /myclass/api/Get_List_Evaluation_UE_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:43] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:27:51.593351 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UECreditRangMoy. ElMoy. Ens.ValidéGrapheObservation
INITIATION A LA PROGRAMMATION5036.397.36 Non a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 15010.00.0 Non okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210010.00.0 Non doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:xhtml2pdf.tables:Col 7 has width 7.58828% +DEBUG:xhtml2pdf.tables:Col widths: ['34.6356%', '11.6454%', '9.46657%', '9.69196%', '10.5184%', '9.3163%', '7.51315%', '7.58828%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 58 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 111 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 132 8166 +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:27:53] "GET /myclass/api/Prepare_and_Send_Inscrit_Bulletin_Note_By_PDF/6TncY6p2FiZFzzgGrjEWhhCGzqwJznbilw/684f106ae266de5f7fd519ea HTTP/1.1" 200 - +INFO:root:2025-10-18 09:29:59.541166 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:29:59.544165 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:29:59.549639 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:29:59.554641 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:29:59.563044 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:29:59.633495 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:29:59.639481 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:29:59.650479 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:29:59.660480 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:29:59.667504 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:29:59.677011 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:29:59.686021 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:29:59.696018 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:29:59.706020 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:29:59.722037 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:29:59.737019 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:29:59.755038 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:29:59] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:30:01] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:30:08.856769 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:30:08] "POST /myclass/api/Get_Personnalisable_Collection/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:30:08.859786 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:30:08] "POST /myclass/api/Get_List_Partner_Document_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:30:19.534508 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:30:19] "POST /myclass/api/Get_List_Partner_Document_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:30:20.781359 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 09:30:20.784351 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:30:20] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:30:20] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:31:06.733006 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:31:06] "POST /myclass/api/Get_Given_Personnalisable_Fields_By_template_ref_interne/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:42:08.816939 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:42:08] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:42:08.867747 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:42:08] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:42:12.921419 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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0RhuKMU/Zqs5woAbCtKhprveBAHIQQQGjEdhNo44S15MLyMfCwfVuRhT0EydN0YcEVHPLYVJKLESJ5v9wzEYLueXZKvhVCEXveRMAeA/Px8bN68qU1BVgDgt99O4bffTjcKI9we9Ho9fvzxx1Z5tIvFYjz99LN47733MHLkyGaF+Z1apkwmw7Jly7Fly1b07t2n4TeJxAzLlz+NTz/9PwwdOqxTNd/W1gnRvR3ISSAQYOLESQgPH2ky7ZUrl3DixIk29aOioiJ8++23JpdGxGIxFi5cBDc39/taPjGB3laTBsehl42hx7yCCIVqZbufq+J12F6cgoomBu/nrJ0RZPNfr1pbkRmed/ZtZFrhAeytK8dNRXmXdpRJPTzxumsgHJvMxOtBWFt0E6cr8rrNSe5mXSUO1hSj6RRmtJkVJrn4QMgO2+g0wbNnzx5kZma0fXIoFGLjxg0oLi7uUBkKCgpw8OCPrcrv6aefxcqVK9u8vUwkEmHkyJF4//0P4OPjCx8fH3z++edYvXp1qxy/7mfs7e2xbNkyk+vDHCfA9u2RyM3NbXXf++mnn5CUlGgy7ahRYzB27Nj7vi2YQG8HjhJzND2NRglCja793q+xNSU4r6oxuD7LxbeRQxcHYJyzF3ybrJcrQDhQnNqlWrKQ4/CYey8ssHOHWWMFHtm8DmtzopFW1/UBOAjAn5X5KDTyrk+5+MNFYsE6aSeRkpKCPXu+b7emePNmEg4fPtyhEKDR0dFITU0xmW7WrNl49tlnTWrlzQscDqGhofjiiw2IjPwOERFTWhURjQEMGTIU06fPMNlPUlKSceTIEeh0OpPPzMrKwu7du0xq9NbWNli0aCHs7Ozu+3ZgAr0daHjeqJBpr26qIx67i1INDkSZa2GHUCOmY0exBZ519DTIf0d1IdK7OHqZrVCM1Z4DMNnC1qDzXNCq8K+s6yju4m10PBHO1hSj6fTJSyjBKAcpBGDaeWeg1WoRGbkNJSUd07A3bfoKOTk57fs2dDqcP3/epLm7Rw8nLF68uMN7jzmOQ79+/eDl5cWOVG0DVlZWmDdvvkmnL4FAgG3bvjVp8eF5Hvv27WuVZSgiIgJDhgxljcAEevuo1KjQ1JvVHByshe3bKpGsqMBxheG+2kecvI2e7iXkODzo4gP7JuvZubweJ0symvFT7zxczSzxludABIvMDETnobpybM6L7ZT1/ObIV9chpd7wCMiJFrZwkJizDtpJXL58Gfv37+vwc8rLy7B3795WaWVNKSsra5Uz3oIFCxEQEMga7S4SHByMJ56Yb1KjLikpxt69P7QYEjYxMQH79v1gMs8ePZywcOGidltlWp7cARUV5Q2n8rX3Lz8/v83+J+2Febm3VTsnHtdrDDUWO04At3Zs3eJB+LEkDalNBOAEiSVG9fBo9j5vSzu8aNsTa+/w8tYD2FKehWluAW0Kj9vmjg4g1NYZa2XBWJ59HcX035VzFYDPy7PhZW6LJ9x6dcladrlGiXwjE4Yhtq7d7mnf9rr7e2h9tbW1iIzc1qqDSYjIpDa7Y8d2TJ48GSEhIW0uhylzOxFhxIgRXb7Hl9EyYrEYjzzyCH7++RiysjJbtILs3r0LERERGDBggMHvarUae/bsQVFRocl+9cQTT6BPnz5d861yAqxY8Qr0el2HnuPr64fffjvTJZOObhPoZdp6FKg7LwCDABxczCzuujk1X1WLPYoSg+v9Le1bfVb2nWQpa7C/qgAcGpvspzvIWlwLFnMCTHf1x9rqgkbRJG7yOpwty8Z8Wd8uNu1wmOTkhdWqGrxSnNKo9BVE+KgwCd4Wdhjp0PlepyX1ChQZrJ8T3Mys7+0Rjwjf5NyAo7DzYuALOQGe9Q2DuJPPqb969QqOHv3J4Azxpnh5eWPgwIE4cGAfWtqDXVtbi507d6J3795tOme8oCAfdXUKWFg0P1kOCuqNgIAAJlHvAXx9fTFv3jy8++6/W0ynUNRix47vEBQUBAuLxuNcVFQUDh48aFKYe3v7YPbsR7t0371QKOzwRFEg6L6JZhcJdA4v5dyAGdd5Fd1LZIadfSbAWnj3NLA6vQ4bc2OR3WTGZgbgIXt3CNvxvqfLshHbZLvbEJEEU1x8TdeJTQ88Y9UDXynKGq5pAewsy0SEqx96iLt2RigRCLBQGoQMVQ2+qCls9FuiXot3c6KwwcwS/padG0zB6Bo9ocsOPelEiY73aks69Ym9OQGepsGdOxkvK8WGDRtMCnOe57F8+XIMGzYcly5dQn5+y+cPHD16BFOnTsXo0aNbXZbS0lKIRC1/84GBgd2i/TBaI7wEeOihh/Dzzz8jKup6i2mPHTuGGTNmYNSo//aHuro67N69GzU11Sb3tS9duhRyuZxVeiNFq4vI0muRrFN32l+9TtPla8MtoeJ12J2fgA3VBYZailCM0T082mw7KFTXYUd5lsF9D9r2hIeRuO1NsRCIMMvFz3CSoFHiXHlOt9SLg8gMKzxDEWFuWN5f1XX4JCcaFdrOjX1dayTMKzgOTmLmkdxReJ7HkSNHceWK6UAeffr0xcSJk+Dj49OqEKB1dQps2vQVampa77hZV6cEZ2Ki7OHh2Satn9G1uLm5Y9GiRRCLW7ZE1dersG3bNigU/z2O+dKlizh27IjJvhQWNgSTJ0d0W1S8+16g/69AADJU1ViXcRUri5JhTDStdPGDczuOiv29PBfntapG05QAgRCzXP1bPTkYaN8Ts80ar5frAPxYkoG6Dq79tBZPCxu86dEffY2Ent1eU4xv8+I71UnO+EE4BP09HoTj70BOTg42bfrKpJlQr9fjqaeegouLMziOw4wZM+DhYTqi1p9/XsCxY8da//21ok27y+GI0XrGjh2HkSNNB5v5/fffce7cWQBAVdWtMwBMOU9yHLB8+VPsKNv7UaDzAIo0KmTXK1r9l1VfizhFOc5X5OHTzGuYm3QGaytyUGvEQjDD3AaP9AyAoI3OX1VaNXaUZRg0xmRrZwRZt/54RjuRGR4zYp7fVV+Nq910aAsHYJi9G/4h7Qf3JtqUGsDHZRk4UprZaSfS2TSz7FKkVbEvugPodDrs378fhYUFJtOOGzce48aNx+11czc3NyxfvtykZkVE+Pbbb02a529jbW1lMkpYfn4etFota8B7CDs7Oyxa9KTJbYQajRqRkZEoLS3F6dOncfbsGZP9Z8qUhzBixAhWyUbosgXpXgIRLDrRw9lGKGqXh3AdgBezrqKtxth6AHVEBpHbGmnHIjO84z0Yju1Yu71YVYCjTbZeuXIcHnX1b7Nn+IgeHphQlIyTTQTajyXpGNFDDjHX9fM2DsBUFx8kq6rwUWkmFHfUWwnxeCcvDr4Wtgi1delwXs05lZVq6u/tr43jsKdnENwknXeQjYDjIOoks2NycjK2bv26VWkXL17SaM8xx3GYPDkC33+/2+TRqTdvJuHAgR/xwgsvmJwAODk5Q6fTtWi+TU6+CY1Gw9bR7zHCw8MxderD2Lv3+xbTXblyGXv2fI9ffvnFZH+QSCRYsmQpbGxsWAV3hUAnMrayTXjPMxQDjIRIbb8pgYNlO7wFedw66rOzCROZ4VOvQejbjndU6nU4VGoYMGGUhR0G2bm2+XkuEgtMdZDiZElaYy29rhxP1pQitB3PbA8WAiGelwcjW1WL7YpS3Kkzxes1eCv7Or70D4dnB7fUuZlbw47jUN1I4+eQV19zj39uHAY7yuBzDx65Wl9fj61bv4ZSaXpnyvTpMzB0qGEgjx49euDZZ5/Ds88+Y2Jew+Hbb7di8uTJ8Pf3bzGtVCqFlZUVeL75iXViYgJSUlIwaNAgNqLfS8JFJMKCBQtw8uSvqKgob1GGfPLJxyaXV4h4LFiwEH379u22dxCLxR32cpdIJN0WpKjDAp3jjOvNPcUW8DL/35xFPW5hj1Weoehv69KuTXQxNSXYcodneoNA5ATYXZDUrmdW67V/6cl3bh/j8WNJGvrZukDUTR3KXmSGVV4DkJb6B8422bb4s6oGn+VE49++Q2HdgaNebSSWcOMEqCZ9IwvB77UlWKjXwkbIHKTayqVLl3DkyE8m092KCDbPYKvR7bFg7NhxGDduPE6f/q3F55SXl+O777bj7bf/0eLZ1dbW1vD19W9xLzrHCXDu3DmEhoayvej3GL1798YTTzyBL75Yb1IxNIVM5oE5c+Z0WzheIsLbb/8Dw4cP75gyKhB2m9MmCyzTBgaIzPCUkzcecQtAD3H74oVriceh0nSjv0XWVSCyrqJTy7y9uhCLlFXw7UatMNDSHv/2GIDF6ReR2sQj/bOqAnjlJ+IZWT9I2mkqlptbw0tsgZtqxR02IeB4fS1K1UrYWLKYzm1BoVDgq6++bDFy153aef/+oS0K/KVLl+G33061qJVwHIcdO77DlClTjWr7t3F2dkb//v1NBpfZtWsnHnroIbYf/R5DKBRi9uxHceTIT8jKyurQs5544gn4+3df+xIRpFLZ3yoCIfNybyUPmtvgcNB4LPUIbrcwB26Fed1eXdRt5c7l9ThSkt5pDmmtZZi9G95072PUj2JdSSpOdWBbnaVQhCFGHAdreT1Ol2d3+7v+3fn1119x5cplk+lcXFzxxBPzWtSoAWDgwIGYOXOWyefxPI9vv/3G4AzyRhqHSISRI0eZPNylrKwU33yzFXV1HQ9mFR8fh+zsbNYxOgkPDw8sWbKsQ7sR+vULxrRp05kF5n7X0C0BvO3iB3+L1mlt5eo6PFV00+B6oroOefW1kFm0fxlBT4QjJekope7dZrOlIhdz3YPQ08yq2/IUchxmu/ohS1WNf5VnGUwy3smNgZu5Nbza4SDGAZjg6IGvKnJQcofw5gB8VZqBSc7eXRr69n+JwsJCbN68uRWnoXGYO/cxBAUFmXymhYUF5s+fj6NHfzLpfX78+M945JFZmDRpUosTBF9fvxbDiQLA3r170LNnTzz33PMmJx3NER0djVdfXQWxWIznn38BEydONBnYhmFCaxQIEBERgUOHDuL69WttF1KiW2edy2QyVpn3u4YuBjDWQYZHXP1a9bdI3g9rHQ1jqOcQj49ybqCyA0FS8uoV2FmVj+7eNZuk1+BUaVarwvLwnRi+x1IownMewVhiZahNX9fV44PsKOS3MzxwiK0zhjbZf08AonRqHCpOY3vSW8mBAweQlJTQKi1rzpw5rdaQgoODMW/eApNroxzHYePGDSgvb95pSiaT4eGHp5l0LOJ5Hhs3bsTGjRtRU9M2B0mdToerV6/i7bffQnLyTcTHx2HlyhVYv/5zlJSUsI7SQZydnbFs2fJ23Tt06FBMmDCBnX7HNPS2I+EEWCbrhzO1pTjTZBvYr2oFduTH4wWvgW12XCMQfi5JQ3qTMK/2nADP2Uth2UkhbXkiHKkpwhVt4y1cm8rSMa2nH2xELTuUEBE685w0J7EFVnsNRGrKeZy/o0x6AD8qKyHKiQLa4QZoLRTjmZ6BOJd1DdVNpiAflaShv7UTwnvIOz3yv5Z4XKjIx0A7V9iIJH/rvp6eno6dO78zbRHhOCxevKRNGpJEIsHjjz+OI0d+QllZaYtpY2KicfToUcyfP99o5C+O4/DYY3Px88/HkJaW2uKzNBo11q//DElJiVi+/CmEhIS06JDE8zyqqqqwb98+fPnlRlRW/teHpa5OgfXrP8fVq1fx+utr0K9fPxaZrAOEh4cjImJqqyLB/dfaY4nFixfDwcGBVSAT6O2jp7k1Vkv74M+sa40iwykBbCjLxHB7KQbZ92zTM0s19dhZnm0QaW6JjQve9A2DWScF8CcCfPMTsbAgvtG2sQsaFc6X52KKq1+L9ws5rtM7hb+VA972CMXTmVeRfoeTnB7AHmVVu587zFGGCSVp2K+sbHQ9n3isybmBrebWCLRy6DShXqfXYnNuHD4uTcc8O3e87TMYtn9Toa7T6bBz5w7k5+ebHFz9/PwxderUNmtIgYGBWLhwIT755OMW7yUibNr0FcaNG9dsbG53dymWLFmKN99cY3ItVq/X45dfjuPcuXOYOHEiJk16EL17B8HVtScEAgGICDU1NcjNzcWff17A/v37kZubY/S5PM/jwoU/MG/eE1iz5g1Mnz4dlpaWbJBsB7a2tliwYAHOnTuLujpFq+6ZNGkShg+/O0FkOI5DcXERQsOCJAAAIABJREFU0tPTO+2Zjo6OXTo5YQK9GcY4eeHVqkK8W9U42loqr8enuTHYaOUAhzbEDj9dloUYXVMvYg6PuPrDojOP/OSAsc5e8ClORnITa8Dm4hSMdfKEZYvburrGrDXaUYZVymq8XpSEmjtiF3TEMG4rkuBptyCcybiIiibxEC5qVXg+9QI+8QlDSDu3F95JmUaFT7Ki8FFVPgDCF1V50GcQ3vEJ+1tq6lFRUdi1a6dJIc1xHJ555lk4O7cvpsTMmY/g4MGDyMhoeVDMz8/DgQP78eKLLxoNO8txHKZMmYITJ34xGU3sNiqVEocPH8JPPx2GSCSCubk5HB0doVSqUF1dDb1e1wrfgVtUV1dhzZrVuHEjCq+/vgY9evRgg2Q7GDhwIB599FF8++03Jvuera0dnnxy8V2bQHEch7fffqvTrDJEwLp1n2LGjBldVmZmP2oGM4EQS2T9MExkZiAMvldVYV9BEvhWiqNqnQbfl2agrkn6+Zb27QpMYwpniSWecjDUdH6vr8W1qqK7Up9iToD57kF42l6KztqRyQEY7SjFuy4BBi3BA/hNU4elaX/idFk26tsZS17D3zKxP3vzLD6qymuYgqgBbKjKx/sZV6DQ/73CjtbV1WH79kjU15uOrDd06HA88MAD7V6/lMvlmD9/fqvu3759O5KSkpr93d7eHmvWvIGgoN5tHEgJWq0WtbW1yM7ORmlpCTQadauF+Z3PsbOzYxHpOjKumplhzpy5kEpNL988/vjj6Nev310tr16vh1ar7aQ/TZeXlwn0FvCytMNb0r6wbCLSOQCfl6Yjprp1zjIXK/NxSmPo/DXXxQ82os4POCDkOEzr6Q+XJjPLKgB7SlKh4fV3pT6thCI85xGCKZ14nKqIE2CerA9W2bkZ/f26To0HMi7jzZQ/EFdT2upDYlS8HimKCnyYcRnh6Rew30gUOi2AWr0WujbsWrgX3HouXryIQ4cOtkKA8XjmmWcahXhtDw899DD69DEd3au8vAzffvst1OrmHU979eqFd95ZC0fH7tWQiQhTpz6E55573mR8ckbLBAYG4rHHHmsxjUwmw5w5c9gOg7aOh6wKWma8szeWV+bh/2qK//txA0jkdfg8NxbrrUa3uI5ap9dhb3EalE22V0VIrDDEwb3Lyi23sMXTtm5Y22TJ4KiiDE8ryhHcCTHV21UuM2v8y3MgclP/wDVd5xyrai0UY4XXQBSmXsCuJuvpt1vs05oi7FKUYpalA8LtpRhg6wJzkQSWQjEshUKoeR4qvQ4KrQpRNaU4W5mHSGUl1H8Ja2O2mNfspVjjPRj2RhwNtc0I+cNlWXCuLu6SunU2s8TEHh4tpqmsrMTWrVtb5a0+bdoMDBkypMPlcnFxwZIlS7Fq1QqTWvGxY0cxffqMZk/q4jgOYWFh+PjjT/DWW2+26iCZjsLzPGbOfARvv/02c87qDIVDKMSMGTNx/PhxJCYmGKlvPZYsWQovL29WWUygd7KJSCDEsx79cT7pDK7rG5tMtisrMKrwJhbK+jV7oEpibSl+UFU1ES/AbCdv9BB3nelOzAkw1cXXQKDnEuHHolT064R15fbS16YH3pKH4Jns6yjsJGuBm5kV1vmHQ559Ax9WGT/Jq5jXY6OiDBsVZbAVCOEjEMFdIIQ9J0Qd8SgjPa7rNKhvhcb9vpMPnvfo36yFxeiBOMTjleKULqvX52x7tijQeZ7H8ePH8ccf502uCwoEHBYsWGg0xGt7mDRpEvbsGYzLl1s+Z12pVCIychsGDBjQrCYsEAgwfvx4ODk54R//eBsxMdFd9x2JxViwYCFeeOFFODo6sgGxsyb2cjkWLVqEN95YY3BcamjoQEyd+hALItMOmMm9Ffha2uNVtyDYGjG9v1ucglSF8T20WuKxvzgNyiYCYoTIDOOcPLq83H1snLHU0nAQ+qGmECmKirvY6ThEOHniTRd/dKa7i6uZJV73GYwNroFw4wQtTlhqeD2idWr8rFFit7oWhzV1uKCtNynMewnF2O8xAC97hnbJcklHMDVBKygowMaNG0wKcyLC44/PR0hISKeVzdraGs8993yrYlqfOnUSp06dNDHhEKB///744osvMGPGzC4xzbq5ueOTTz7FqlWvMmHe2X2V4zBx4iQMHhxm0PeWLVsGV1dXVklMoHfdQPlwTz8ssnGBsImmncnr8Un2Daj0OoP7UhQV2FFraF59xEHeLZHMLIUizHU1PM0qidfjRFnmXa1TMSfAPPcgLLbr3GUHO5EET3uEYL/fCCy0doJrJwWjkHECvGjnhoO9xmBmT39YCP9exi29Xo+DB39Ebq7pkLv29g54/PHHOv0QjGHDhv11hjpMTii2bt2K4uJik0LBy8sb7733PjZt2oIRI8I75RAMe3sHLFiwELt27cb06dPZmnkX4ejoiMWLl9zhZEiYNOlBjB49hlUOE+jNDA6d9BwLgQjPe4Sgt5EtX7uUFThUlNIoxhoPwvHSTAOTcm+BCBEu3bc2NNDBDbPMbQ2u76/IRV694q62jZ1IglWe/THF3KZT20zIcRju4I5NvcZgl/cQPGZhD6927vPvLRDhFTs3HPAPx8cB4ehl5Qjub9jX09LSsGXLllvHHbfwx/N6LFiwsM2e5K3BzMwMy5cvh0QiMVmOqKhrOHLkp1adwmVtbY0JEyZg06bN+Oyz9Rg9egxsbe1aHTuciCAUChEY2AvPP/8Cdu/ejbff/gf8/Py6NDrZrfrmm/2jLoh2SIQW8+xIvPX2EB4ejgkTJt5SQCytsGDBQtjZdd/hSqbqorP/upoOqxm2IjMscZA3GkwIBEdx92/tcBSbY4W91EgZO2efsK+VPd6Th+BsVaHBbzGqakzUqhvWxau1GlQQb1AeuZk1/Cy7z7HGXmSGBa4BkFcXNhJEBCBNWQWZuXXjwVEkwQJHeaMDTngQLLtor7WnuQ0+8h6CvnesLXMAHDqh/5gJhBjv5IlwRxmSFRW4VF2IazUlSNHUIUWvRSHPAw2BeDmAE6CPQIheQglk5jYYZ++OXjbO8LO0g6CNA7uVxByrHeXoTr92XwvbZgVHcXExli9/yrTlRCzCjBkzu2z9sn//UHzwwUcoKjK9fVIgEKKurg7W1tamrWgcBzs7Ozz00EOYOHEi0tLSEBcXhytXrqCgIB95eXmoqalGfX09hEIhrKys4eTkDC8vT3h7e2PkyFHw8/ODs7Nzt4QYFYlEeOONNw3Wjxu/vwBOTk6dluft6H3Tpk1r2Rolk3VbRDwrKyssXrwEp0//hhkzZiIsLKzbvpeIiAgMGzasW2VUr169uvT5HBELes24f9AToUSrQq1WDZ1OC9xeM+cE4AQCmIskcJJYsDPV/4dQKBSorKyERqOBXq8Hx3EQiUSwtLSEk5MTc766y+h0Oqxfvx4TJ05A3779WIUwgc5gMBiMvyu1tbWwsrJisfKZQGcwGAwGg8GmQwwGg8FgMIHOYDAYDAaDCXQGg8FgMBhMoDMYDAaDwWACncFgMBgMJtAZDAaDwWAwgc5gMBgMBoMJdAaDwWAwGEygMxgMBoPBBDqDwWAwGAwm0BkMBoPBYDCBzmAwGAwGwxARqwLG/QARQaVSged5mJmZQSxmx6MyGIz2odfrUV9fDwAwNze/Z47gZRo6475AoVDgrbfewpNPLsKNGzdYhTAYjHbB8zyOH/8ZTz/9FNat+6RBsDOBzmB0EzU1Nfj552O4cSMK7u5urELu0DTy8/PATlFmMFpHQkIC/vnPf0Kr1WHRoidhZWXFBDqD0Z2kp6ehvLwUwcEhsLOzZxUCIDn5Jj788ANs3rwZPM+zCmEwTFBYWIj3338PcrkcH3zwATw8PO6p8jGBzrgvyM7OgZmZOQYOHAQLCwtWIQAOHTqEr776EsHBwRAI2FDAYLREfX09Nm3ahIqKCnz00Ufw9va+58rInOIY//Oo1WrEx8eBiODv7w+RqPXdnogamaNNCb4707dXSLYlzzvTcRzX6F6O48BxnNH0CoUCN2/ehFgshlQqa/Tb7XuM/Z+IjD73Tg3f2O/tLa+xe1tTnq56bnO09f1bWz9tKWNb3rm179KZfa8tfd1UPbVU562pY2PlNHWfXq/HrFmzsHjxYsjl8ja9W3dNmJlAZ9wHAr0eUVFRUKvr4ePj0+pBLTs7G9euXcOVK5dRUVEBBwdHjB49GsOGDYOTk5NB+sTEBJw+fQapqSkQiyUYP34cRo8eA2tr61blqdfrkZ6ejtjY2L/yrISjowPGjBmLsLAwgzwBYNu2bSgpKcaMGTNRX3/rPePj4/6yRgzE2LFj4ejo2JD+woULOH/+HOrqlLh27Rp4nsePPx7AmTOnAQDTpk1Dnz59UVZWhm+//QZCoRDLly9HYmISzpw5jczMTDz77HMICQkBEaGkpARxcXE4f/48iouLYW5uhpCQEISHj4S/v7/B4BgVdR3nzp3HgAGh8PHxxcmTJxEdHQ2VSgl/f3+MHj0GgwcPNvAa1ul02LRpE+rqFJg9+1FoNBqcPXsGsbGxePDBB/Hww9MAAEqlEjdu3MCFCxeQnp4OgYBD7959MGrUKPTp08dgMpebm4vvvtsOHx9fPPDAA7h27RpiYmKQn58HPz8/hIeHIySkv9FJIM/zKCwsxLVr1xAVdR25ubmwsrLGyJEjER4eDjc3t0bvX1paiu+++w48r8eyZcthb2+49BMfH4ejR4/C3d0dCxYsBMdxqK6uxtdfbwERYdmyZUhPT8eZM2eQlpaGxYsXY/DgMOTkZOP48V8QHx8HgEN4eDjGjx8PZ2fnVk8i8/Pzcf36NVy9eg1FRUWwtrbCyJGjEBYWBplMZjDBOH78Z0RHR2PKlCmwsbHFpUuXEB8fB41Gg5CQ/hgzZozBfXdOsuPi4nD16lUkJyejqqoK7u5ueOCBCRg8eDB++ukwSkpKMXXqVPj7+zfKNy8vr6GcxcVFsLa2xogR4Rg9ejREIhG+++47mJubY8GCBbC0tGy4T6FQIDo6GteuXUVmZiYUCgV8fHwxadIk9OnTG9u2RYKIx9y5jzX61nQ6HZKTkxEVFYUbN26gsrIC7u5SjB49GuHh4Q153Eaj0SAq6jr++OMPJCenQCp1x/jxD2DYsGFtUibaqw0wGP/TpKSkkFTqRoMHD6KSkhKT6Wtra2nz5s00YEAoeXjIKCDAn0JDQ8jDQ0YeHjKaNGkixcREN6TneZ6OHz9OgYEB5OPjTWFhgykgwI+8vDzotddeo6qqKpN5VldX0+bNm6hfv74kk7lTYGAAhYQEk6ennDw95TR16lSKj49vdE9lZSVNnTqFAgL86MknF1FAgD/5+nqTn58vSaVu5OEho2nTHqasrKyGe/bu3Uu+vt7k5eVJcrmUPD3l5OfnQ35+PhQWNohSUlKIiOj69etkZ2dBU6dG0CuvvEy+vt7k5uZKvXsHUWpqCmm1Wjp16hRNmjSRPDxk5O3tSaGh/cnHx5tkMnfq3TuIjhz5ifR6fUPeOp2O1q1bRzKZO82YMZ3CwgaRXC6lwMAAksulJJO5k5+fL3300UekUCgavWtWVhYNHTqEQkP707/+9U8KCupF7u6u5OhoS6dOnSSe5ykhIYFmz55F3t5e5OXlQSEhwdSrVwDJZO4UEOBPn376KdXX1zd67qFDh8jV1Ynmzp1LU6ZEkIeHjAID/cnDQ0YymTv5+/vS5s2bSaPRNLpPpVLRd99tp8GDB5JcLiUfHy8KDu5L3t636nX06FF06dIl4nm+4Z7Y2Fjy9/elmTNnGDzvNl999SXJZO60cePGhnsTExPJxkZCEydOoFWrVpKfnw+5ublSYKA/xcXF0qVLl2jQoIHk6Smn/v1DqG/fPuThIaO5c+dQbm6uyb6nUqlo//79NHJkOMnlUvL19aaQkGDy8vIguVxKI0YMp7Nnz9Adr0I8z9Py5cvI01NOS5YspuDgfuTl5UEBAX4kk7mTXC6l4cOH0dWrVxvlxfM8ZWdn0TPPPEN+fj4klbqRp6ec/P1v9VkvLw+aO3cOjRgxnAIDAyg7O7tROX/4YS+Fh48guVxKHh5SCgrqRV5eHiSTudPYsWNo7dq15OrqTMuXL6O6ujoiItLr9XT9+jWaNu1h8vLyJKnUjXx8vBry9/f3pWXLlpK3txfNmDGdysvLG/IsLS2ld9/9N/Xu3euv79KfgoP7NXw7Tz75ZKPvS6vV0ubNm8nPz4f8/f0oLGwweXl5kI+PF61bt460Wm2XjnVMoDP+5zlx4gS5u/ekpUuXGAiKptTV1dHatWtJLpdSRMRk+u677yguLo6Sk5PpwoULtHz5cpJK3WjatGlUXV1NREQVFRU0YEAo9evXh44fP07p6el05cqVv4TWYDp79qxJYf7GG2+QTOZO06Y9TPv376f4+Hi6efMm/f777zR//nySy6U0f/78hjyJiJKSkqhv3z4kl0tp/PhxFBkZSbGxMRQbG0s//LCXhg8fTnK5lNaseZ10Oh0RERUUFFBcXBz961//JKnUjd566y2Kjo6m2NhYSkhIaBB427dHkru7K8lk7jRp0gT6+ustdOnSJfr999+pqqqKDh48SAEB/jRo0EBat24d3bgRRSkpKXTjxg368MMPycvLgwIDAygmJqahvBqNhubMeZTkcin5+/vRq6+uovPnz1N8fBxFRUXRhx9+QIGBAeTu3pP27t3bqI4uX75M3t5eJJdLaejQIfTRRx/RH3/8QefPn6eysjKKioqioUOHkJ+fD61cuZIuXvyTbt68SfHx8bR161YaNGggeXl50KFDBxsJ2c8//4xkMnfy8JDRiy++QOfOnaP4+Di6evUqrVmzhmQyd/L0lNOVK5cb7qmvr6fPP/+M5HIpjRo1krZs2ULR0dGUlJRE165doxUrVpCnpwc98MD4RgJ1z5495O7ek955551GE507hd0rr7xM3t6edP78uYbr+/fvJze3W20xbtxY+vLLL+nixYv0+++/U35+Ps2dO4fkcint2rWTkpOTKTExkZYvX0aBgf4UGRnZYt+rr6+nDRs2kIeHjEaODKetW7dSTEw03bx5k6KiomjNmtcb8s3JyWm4r6qqiiZOnEByuZQGDhxAX3zxBV2/fp3i4uLoxIkTNHPmDJLLpTRz5kyqrKxsuC8jI4MiIiaTVOpGDz44iXbu3EFXr16luLhYOnfuHL344ovk4SEjuVxKY8eOaeiPKpWKPv10HXl4yCgoKJA++OB9unTpEiUkJFBUVBT93/99SgMG9G+YGH711ZcN7Xzp0kUKDu5HMpk7zZ8/nw4ePEg3btyg2NhYOnr0KD3++OMkl0tJLpfS6tWvNdxXUlJCS5YsJrlcSnPmPEpHjhyhuLg4unnzJp09e5Zmz55FMpk7rVy5oqGcSUlJJJO508SJE+j8+XOUnp5OJ0/+SoMHD6LRo0dRXFwcE+gMRnvR6/W0adMmkkrd6LPPPjOZft++fSSVutHcuXMazbxvU15eTk888QTJ5TLat+8H4nmeoqOjycNDRkuWLG40YUhMTKSUlBSjg/edg/jWrV+TXC6lBQsWNNJI7tQSpk6dQq6uznT48OGG67/+emuiMnPmDKMDxZEjP5G/vy8NGjSQioqK7tCUtfTPf/6DpFI3OnbsmMF9Op2O1q5dSzKZOz399NONBvJbmmYM9enTm8LCBtOZM6cbCcjbgvv9998jqdSNVq9e3TDYFRcXN2iQBw4cIJVKZZDv999/Tx4eMho/fhwVFBQ0/BYZuY1kMnd68MFJFB0d3ahOi4qKaPr0aeTj4027d+8itVpt8E6HDx8iLy8PmjDhgQaLiUKhoMWLnyR3d1fauHGjwWSvtraWli9fRjKZO23Zspl4niee5+nEiRPk4+NNERGTKS4u1iAvhUJBS5cuJXf3nrR58+aG6++88w7J5VI6evSo0b5QUlJCERGTqU+foAZLiV6vp//7v09JJnOnJUsWU0ZGRqP6rq6uosBAf+rXr2+jNs7JyaHo6OhmLQG3+95vv51qsDrduHHD6Lu89tqrJJW60fr16xuux8fHU9++fWjo0CF04cIFgz4QFxdHAwcOoMBAf4qKiiIiIqVSSS+//BLJZO60evVrlJeXZ5CfWq2mf/97LUmlbvTmm2+QVqslnufp119/JblcSmFhg+nkyZMGbazX6+nChQvUr18fcnbuQadOnSIiosLCwgYr0rp164xayyoqKmju3Lnk7t6Tdu3a1fC8zz//jKRSN3r55ZeorKzM4L60tDQaM2Y0+fh40aVLF4mI6JdffiGp1I0++OD9RmmjoqIoOzu7xbGgM2CurYz/aTQaDa5duwatVgMfn5a9UsvLyxEZuQ1isRivvLICnp6eBmkcHR0xZsxo6PU6XL58BTzPw83NDebm5rh+/ToSExMbnHWCgoLg7+/fokNMSUkJvvnmGzg5OWPVqlVGt8E4OTnh0UfnQCKRICYmuuF6TEwMBAIBHn/8CfTt29fgvgEDBkIu94BSqURZWWnD9ZqaWsTExAAgo/nV1NTg2rWr4Hkezz//fCMHII1Gg71796KiohzPP/88xowZa7BGKhaLMXPmI6isLMTJkyegVqsBAJmZGaipqUZYWBgiIiJgbm7e6D6hUIjJkyfDy8sbMTE3EBsb2/BbQkICRCIRXnzxJYSEhDSq099+O4XLly/h0UcfxSOPzIJEIjF4p2HDhsPfPxD5+fnIyclpWG+/dOkSevRwwpw5cwz2E1tbW2P06NHgeR75+QXQ6XRQq9XYuvVrCAQCrFy5En379jPIy8rKCrNnz4JIJEJ8fDx0Oh1qamqQnJwMsVjSbByEysoKZGVlQSaTQyaTNZTx4sWL0Om0ePHFl+Dt7d2ovgUCIfz8/FFSUoQLFy5Ar9cDAORyOUJCQlqMiKjRaPDNN9+A4zi88sor6N+/v9F3mThx4l/r+/FQKusAAEVFRaiursIDD9xaG27aB273faVS2RB4JS4uDt9/vwseHp545plnIZVKDfKTSCSwt3cAAISGhkIkEkGpVGLjxg3geR4vvPAixo0bZ9DGAoEAffv2hYODI4RCAXr16gUA+OOP35GUlIixY8dh2bJlsLOzM8jT1tYWYrHoL3+L3gCArKwsREZGwsnJGa+88gp69OhhcJ+vry9GjBgBrVaLxMREEBEcHBzA8zx+//13pKenN6QNDQ2Fh4dHlzvHMac4xv809fX1iI6+AUtLKwQG9moxbVpaGi5e/AO2tvbYt28fjh49YiQVh5SUZAgEAiQl3RLePXr0wBtvvIk1a1bj2WefwcqVKxERMQW2trYmy5eSkoKiokJYWVljx47vDITcbW4PDpWVldDptOB5QlpaOiQSCWQyqdF7BAIOAoEAYrEYFhaWjQR2XFwsvL19jXrr1tTUIDY2Br169WoQLLdRKGpx7NgxCAQCnDt3HqmpaQAMg9IoFHXo0UOK/Pw8qFQq2NraIj+/ABqNBoGBgc2+p6WlJYYPH4HU1BQUFRUBAKqqqpCamgorKyuDSZZWq8Xly5chEomQkZGJd9/9d7NOjqWlJairq0NtbS0AIDs7G0VFBZg5cxYsLMybqUMhAIKdnR2EQiGSk5ORmJgIgHDo0CGcPXvW6H35+fkgArKzs1BdXYW6OiVSUpLh4OBgdKJ4u40rK8sxZcqUBoGlUCgQExONwMAgowLQysoKzz33PJYvX4o1a15HYWEh5syZY9SBsinZ2dmIiooCxwlw7NjPuHDhgtF0hYVF4DgOCQnxqKtTwtLSCteuXf1r0jjAqNPb7X4nkUgaHMESExMgEokxe/ajzXqJ19fXIzk5GRzHoWdPt7/uS0RsbCyGDBmKyZMnNysUq6urkZSUgCFDhjU4qp05cwYcx2HWrNmwsbExel9tbS3S0tIgk3k0OCpevXoVhYX5cHR0wqZNmyESCY2OBdeuXWuYrBARgoODsXjxEkRGbsOzzz6Dl156GePHj4eZmRnzcmcwOkpWVhZKSorRu3cfODg4tJg2MzMTZmYW4HkeBw7sazGtWCyBg4MjOI6DUCjEo4/OgUgkxtq1/8Krr67CyZMn8eqrryEwMLDFrTfZ2dl/aXDV2L+/5TxFIjGEQhE4ToCysmKkpqbA2toGMpnxwbGmphbFxUVwcXGFi4tLo0lETU01pk59yOhAk5KSAqWyDqGhAwwEb1lZOYqKCiAWS3D69KlWlFcIiUQCnudx+fIl8DyPgQMHmahbMQBqGETLysqQnZ0NV9eeBoKgqqqqYbJz5colXL16uYUnc5BIxBAIuIb2Njc3R0hI/0YTnjs9qktKigFwkEqlEAgEqK6uhkJRC57nm5nw3fn+IlhaWkIkEiMvLxd5eTl48MEIWFsbFyxJSTchFIowaNDgBqGVnp6G6upqTJr0oIE3NXBri9WECROwefPXWLv2Hbz//rs4ffo3vPzyywgPH9li3ysqKkJ9veqOdyET/d0BQqEQGo0GyckpsLCwaHZyUlFRgaKiYtjZ2cPBwQFarRZZWdkg4iGTyZoVyrW1tUhMTICFhQV8fX0bnqXTaREQEGBUU75NamoKRCIxBg0aBFtbW9TV1aGwsBDm5uaQSt2bvS8nJwdVVZUYNGgw3NxuTSIKCvIhEolRV6fAnj27Tfbz2+UyNzfHypWrYG/vgA0b1uOpp5ZizpzHsGLFSri7uzOBzmB0hIKCAhAR+vbta3SbUFNtj+M4PPLIbKxevdqo6bbpYHp7e5WFhQUee+wx9O/fH5s3b8Lhw4eRkZGBr7/+Gn5+/kbvJyKo1bfMkXPnPo7XX3/d5PsIhUIIhUKUl5cjIyMdAwcOanZrUmZmJioqyjF27LhGwXSysrIgFkswYMAAo5pydnY2RCIxgoODDQS+Wq0Gx3Hw8/PH119/DUfHHibLbGdnB51Oh5iYGNjbO7Q4sOl0OkRH34BOp4OX160lkry8PJSWFhvdIqTT6aBQKCASibB79x7RMj2ZAAAgAElEQVQEBgaaLI+lpeVfZtIE6PV6eHp6GhV8t7bARUOjUTcsTRAReJ7HkCFD8dVXm0weysFxHGxsbJCamgqOEyAkpL/Re9RqNdLT0yEUiuDhIW8oT25uLgQCAYKCgpoNiCQSiTB58mQEBwdj584d+Prrr/HKK69g/fr1GD58RLNl43k9iAijRo3BJ5980qr+bmNjg8LCQqSnp8HOzh5ubsbbsqSkBEVFhfD29oZUKgXHcTAzk0AgEECpVDabR0VFBWJibmDSpMkN5vHb6VuqayJCTk4OOI6DTCaDUCiEWCyGWCyGVquFRqNp9t68vDzU1dXB09Ozob/rdDoAwGuvrcbcuY+Z3FMvFAobJikODg54+eWXMXJkOD799FP88MNeZGVlYdOmza2ynDCBzmAYQa/XIyYmBjyvh6+vr8kT1pycekCn0+LmzSRYWlo2a6IzZs4VCAQQCATo06cPPvjgQ7i5uePLLzdg586d+Mc//mlUI+E4Dl5et9ZECwryYWVlZXJQvU1iYiLU6noMGDCw2YEuMTEBHCdopPHV19cjISEBOp3WqHalUqkQExMNrVYDDw/D393d3WFuboEbN66CCCatHv8dNHNRUlICb29v9OzZs9lB+fz584iLi8WQIcMQEBAAAEhIiAeARu9x52TB398fmZkZqKysbHV5FAoFrl+/DqFQ2OwkQKVSITo6Cu7u0ob4BRYWFjA3t0BVVRV0Ol2rB+jy8goIhUK4uroaba+KinLEx8fBwsK8YQKo0Whw48attvD29mm2790OjiKTyfDqq6/By8sbb7zxOj7//HMMHhzWbL93dHSERCJBamoKVCoVXF1dW/ku5cjPz0NY2JBmJ5O3td6goAiYmZlBIBDAyckZt5asUqBWq41ah65fvw6JRIJRo0Y1TDatrKz++kYKoFQqjVoqNBoNrl+/jvp6JYKCggDcWo93de0JrVaLmJhYhIUNMRDMer0eFy/+CZ7nERIS0nDd1tYORDxSU9NgZWXVapO5Xq+HUCiESCRCWNgQfPnlV1izZg1OnDiOkyd/xWOPPd6lYx5zimP8Twv0qKjr0Ot5BAX1Npk+NHQA/P0DER8fh2PHjkGr1RrVAhQKRcP/4+Li8OWXG1FSUtKQzsrKChERERCLxUhISGjx4JOAgAC4urri/PlzOHHiF4OY6jzPIyMjw+BEp4yMW9pcSEiIUQGh0WiQnp4OkUgEX1+fhoFMrVYjLS0VSmV1wxplU00xKioKjo494OfnZ/C7lZUVpk+fCWtrO2zfHom6ujqjA35hYWET824xqqqqoFarUVtba1AnPM8jOTkZ//nPR9Dr9Zg79zE4OztDp9MhLS0NIpEYvr6+BgOypaUlBg0aDL1ej02bvmpYd2/6Tv/f3nlHx1Vdi/u700fSqGtmNCpWteUqG4MLmGLAlICBAAmmOLz3CxB4TkKABMgjgVASQhJqEpKQAAYC5NEMxhhsggFj44JxxUWWbFnF6l1TNeX8/pA8nhmNpJFkmZLzrWXW4mpuOefuu/c5++y9T2VlZdg929ra+OKLnUyePDWs8E4ohw5VcvhwLdOnzwgakaKiIoqKiigr28fy5cuDM7lIV3ZbW1vYseTkZAIBP9u3b+83W/T5fGzYsJEDB8qZPHlKsBCR1+tl69bPSU5OCSuuckT2amtrWbp0aVjwlVqtZt68U8jMtNHQ0NjvOUIpLh7PxImTqK2t4a233ooq7w0NDTQ3N/cbKLpcLkpLpw/oOj8S0Dhz5szgOzvttNNIT8/gueeeZfny5djtdrxeL16vF7vdzooVK3j00UdQFBVW69GiPIWFhZhMibz//mo2bdrYT3b8fj979uzhk08+ITc3n3Hj8oJ/u+SSSzAa43j66adZu3Ytbrc7OGNvbW3l6aef5rXXXiM+PoHi4vHB8+bOnYtWq2P58rf4/PPPo8prZWVl8LvsHRhs4E9/+mPQo6AoCmlpaVxxxRUEAoHgOvtYov7Vr371K6n6Jd9EmpubeeKJxxGi16DX19ezf//+fv8qKyuxWq2kpKRgMBh4//3VrF+/Ho1Gg9VqRQhBe3s7q1at4qc/vZWGhkbmzJmDoij85S9/6fuIXZSUlKAoCnZ7NytXvsvatR9z8cWXMG/evAFddiaTCY1Gy4cfrmHdunXExcWTkpIMKLS2trJixdvcdtutOBxOZs6ciUajoauri6VLn6WmppobbrghqmFubm7iqaeewu128z//syTovuzp6WHZsmW0tbWTk5NLTk4OXq8XlUqFRqOhoqKcJ554jFmzZnP55d/p55LXaDRkZGTw7rsr2bRpIx0dHVgsFnQ6HXa7nc8++4z777+fd99dybx584JejhUr3mbt2o9pbW1h48aNpKSkBF3xTU1NrFy5kjvu+BlVVYdYtOhKrr/+BvR6PS0tLfzjH3/H4XCyZMmSqFHKNpuNLVs+Z/v2bZSVlWGxWDGZTLjdbsrLy/nLX/7Cvffew+zZs4Pu/m3btvHqq69y1llnc/7534pawWvDho2sXr2KSy+9jNNOO63PbawnNTWFlStXsnHjRnw+LxaLBZVKRUdHO2vXfsIdd9zOvn1lzJ07Nziz8/v9vPnmm+zZs4eMjAxsNhter5eamhpeeulFHnjgPoQQLFp0FXPnzkWlUlFVVcUTTzzOpEmTueKKRf1mpsuXL+fuu39BRUUFU6dORa/X43K5WLNmDStXvsO0adP47nevGNCDo9FoSE1NY+XKd9i0aSM+n5+0tDQ0Gg12u51169bxs5/9lN27dzN79uzg/V9//XV27tzBdddd32+gcUTGXnzxn9TUVHPdddcHg/nS0tKIi4sLDl7Xrv2YgwcrWbv2Yx555BGeeeYfOBwOfD4fixcvJi8vL+iFAVi/fh2bNm0mOTmF1NRU/H4/9fX1LFu2jDvvvJ329nZmzpzJpZdeGpTbnJwcGhsbWL/+E95+ezmbN29m7959rF69igcffJAVK5bj9XpJTk7myiuvDK6FJycn43K52bRpAx999BFJSckkJycjhKClpYVXX32FW265GZMpkcmTJ+P1evnFL+7iX/96ORi8KYSgra2NpUuXsm/fXhYvXhw1K+KYIjOVJd9UNm3aKAoK8oPFJrKyMqP+u+qqq4I5yE6nUzzyyCMiJydLZGVlivT0ZFFcXCiyszOFzWYRJSXjxQMP3C+6u7uEEEJUVlaKSy65WNhsVlFQkCcWLrxQzJx5gsjMtIhFi64Iy6UeCLvdLv7whz8IiyVdZGVlitTURDF58kRhs1mEzWYVEyaMF48++kiwqExNTY2YMWO6OPHEE8KKdoSye/dukZeXKy66aGFYdbSenh7xxz8+IdLTk0VWVqZITo4XBQV5Yt++vUIIIVasWCFsNqt44IEHgsVoouWpr169Wpx44kyRlZUpzOY0UVRUIAoK8kRmpllYrRliyZIl4sCBA8F85yVL/kfk548T119/XV+VN6swm9PE1KmTRVaWta+dxeKhh34rWluP5vyWlZWJoqKCfu2I5LPPPhPnnLNA2GxWYbWaxbhxOWLSpBJhtWYIq9UsLrpoodi8eXMwl3zp0qV9eeJ/jXo9r9crHnjgfmGxZIg33ni939+WLl0qiot7q6KlpSWFvC+LyMnJErfffrtoa2sLy69+8MEHgzKXmWkREydOEOnpyeKUU04Wp512qsjOtonly5cHz1mz5oN+hYEi86d/8IMbhMWSIQoLC8S5554jzj77LJGdnSlOO+3UqHnl/d+lVzz//POipGSCyM7OFBkZKaKwME+MG5ctMjMtIjc3W/zv//5cNDU1BgvKLFq0SBQW5ocVDQrl8OHDYv78M8QJJ8zoJ/9ut1u8/PLL4sILLxAmk0YkJRlFamqiuOCCb4lnnnlaXHHFFcJqNQfzyENrMdx0043CbE4TWVmZwmrNEMXFhcJqzRCFhfli/vwzRHa2Tdx33339nqe9vV089tijYs6cWcJgQCQm6oXVmiEWL75GPPvss2L27FmitHSq2Lt3T7/z7rrrLmGzWfp0QUrfPc3CZrOKqVOniL///amgXO7atUvMn3+GsNksYtq0KeLiiy8SU6dOFhZLurj55h+HFYUaKxQh5EbIkm8m69Z9wqpVq4b8XXHxeL73ve+FzTB27drJO++sZNeuXTgcdtLTM5gxYwbz589n0qRJYWvdjY0NLFv2Jp99tpnGxkZyc3OZOnUql112GWZzbOuSHo+HTZs28emn69m5cyddXV1YLFamTJnCggULKCkpCc4i9+3bxz//+QJms5kbb7wp6rr7+vXreffdlRQXF3PNNYvDZmkOh4M1a9awd+9euru7MRj0/PSnP0Or1bJixdts3ryZ008/gwULFgwaN1BeXs4HH3zA1q2fU1dXh8lkYtKkyZx88snMnTs36Dru6OjgyisX0djYwEsv/Qu3283q1b3uU4/Hg9lsYcqUyZx++hmUlpaGzZY3b97E8uXLo7Yjmqv7/fffZ8uWLRw4UIFOp6O4uJhZs2Zz6qmnBiP9fT4fzz23lMrKSi666GJmzZrV71put5snn/wzbW1t/Nd//Xe/5Qe/38+uXbv6atFvo7m5GYvFQmlpKXPnnsyMGTP6zagdDgerV6/mo48+orx8P3l5+UyZMpkFC85h3brevOVrr72W4uLxCCFYvXoVn3yyjtmzZ7Nw4cKobW5vb+f999/nk0/WUlVVRWpqKtOnT2fhwoUUFhbFHAPS25bVbNu2nfb2NjIyzEyePIl5805lxowZwYC8xsZGnnqqd7vdW265NWpqZnV1Nf/4x99JTEziJz/5SVTvR0tLC21tbXR0dJCcnExqagpqtYZFi65g69YtvPvuKk488aSwczo7O1mz5gM+/PDDvowUPVOmTOGcc86ltbWVjz/+iPPOO5+zzz47anzGkaUQt9tNUlISGRkZNDc3s2jRFWg0Gv71r38FAzFD4yjWrl3L2rUfU1ZWht3eTVFRMVOmTOWMM86gqKgo2D4hBFVVVbz99tts3LiBjo4OiouL++rqnx1zfMdokAZd8o0lEPATCAwt3qHR6pFrmw6Hg0AggEajISEhYdAdnBwOB16vF71ej8FgGFERiUAggN1ux+/3o9VqgwFBkb85Egw1kIGL5TdH2njE/XrEUAkhgkF+seBwOOjp6UGtVhEXF99Pge/evZvvfvc75Obm8vrrbxAXF4cQgq6uLgKBwIDtHE47Io2xy+VCURTi4uKiDniOtFOtVg/4To/0zWC/EULQ3d2Nz+dDq9UOKiNHn8+Fy+VGr9cHjX60ew3nXbhcLjweDxqNmvj4hBHttBYICOz2bvx+PxqNhvj4+H73FUIEi9cMtNGIEAH8/gCKAmp17HHXW7du5bLLvk1qahrvvbdqwIC73ra6URQV8fG98ubz+YLPHaucALz11pvcfPOPOfHEk3jhhX8OmEng9XpxOp34/X6MRuOgWzAfkYkjvx2o5sJYIKPcJd9YVCo1oynMpNFooq7ZDjQoiHVXtcGfWTVkQZpYFHysBjlSKQ9HGR4hPj6+X5W1UOrr6+ns7OCEEy4JKkJFUWLq2+EMLI5gMBiGVKKxtDOWnbEURYmpgFD48/VGyg91r+G8i6GMTGyyN3RbFEUZsl8URYVGo+o3OFqx4m3Gjx9PScnEsHcaCATYt28fDz30W7xeL1deedWgs9lobdVoNP2ey+Vy8corr7BgwYJ+O9/19PTw2Wef8cgjvUF4V1yxaND+02q1w9IFw5UJadAlEsnXgq1bt6JSqZg9e86I9+iWfL3ZsuUz7rjjdpKTU5gzZw4nnTSLrKwsmpub2bZtG//+9/s0NjYwf/6ZLF68+JhsM/r228u59957eOGF55k9ezYzZ55IcnIyhw/XsmPHDt577z26u7u48sqruPDCC78R/Sxd7hKJZMzw+bzcdNNNfPTRh7z44stR16sl33x6C6v8lQ8++DdNTY1h6VuKopCRYWbhwoX84Ac3DlinYLhs376dJ598kg0bPqWzs6Of98NqzeTaa6/l6quvibnmhDToEonkPxaPx8POnTvx+31MnDgpZrel5JtHbx2HKqqqqqioqKCrq4vExCRycnIoLCxg3Li8ES35DIbf72ffvn3U1dVRWXkQp9NJamoa+fn55OfnYbNljfmGKdKgSyQSiUQiGRayUpxEIpFIJNKgSyQSiUQikQZdIpFIJBKJNOgSiUQikUikQZdIJBKJRBp0iUQikUgk0qBLJBKJRCKRBl0ikUgkEok06BKJRCKRSIMukUgkEolEGnSJRCKRSCTSoEskEolEIpEGXSKRSCQSadAlEolEIpFIgy6RSCQSiUQadIlEIpFIJNKgSyQSiUQiDbpEIpFIJBJp0CUSiUQikUiDLpFIJBKJRBp0iUQikUikQZdIJBKJRCINukQikUgkEmnQJRKJRCKRxILmy7hpm9dNvdtOmauT9h4XakVFgkZHviGBHEMiaTojakUZ8PweEeCgowN/wD/iZzCqteTHJxN6l2aPk6YeB4ijxyxGE+laQ/j9AwEqHO0IEeg9oECy1kiWIWHI+3oCfiocbWH3MGn15BgTw57FKwIcOAZtLIhPHvX7qnJ2Yvf1jPh8tUpFQVwKOpUKd8BPpaOdgBCjeyhFwWYwkaLVD/vUCkc7Hr8v7FiKzogthvd39N204w8Ewo5nGk2kRsjKUKzvqKelxxX8/3i1ltNSbOhU6pivYfd7aXLbEQzepwoKBkWFolKhV2tJ0RpQhrh2t6+HGlcX4sj7UsCsTyBDZxzyubp8PdQ4O8OOZejjMevjwo45/F6qnJ1H7zECUnVGMmN8f6G0e93UubpHJYpDvfcuXw/17m72Ojtp97oAhQSNFqsujmJjEsk6I4ZhvO8j19vlaKfb50EA8Wod4/Tx5Mclkaw1oB/G9SJlu9njpMzRTr3XSXePGxAYNXqy9fFMjE8lZRjPKxAccHbi8XmDxwxqDflxyagUZcjzy+xt+EJ0oFalpiA+BU3IuQI45OzEGaKjdGoNBXHJg9qRI5Q72ukJ0Qeh+ir0HrWubrq87pHPnhWF/PiUYb3rr7RB7/B5WNVUydKWA7zncfR1kxLSZTBZY+DyhHS+lVFAaaI5qmB2+Xq48cCnfOxxjPhZfmay8OuS09AqR1/axvZaLqreFva75fmzWJiRH6HkPEwuXwshAnS2IZG/j59HnsE05GBmyr4PIUR5/S51HLcWzg4TPoffy7UHN7DZbR9xG28zZfCHiWeO+r39q3YXd3YcHvH5J+niWDHpbMw6Iy1eN1eXf8K2UQwQjhj0jwtP5rTU7GGf+vChLfzV0RZ27Lvxqfx5/Kn9Bm/R+LjtMN+u3IQ9bLAlWJ0/hwUZeTE/R1OPizsqN7PeF6IkFBV7dWdSYkqLfcDl7uaEPR/QIwIx9Bug0rBAa+A0YwoXmAuZZEof0AAccnUxrewjCGnrdYkWfls4l7QhBlPVjnamln0Y8o3DmznTuThzQvjv3HbO2v8xDRGDrOHwkqWEK8eVDvu8HV3NzD+wPuwZh4fg3wUnc1Z6br+/OAM+3m06yKvNlfyfuzOqvrOptVwel8q5abmckpJNkkY34J2cAR//bj7ES80H+D9X5PV6rzlOrePbcSlclJ7HzGQbiYNcL3KStLOriTcaK1juaGG3zxOlTwRZah2XGlO41FzASck24tWDmxCfENxTtZWXupuDx2xqLf8qmMO8FNugvS6E4NaDG1npPjrgutSYxNMTzyQ5pF1+EeCx2p080VEXPJaoUvNG/mzOTMsZ/B4I7jy0hTdC9EGovgp9ltcayri1+cCIZXSqWsuKKeeSq48fMxt73FzujR4nP9u/nkWHd7IqaIiVCE2jsNvn4d6Ow8yuWMed+9dx0NkRdd6hQQFl5P9USnRdp4743UDCkBnxu7Webh6v3o7d7x2yLywRz64MMIrUjbaNI1ZS/ftlNM+hVY5tu1AUElFG3DpVlPu/4mxnWWMF/iFmiY09Lp6s34NdBPpdQxnmA33aVstWX09Q9kEBIXi7qYIAYlhvyBpr36FAwM/7Hge/7KjlhPK1/PrAJtoGmXlYIq7xj+5mXqzfizeWAUSkrEd5a4oCCaOUCWWEwqAoo5fFaPe2+708cnALl9fs4BV354D6rs7v44nuJi44tIUb9q5hU3tdVO9Vt9/LHyo/5+KabX3GnCgGV6HK7+Wx7ibOrNzMkn0fsamjnqHe0mG3nfvKNzCvfB0PdtaxOyiT/d/lYb+XP9qbmH9wE7eWfcyhCA9MVA9dxLutC/h4oGYH1e7uGM4N1z3qAZ4sUm93iQC/qt1BubNj2PfQKmOjB3WKMuZ29rgYdJffxy8PbuQfjpaQselQA1/BY92NPFi1FV8gwFedHuC5rgaer9+Hb7TuZMmXwh+bKyhztA8623i1YT/LXJ2j/yYCPl5pOYgrytfwUlcDDW7H8Wm0ENzfUcsvDmykO4bBaK9iEzzUfIBVLdXDHHj8Z+AXgmdqdvHL9uq+OWBsvOLu4mfVW2mJGFx5RYB/1OzinrYqlGHoln+6OvhV9TZaQ5Z0Itlnb+XG/Z/w687DeMRw9KzgKUcr3y/7mE/b64bdR6s93fyhajudo/XUDcK6Hie/q9pGm9fzHyN7x8XlvqWznlfsrWHHUlCYqDMyXp+ARwSodHdT5e+hPkRg81QafpQ9Fa1q6HGHCchV1DHP2kJd7ceKdiF4vLGciXEpzB+BK7j/JxNOPJD3JbcRwACMU1RoY3ySBEUdZg40itLr4Yg0mEBzhMJKRCFeiT7qP9bj3V2+Hv5yeDe/LZob1ZW4x97Kn1oOHpN77ehsYrW7K+rftvs8rG2rYZFt4oivn4FCiqKEyVAAcCFoFYJIFfdidxPnNFVySeb4mOSyLuDn14d3UhSXREl8yjGXsQwULMOQX/UxnP3kKwrxMc51vIh+krjP2c4TrZURcgwTtQZKDIkIBBVuOzU+D/UiwJGFhnhF4ee2yf3iE6qdXfym9SBKiE6IB6ZqjRTpE9ApCgc8Diq9LupFAG/Qi6hiibVkwHiHSlcXPzqwkX/3OPrNRFMVhSxFTZrWQADo9Lmp8vtpj9BKa3xueg59xpOaU5hqSh9WP7/Q3cDEhjJuzJoS03r6SPg/ewsT6vbwk9zSMdGH4xVVr8cxBhIVFWM9Rx9zg+4XgnfbaukKEwSF32dO5LuZEzD2rd35RIBd3a280VjOsq4GykSAe6wTmJyQHlWh+CIE6xR9PC9MPCvmDtMoqjF5wfsDPu6v2UGWIYHxcaMISBP92zhdH8/rJfPRxPjcY2XQC1VqXi6Zj00XF5sbSFFIUveueaVp9fyx6JSobsVDjjYur90e5lT7QUo2V5iLovq/hopXGAl/627grNZqLjbnhynqbr+XPx/eTVmMs9ihvom3WippHWS29XxLJReYCzHFuAYayZJEK/89bkaYBPlEgE6fh71dTfypqYKNvqNmvQt4sbmC8y2FMQdUbfS6ub9qK48Wn4xZaxy5qAuIDP1cnJTJXfknxTy/PZaBRo/ZpnLKMGIh4tXaMN30RWcjNRHBrHek5bEkd3pwoOgXgjJnOx+2VLO0rYrtfi+3JGczPy23nw7b2FFHS4iX0gjck57PDTmlwesFhKDc2cGqlkO83FbNLr+X7yfbOHeAdjj8Xu6t3MxHEcY8BYXz4pK5MXMi0xIzetsmeoN5N3fW81JjBW84Wgj1Y63zebiv8jP+OnE+acMICu0Ugocb91NkTGbBEGvdI8WO4I/NByiJS+bCjLwRTwNEn4cu8ujL409jnDExRs+WMmiMxNfCoAsEO93dYZ9lqdbAxZZCTCEfgkZRcVKShZmJZq60t7C6tZpLLcVRR94KfWvoocYLhTSNfsD16OPJhz0OnqjezgNFc0nW6Ed2EYV+M2ANKlI1hpg8FmOJGkjR6If18QaVkUrNjAFG8vFRXH65ujhmJmYct7Z5heCR+j2ckGgmty9qOoDgvZZDPNXdeEzucdDZwf91Hb2WHphvMPFeyJriux4727qaRhT0B5Cq1gafP5ITTBlMNZm5qvwTdgeOBqJt97qpdHUNa8b9qqOVaYf38JPc6SOOrFaUvnXMCDlJ1eq/FPlOVGtGJNtHNN4OVydhjmRFxTXWCWHKXKPAtIR0piakc7G5kFcaK7g6c0LUgUlnRPBvhqJwiWV8uHFQYHJCGpMS0rjUXMRz9Xu5LnvKgIP6T9pqecfZHvQOKIAJhfstxfx31hTiQj1UCmhVKs5My2VOso0ZNTu5r/kATSFa/TVPN5c1Vw7bq1QZ8PNg7Q4KjYkUxiWNyfusFQF+fXgXecZEpiakjVQdh0XWHyFZoxuFrBx7jotlcIvw0epOfw9Ob8+As7lppgx+mjcz5gjNryJ/7m5mad1eer4G6/+SCGXX4+Sl+n3Bd1fl6ubh+n3HaIALq1qrqAwxpFPVWn6bdxKlEYO/V5sq6BlF2uJgTEvM4Fvx4cqtQvhxD3O90Qv8vqWSt5sr5Xp63/v1RL4zEaC5xzmgociLS+L2/JlR014DCJyB8Oh/p4A6j33Q691TOIcsffQBnSvg5+mGMlpCZpwCuCU1h+9nTw035hHEqTVcl1vKjSlZ/b1KzQdpHWZalwA+6nHy2+qtwz53OPfY5HXzUPU2mgaJJ/gmMOYGXUEhN8IdJwJ+flm5mY3th49ZwEIwAvFLwhYxslYQPNRUwcdtNcdUzX0FHBDfAK0bPsgap6goVevCPoY/tVWxpasBrwjwUv0+NnnDFcG5UZSlLoYljpYeF8vba8N8L+clWpmSkMb1qeGpTy/am6mIIUp3pMRFzixE8D/hRiXC1Rgp660iwG/qvuCL7tYIY8SIs8G+XDFXRnGmQl7EUpQK+MWhLaxoPECjx8lwhvgqFBIjBnotCH5VtZW3Gito8jiHzMyIZL+9jU0hAwIFOFGj579sk2JautCr1NyUU8psdfiE690ee7++FXMAABcqSURBVL+6A9HIUFSoI9aTX+hu4YX6fUNmTgQzVIbAgEJ8xPf4oqONpw5/gfsYDpIVvloKecxd7ipF4VyThRe7m8LcUM8723m+Yh3n6U3Mj09joimdUlMGmYaEIdd+BaLf+nKd38urDeUxGbx5yZlk6hOOaTtPiUtmoi6Bh9qq8PSpxSYR4N6a7RTEJVE4zPV0IXrdv6E0+3t4rbEipgCgk5OsZI3BGjP0rn2903KI1Bg8KHnGJE5KNH8F51FHmajRc7V1Aotrd6CI3lXbwwE/T9TuYrHXw8NtVWG/X2IyMy/RwqrDu8KOxxKuuK6tlvdDZms2ReHijALUisK56XkkNR+gs0+ptQvBe82VlMSnHvOgIXfAT21EtH6WSoUmZBmsVw4FkdnhVyRn4exx8jd7S/DYNl8Pv636nCcmnB7M5ffFGDUdbQ19j7ub1xrLhxwMKyicl5ZLwjH05n3U1UhzDGbXqNJwdlpumBFUgBmmDNKaKmjte/oAsNrr4r3qLZxYb2RhfDpTEjM4wZRBuj6BhCFyuScmWqC5PGyg8ZHXxUfVW5lRt5sL4lIoTbIyM9GMWZ8wZG74ju5makLejQDON2XEvBYMYNHHc1myjU2th8LexsbOeqYnWQY9N1tr4O7UXO5tPOol6EHw+6YKJselcFZ67oAptxoltlmoW6XiWWsJd9Xvpa6vrQrwcOshpsSlstBcMKxASkF/fQwKK1urMHcPHT+SYzAxO8n69Q+KU4CzMvKY21zOWq874gNVeM9j5z2PHdoOUajWcaExiTNTx3Fyio30AaIzFZR+a+hbfB6uqNka05v5XHfqMTfoCYqaH2RPocxj5xVH76ccANb7PNx/6HP+UHxKTEVLQmfi2giB2+PzcGXNtph6/bPCk/sZ9EpnJ+4hineoVArF8SmD5rBXiQA31u0mlgTE35uLx8SgCwQVjo4hUxo1KhXFkWvCEQNGBYX5qdnc5Wjj123VweNvujpZW72V9r4PWQEma3T8IGsKdVFmIkOl/dj9Xt5sDR8cnGJIYkpfTEGOMZEbTGZ+39UQ/Ptz7TVcYZtI1jEsRuEK+Hm5bm9fsZOjTNboyYyQGeVI/mzIqx6nNbLAOp5dFZ/yacjg5E1XJ0XV2/l5/okYVZpej4UYesIbbQ39DVcHb1RvjUnWG5Osx9Sg39tRCx01Q/7uQl08J6dk9ZvVnpBk5bL4VJ5ytIZ7K1DY4nWzpe/66SoNF+hNLEjJZn5aDrYBBuAzksxcH5/B3x0t/f62zedhW1cDdNVjVmk4X2/irJQszk7PI3MAmamM4q4vTcgYlrFRgJMSzRhbDxHqu7LH4NJ2iwCXmwtp8br5feshnH2apE4EuLt2J1lGE5PiU6Oe2yNEzFUmv5WeR4ffyz2N5XT1hVd2CMG9dV+QazRxwjD0kkJ/fawCftiwLyY9eG9qHrMSLWMe43Vc0tbSdEYeK5jDzQc3stHrpmeALjvg9/K4vYXH7S2c27CX/82eximpOcMYScXwO2Xs1vnMOiN3586govwTPg+JIH7O0cr4w7u5LXf6l+oO/F3NDv7Z3TTob0rUOj4tvQBVTAFOSgy/GBsB9gvBbVWf8+EQLulzdXG8PPW8CK9P/2cyqNRcZ5vMp/ZWPuyL/PUA9SHuOYHCTy0TmGxK43AUgz7UGvLu7haedx6tSBUPLM4oCAaT6VVqFmbk8/uu+uAzlvu9rG+t5ju2icPqyR3uLpY17A8tSEgAQXWPk+32Ft52deAM+aMBuCY9f8jqb0d6YlJ8KndnTeX6qi3BiG4X8Nf2Worjkrk6ohrc8Zb143Fv1QDSHafWcE/BbFwVn/KqqwP3ANdvCfh5ztXBc64OTm7az83WiXzbWtzPQ5mg1nJvwSxcBzbwqrMdzwDXawq53pnNB/ihtYTzzYX9Bhz9iwgJsnTDz1LQa/TEKwquEDmq83rwBALohwjcNag03Jg9h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HTTP/1.1" 200 - +INFO:root:2025-10-18 09:45:48.047750 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:45:48] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:46:03.239186 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 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BULLETIN DE NOTE - ii
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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HTTP/1.1" 200 - +INFO:root:2025-10-18 09:50:02.915270 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:50:02] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:50:08.622866 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:51:40] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:51:40.760418 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:51:40] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:51:44.395754 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:53:29] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:53:29.048056 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:53:29] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:53:33.204019 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:54:40] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:54:40.861190 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:54:40] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:54:56.854186 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:56:41] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:56:41.716716 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 09:56:41] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 09:56:46.341867 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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: Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:00:47] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:00:51.838433 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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: Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:05:44] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:06:06.758868 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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: Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:08:34] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:08:38.030305 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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Photo
part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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: Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:10:35] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:10:39.798137 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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: Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:11:35] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:11:39.733950 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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: Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:12:40] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:12:44.821095 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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: Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:13:46] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:13:52.095997 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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: Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:17:22] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:17:25.829262 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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: Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:18:58] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:01.046123 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 30% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 10% +DEBUG:xhtml2pdf.tables:Col 3 has width 10% +DEBUG:xhtml2pdf.tables:Col 4 has width 40% +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: ['30%', '10%', '10%', '10%', '40%'] +DEBUG:xhtml2pdf.tables:Col 0 has width 10% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 10% +DEBUG:xhtml2pdf.tables:Col 3 has width 70% +DEBUG:xhtml2pdf.tables:Col 0 has width 10% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 10% +DEBUG:xhtml2pdf.tables:Col 3 has width 70% +DEBUG:xhtml2pdf.tables:Col widths: ['10%', '10%', '10%', '70%'] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:02] "GET /myclass/api/Prepare_and_Send_Inscrit_Bulletin_Note_By_PDF/6TncY6p2FiZFzzgGrjEWhhCGzqwJznbilw/684f106ae266de5f7fd519ea HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:40.605443 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:40.607443 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:40.611443 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:40.615444 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:40.628443 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:40.674516 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:40.676516 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:40.679517 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:40.686999 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:40.689006 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:40.689006 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:40.693007 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:40.703004 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:40.707006 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:40.718005 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:40.722006 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:40.738036 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:40] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:41] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:44.934658 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:44.935657 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:44.939161 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:44.946190 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:44.949188 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:44] "POST /myclass/api/Get_Partner_All_Class_Few_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:44.953188 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:44] "POST /myclass/api/Get_Partner_Session_Ftion_Reduice_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:44.962191 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:44] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:44] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:44.973198 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:19:44.979702 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:44] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:44.987735 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:44] "POST /myclass/api/Get_List_Unite_Enseignement_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:45] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:45] "POST /myclass/api/Get_List_Partner_Apprenant/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:45] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:45] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:19:59.023736 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:19:59] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:20:19.403975 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:20:19] "POST /myclass/api/Get_Apprenant_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\apprenant_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 10:22:08.197511 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 10:22:08.197511 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 10:22:08.197511 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 10:22:08.197511 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 10:22:08.197511 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 10:22:08.254639 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:22:08] "POST /myclass/api/Get_Apprenant_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 10:23:28.893461 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 10:23:28.893461 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 10:23:28.893461 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 10:23:28.893461 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 10:23:28.893461 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 10:23:30.965878 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:23:31.012440 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:23:31.052446 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:23:31.084126 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:23:31.120019 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:23:31] "POST /myclass/api/Get_Apprenant_Recorded_Image_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:23:31.168049 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:23:31.199031 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:23:31] "POST /myclass/api/Get_Given_Apprenant_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:23:31] "POST /myclass/api/Get_Apprenant_Recorded_Image_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:23:31] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:23:31] "POST /myclass/api/Get_List_Suivi_Pedagogique_No_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:23:32] "POST /myclass/api/Get_List_Apprenant_Notes_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:23:33] "POST /myclass/api/Get_Apprenant_List_Inscription/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:23:54.166364 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:23:54] "POST /myclass/api/Add_Update_Apprenant_Image/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:24:11.721773 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:24:11.744126 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:24:11] "POST /myclass/api/Get_List_Evaluation_Final_Note_Classement_Detail_With_Filter HTTP/1.1" 200 - +INFO:root:2025-10-18 10:24:11.772617 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 10:24:11.801719 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:24:11] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:24:11] "POST /myclass/api/Get_List_Evaluation_UE_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:24:11] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:24:11.827611 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:24:11] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:24:11.850654 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:24:14] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:24:16.198938 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom6_client   part 6
Né(e) le 03/06/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION18.787.36 Faible 0110
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 excellent travail
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Note générale  

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RangMoy. ElValidéObservation
12.931iiiiiiiii
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom5_client   part 5
Né(e) le
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION36.397.36 a encourager - 17/10
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0 okkk 17/1000
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 doit redoubler d\'effort
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Note générale  

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RangMoy. ElValidéObservation
32.131Indulgeance du Jury - 17/10
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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part nom6_client   part 6
Né(e) le 03/06/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION18.787.36 Faible 0110
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0 excellent travail
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Note générale  

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RangMoy. ElValidéObservation
12.931iiiiiiiii
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:58:02] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 10:58:10.094249 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 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+DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +INFO:root:2025-10-18 10:58:12.777416 : Create_Bulletin_By_Inscrit_PDF -no loader for this environment specified - Line : 3095 +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 10:58:12] "GET /myclass/api/Prepare_and_Send_Inscrit_Bulletin_Note_By_PDF/6TncY6p2FiZFzzgGrjEWhhCGzqwJznbilw/684f106ae266de5f7fd519e0 HTTP/1.1" 500 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 10:59:12.777191 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 10:59:12.777191 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 10:59:12.777191 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 10:59:12.777191 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 10:59:12.777191 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 11:00:38.554802 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 11:00:38] "POST /myclass/api/Get_Given_Personnalisable_Fields_By_template_ref_interne/ HTTP/1.1" 200 - +INFO:root:2025-10-18 11:00:49.169656 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 11:00:49] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 11:00:49.217692 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 11:00:49] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-18 11:00:51.950113 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 374 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 286 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 263 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 387 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 436 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 351 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 364 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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© 2024 Mon Site

\n
' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_278.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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© 2024 Mon Site

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connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 19:00:35] "POST /myclass/api/Get_List_Partner_Document_with_filter_Admin/ HTTP/1.1" 200 - +INFO:root:2025-10-18 19:00:37.647016 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-18 19:00:37.650952 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 19:00:37] "POST /myclass/api/Get_Given_Partner_Document_Super_Admin/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 19:00:37] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-18 19:00:41.756553 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 19:00:41] "POST /myclass/api/Get_Given_Personnalisable_Fields_By_template_ref_interne/ HTTP/1.1" 200 - +INFO:root:2025-10-18 19:01:13.577304 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 19:01:13] "POST /myclass/api/Update_Partner_Document_Super_Admin/ HTTP/1.1" 200 - +INFO:root:2025-10-18 19:01:13.652532 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 19:01:13] "POST /myclass/api/Get_Given_Partner_Document_Super_Admin/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 19:08:15.097078 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 19:08:15.097078 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 19:08:15.098080 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 19:08:15.098080 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 19:08:15.098080 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 19:10:23.949413 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 19:10:23.949413 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 19:10:23.954408 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 19:10:23.954408 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 19:10:23.955415 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 19:11:00.349843 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 19:11:00.349843 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 19:11:00.349843 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 19:11:00.349843 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 19:11:00.350845 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 19:11:16.063876 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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© 2024 Mon Site

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' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_025.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 41 4 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 35867 +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 30% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 10% +DEBUG:xhtml2pdf.tables:Col 3 has width 10% +DEBUG:xhtml2pdf.tables:Col 4 has width 40% +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: ['30%', '10%', '10%', '10%', '40%'] +DEBUG:xhtml2pdf.tables:Col 0 has width 10% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 10% +DEBUG:xhtml2pdf.tables:Col 3 has width 70% +DEBUG:xhtml2pdf.tables:Col 0 has width 10% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 10% +DEBUG:xhtml2pdf.tables:Col 3 has width 70% +DEBUG:xhtml2pdf.tables:Col widths: ['10%', '10%', '10%', '70%'] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 41 4 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 35867 +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 19:11:21] "GET /myclass/api/Prepare_and_Send_Inscrit_Bulletin_Note_By_PDF/6TncY6p2FiZFzzgGrjEWhhCGzqwJznbilw/684f106ae266de5f7fd519e0 HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 19:13:03.747067 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 19:13:03.748070 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 19:13:03.748070 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 19:13:03.748070 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 19:13:03.748070 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 19:13:56.316028 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 19:13:56.316028 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 19:13:56.316028 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 19:13:56.316028 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 19:13:56.317028 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 19:14:03.729520 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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© 2024 Mon Site

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

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Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

\n

Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

\n

Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

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\n

Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 19:43:42.770797 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 19:43:42.770797 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 19:51:44.250611 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 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+DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

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Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

\n

Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

\n

Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

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\n

Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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c9i1At99913fxG7hdcQnkJAIfoITZ5AeICmsCAM3+QRfu4dmq43hYemhLFF8xa0aNZM+n3C6zT9HQKEDm3aSQljY+LYmC/06CIjAdCpbVsJhh5fcoU+ojEloJDpwiBFRQx1dTmwfz/Pnj375wVBNJHEwo3nT56wb9cudBZrMnyQMiMGDZQqhMH9FaVQ0FdUBt27ItOpg6TuMp3pKkD4GQRC4n2H9sIAHaRuYZNhhMTIFyO1y5dyUhhJ5BAdO3SUjNu8uTBua+m1tagmWrWS3gtARBLZrFlzvv/+hy/f25wWP0sipcrhS/koQoSATgDWVniYNm0lNQLRVoJBVBCilOwsYBFJbNt2UpjoLXogPXpIQIikUaF3b5Ys1Gj0CF+Wz/9TgiA+1Ls3byUYTh0/jp72UsYMH84gBUX69ehBH9HN69xZukndvqizlAy2kXKCprygfTsx0ttJVYIwiOTixcgWo1mMXlEethRGa0Pr1m1p9yVxlPKItiLrbytB0LKFML4Y8UKtaN6shfTvZs1a0OyH5j/7WgsJCAHAz0NHk0RJ2pRgSkC0biP9PgGE+Ju6ipAmSti27enQuq3kLUTY6NqpC71Fe1o0nHp0Z6CCPHNnzuTQwYPSfMs/PQgvX9Rx7swZzIwMpYmYAXJy9OnWnV6iXhfZf0cxP9BJ8gAd2rWlfVthaFEOipHdjrZtxGhvdMdtWje6cqmr16GjZGgpT5BGeSMIbVq3lZLEtm3a0UaUkBIIwu23lgBoIQwuGV2EhD9LgNJSNKF+wfi/JCl8SCGpEYbGykWA0DjP0aVDx6/hotFbiBAhg2y3rlLlIJpmixYs4NLFi187jN8a4x+pvx0IYnpZrCx+UcfZ06dZbmzEuFGj6N+vnwRBN1EOCtfcto1UIgrDSxC0FSOsNZ06isSs8b14FeXbVxCE2+0gPET7xqRRut4oYfgmNV0TEHyrVl+8yM/VsuUve4FfUhMITfmJkAhHAgQxByESSGF8oXatWtO+dVu6d+4iJYzCIyjKykoTUdevXfvaVPrWGP9I/Q1B+MDrl6+kuYVd27ezTGsJwwYNkkAQNXbn9u1oL5I5qSZvdP8CiCYoRHIoPEOTlxBdPRGXRVxvrAo6SKNfKutECPi5UZsM/nWkCwP/Agjf6H8Ogqh0utBJVDDiekvx+dpKEuGhqXoQIMj16ompoSF3bt/+2mb+1hj/SP3NQBDLzcWeA7HeMCUxkTkzZzCwvyKKfftKtbWAoG3LlhIIwthSUtix/VcQ2rZuJXmDDu2Fu2/VmAt86QA2xX/JoK2ER2jKBZpCgEgMW9GsuUj+voQBYWQJlLa0aNma5lJIaMoLmkLFXxv8P9NfgiB6Cq3p2K6xtGwrQlWzFrRu3pL2oppp066xtJTpSm8ZGSk0KMn1Y/PGjdL+i99afiD0NwFB/CAx1Xzu7Fky0zMw1NOT5uYHyIslY7ISCO1atqRNi+a0b9P6a/dQNJCE4QUQzZt9T5tWwguIkd2MFs2bSfW8MIDwDE2JYMtWAoA/h4OWLdvQrEVrfmjWih+ateSHL0lhc2FsAYD09Vb80LyFVC00ay4SxeY/A6IxWWySBMfPk8ifJZKiihAeQIJWkghjbaXWc2tRoTRvKTWdhHcQayVEb0SUj7IyXZgwaiRnTpyQVmT9Ftck/I9A+Isf9uGDtAQ9PTWNxRoaDBs8iH6yvenaqSMy4gZ27EhbUdu3bvU1YezYTmT7olQTpaJI+ERnT9x4AUALKUuXMnSprSyaQyIhFLX+z/OANjRv0Ybvf2jF9z80VgVfK4QWLflBUgt+aN6cZi2+qHlzydhNM5ZNAPw8qWwML41t6ybP1E7kNG2EN2hFJ1HJiHKzRXMpjImfJbyFaIeL/EY0vkRZKUBQ6tcXK1MT7t68KeVSvzUIhP4mIEgfTOj9B2npmdrcuQxWVqJf715SpSAaLW2bN6dD69aNHcROoj3biZ5ihVDnjo3NI1ExtP0zCK3bNNbzwvW2F+5XVAxNieGX3EC4exEOBAythJq3onWLRrX6YsjmrVrSvHULmrduTos2zWnZWoSMdrRu2ZHWLYV3aUnLVsJ7CDWGFEni54gw0KaNBMIPzZrTvFlzWjVvLoU5GRHOxP+ToGohSUAr8oamuRBpllJGRuqm6mtpcePy5ca9lL9giH+0/uYgXL96DYvlyxmspCStLxD9gi7t20veQOrFd+kiTTcr9BVLx/tK/QOREzSCIKoAAYKo2xtDQuP8wZ/zgaY8oW1bMY3cmDe0a9OOLu070EUkaS1b0blFozqKdnRr4a6b067197Rv/Qc6tvqBbm3b0rt9F/p0aE/31n9CtkNzZFr9ke7tWtC++R/p2KYlbVs0o13LFo25TevWNBPdRtGoatGS1i2a06ZFC5p9/z0//PDD11yjseXd8WuDq4eYeJKRoW/3rmipq3P1woXfDwg3rl3H3cUFJUUFaUaxe5fOUvtVwNBTpotUPnbrIubzu0m1t5Rxi4RQwCImfqQegfAMAorG8lFq+0oJX2MYaN6iNd//0EK6JvoHImmTlenEsL69mTFwIDMHKDNBti8ju/VgXJ++TJaXRXvScExnjMRs6mCspg/Cdc5wApZMJNJ4FnEmqgRrT2GVxniMJioza0APxvaVoX/X9nRrJ5LcFrT4oRnNmzVrbCwJL9PsBynX+OEHoWZSTiHKXFHhiG6n8AxNOYJC714sW6z5+wLh1o0bbAoOZtTwYdJKo749e0rtVqmx1KMHHdoIt9pR8hRSz/7LXMJfgvClPyA6hF+SNQFHq5/1DETy2LVje+S7dmCKUk/MVUfibzCHWBsd0lxMSPcwJ9nDjKy1tuxNDOJ6VQaXC+I4mxrEmURfjkc7cWrzCs5vsuJ8iCWn1i/nkJ8JNV765NtrkWiuTrDOLNznjsdo7EDm9JdlQo9ODO3Qmh7Nv6f1H/5DChMi1/jh+2ZSC7tje7E2QsyWNk6UdenQQQJBVA3aizS4eeXK7wcE4RHWBwYycdw45Pv2kWAQK3gGDxggTcZ87SW0bsyspb69VB42TgNLBv/aKBIeoXFqWISJDm3b0LlNS2RaN0e2fQtG9+nC8lkjSHTSYtcGc46H2XE1eRW3Mvy5lbuO6wUbuFcVzvOdqTypSeN5VSrX0zdwMc6T87H2XI2y5kbocq4Em3JhgwnnAow4vcaA46v02eemzS6XpWxz0ibXfCGbtWYQrKmC7/zJLBs9kFG9ZejauiXN//gnWjQTIaM1Hdp2pG2rtrRp1YaO7YQXbARBzD4u1VjInRs3fj8g1D1/QWR4OCOHj/i6Q0nsEhILOETX7ecNF6kt207MNzS2jr+OfJEPiISwTTvatutIS5G1t2yGTOvvGdGzLcvGyuM6ewixRjPYuUqfC5tXcD3ClquRDlyP8+R6gg+3Uv24nbmBu3mbuF8QzuOSGJ6WxnInPZAr0S5cCrfjWoQD18LsuRRqzU8bV/DjBitOrbPgmL85+1cZs9vbkJ0rDdjmrke5sw65NuokW8wh3GgGLvPHojqoH91bt6HZv39Pqx9a0aFVB9q1EJ+xA906dqFr+47ItG0nNZTU56hy69q1f04QmmD4OQhir9/e3buZMG48/RUUke3ZS4JALO1qnL5tbMOK6VwBhJi8EcmYKLcaa3kBQ2NVIBpQXVo0Q65dc6bLd8N04kACNSeRabWAvasMObPenPNB5lwKseBSiCWXN1lzLcqZO6lruZcVxL28zdzPD+dOdgi3M4K4lRHE1aS1XIz24Eq0K1ciHLm4yZYLITacDbLiRMByjvqZcdTfnANrTNnlbUiN2zIqnbQoE3JZQpm7FoUumiRYqeOpMRnVIf3pI6a+m7WiS5sOdG3XmZ6du9K3Ww8UevVmoLy8tBLLzEC/0SP8BktHob85COJcgJvXb0ggDFIeKIEgVvGIXnyTJxAQiKZLmy+lnoBAqumlBo4Y/a3o2LwZ8u1aMn9gPxxURhGlN4dSey32eehycrUBp3yWcXrVMk756nJ8jR6n15lyZr0Z54ItubJlJbcyN/KkLJ5HpfHczNrE1ZR1EhAvymJ5VhTB3dS1XI5y4XyoLWeDVnB2ozUXNjtwIcyJnzY5cjrYjpMb7TkRZMcBP3O2euhS5KBBseMiKt2Xku+oSZTJHBzmTWLGoP707SxDz/ad6NmhM3279kC+R0/ke/aS5hj6de+Guqoq92/f/s1tlW/S/xgEoZ+DIHTzxg1WenoxasRIacm52AfQuKqnI11EMiVcfsvWtBZ1v3htKeYWGvv/It72bN8BnVGD8FswjUSDeRQtV2eH3WIOOi3mkLMmR92XcNRTi+M+uhxfa8ixABNOb7DkTJAl50JsuBjjxfWMjdwvS+JhdSaPtmZxvzyZWzlh3M4K4XpKABei3Pkx1I4zG1dwLsSOC5sdJQjOhjpweqMtx9ev4JC/JfvXLGeXjxE1HrpUuepQ7ryUMuellLhok2Klzjrd2RhMGcVIsQhXlIudOtOzk1imJiacukmzj2Ixjp2FBc8ePfrnBkHo5yC8fvmSspJS1NUWSF5BhAbhCcR+xca5+0YQ2rYQSaOYUWzXCEGLVvRo25pFw5WI1p1HhY02uxyXsdtBi/1OSzjovIRDrloc99bj5Bojjq8x4thaI04EmnFKgLBxBT+FOXIpYQ23C2K4XZLEreIk7lWkcbc8lRsFcVzLi+FW4RZu5UVwL2sjtxJ8uBzlJnmBsyEOnN3kxPlwV34Kc+FMqBPHN9hyYK05270M2OquR6WrLuWuupS56ZPjsJRIEzWc1KYwZ6gS4wYoMFBsyBGlY8dO9OvZS5pjEKEhdP063tbV/R5A+NgIguijv3vP/bt3sbO2ob+8AoOVBtKzq+gpyEgLNsS8fbuWAoJ2dG4nEsYOUidQlGPDunVkndZsah312OuozSE3Pfa7aLNPyFWbAx7LOOxjwCEffY6sMeZEgBmn1i3npxAbLkW6ckF4g6zNPK3N5f7WPO5UpHO3MpMHNbk8313SqJ1FPKhI43r6Ri5GeXI+wo0LEW5cihK5w0qubvHhapwPl6K9+CncjeMb7Ni3xpxdq8zYuWo51Z5GlLnqU+isS66zHhsM1NCdNJypAxUZId8PhZ496Ne9BwqysgxVUpLKx4KsTD78hk93/R+D8O0PlPThg7TtLSosXFrTJw63EN5AJIxi5a8AQczSiTl7ESbEa7sWLZFt25LFI/oTa6JBrZMuO5102OG8jB2uy9jlrsduDz32eRtwcJUBR3yNOBm4nHMbrbkW4cjtWHduJPpyOTWI22UpPN1TwZP923i6fxvPD2zj2b4qHtfmcbckkdv5UdzOi+BG+kZupW3gXuZG7qSv52rcas6FufHjJmdJp0McObrOhv1rLNi9yowd3qZs9zGl1tuMmpWmVK80odzTiGT7ZTirT0F9zFAmKPdHWVaW/rK9UZaTk9YqKvTqSU1ZGfVfzlz6LervA4JIIN+958iBg9IBV+L8gr69ZCVvILyCgEGs5BEwSItBxTq/Dh0Y1bcn1qqTSLLSptrViK0uemx11aPGXZ/tngbs9DJgr48RB1YZcsjXkOP+ppzdaMXVcAfuxLpxK8WP2/nR3KvM4E5VDg+2F3OvtohbVblcL03nUm4sV3JjuFUUz8PKdJ5XpfC8JIaHuZu4mRLIpdhVXIjy4lKMN5djfbgU48W5MFdOBjs0ArHWQvIK21eaUrPSjApPY0rcDSjwMCZ8+SJs501h0fgRjO0vz4j+CgwfMECqGgbJy3Hy8CEafqOlo9DfDQThFRo+fGTtqtVfTh1RkM4wEBCIJeDCQzTuGJKhe6fO9OvalTF9e2A/bwrpjoaUu5tS7mpEhbsB1R6GbPMyZIe3EXtWGbFvlSEHfQ05utaIHzeYcyXMljuxrtxO8eNxRRJ1u4t5truMVwdreHVoGy/3V/PmYDWv91fwco8IDYU82ZbHi5oMnhZFcys9iMsJa7kcv4Ybyf7cTlvH7dRArif4foXhlIBhvQ3HghzY72/D1pVmlLoLEAypXGVOrqsR4ZbauC+dj67KROaMGcm4QQMZKCfHSmcnnty7R8NvND8Q+juC8JHPHxs4d/oMHi5u0vaw/nIKXxNG4R0ECKKKEHMRA/vKMkGuB64LVUhzNKLUY7l0o8vcjajyMPoCgiF7Vhmyb3WjRzjmb8KPQRZc3mzDzWhHbiau4mFxLHV7SnlzbBdvTx/i/Y9HeXvyAG+P7+bNkdpGCKrTeViawIOiGG5nhnIjNYibaUHcyQzmXmawFCZuJPlxZYu3BMHR9dbs8jGhyk2Pak9Dan1M2eaznEpPE0rcDCjzMiHfTYQIfTaba+OlpYbl3FksnaVC947t2bR+Pe/qXvLp23v0G9LfDwSxIVYkj2/fsX3rNtq1aoOSQv8vxm8sIyXvINOVfj17MHKAArOHKuJnsJAsVzNKPMSIEx7BkGoPA7Z5GbDD24DdAgRfQw77mXBi3XLOBltxYbM1lyPtuLbFndvp63hSmcK7Q1t5d3w3747t5NWBKp7vKOBxdSZ3ShK4VRDD/ZIEnlWm8KIiiWelcTwujOJuVig3U9dzIzmAG0lruRq3iguRHlK+cCbEnjOh9hxdb8XOVSZs8zGhwtOIYlc9KrxNKVtpRqHXcjJczYi00sPPYAm22ovp0akDuWlpUn4gdop/e49+K/q7gSB2RYvQIGC4feMmq7y8pbOPxNyDaDAJzyBW+Yql7oPk+zF+0ACWTh7BWn01MlyMKHQ3ptTNgEp3farcdanx1GWHtz67VxlIIBwJMOXEenPOBFvy02YbLoTbcUl0CiNduJEexLNtWbzYU8qDrdncKU2WSsZ7pQk835ZJ3Y4c3h8oo35/Ka+2ZvCgIJrraUFcSvDnenIA9zI38DA7iDupflyO9eJ8hKvUfLq2xY1L0S6c2uTAoQ021K42odhVlxIPA8q9zShfbUm+pzlJ9kZE2RiwxtyASUMHcXjfXhq+HDb67X36reh/DMIv6menj4qSUuyJPHvqjHTglDgVpb8osfrKIttNhn7dZBjSrzcqIwdiqTYFf4N5ZLgYUOBmQKmbLpVuOlS7a1PjsYztK/XYs9qQfWsMOeBnzJF1ZpwIsuB0sBU/htpwMdyBm/Fe3M3eyPOtqbzZX8rLfWW82FtO3YEqXuwrp25PCS/3FPJibzGvdhXwrDKN23lRXMsI4VZWKI8LwnhWEMrTvCBup63hcpwHl7a4cT7KkbPh9hxeb86+wOXsCzDjdLgjuwIsKXbXp2SlCVV+1lT425HjbUmiqwlbXJbjrKXGmeNHqK//yEcJBJEniKTxt5Uv/H1AEGo6hvaLV3jz8hX+vr4oK8ozSEmRAXKyyPfqyrgBiqhPGoup+gwcNaYSunwheR5GFHsYUeKiQ5WbNtXuS6nx0GaHdyMI+9cacyjAlGMbLDgtJozC7bkU7cyNLe7cS/LhbtY67pfG8GRHHnUHKnl5aCsvDlTzfG8Zz/eUULe3hJcHynm1p4SnIlwUxnE9O4I7+VE8KIriYVEk9wrC+Sk5kJ0bncj3MiHL1YB0Zz0izRcSaaHBZuN5FK40ZW+QHVWrTSnyNKRsjQVbNzhR4u9AmpcFcV5WuBov5scfT/CxoZ6PDR/5UP9e0u8CBLEh9i/OJBaJY30DB/fuZeTQQYwcNhAlhd4MkuuJ2thR2CxWw01vAat1ZxO9QpMs52UUuxtS6qJDtZs2W92XUuupwy4fPfb5GnEoYDnHg1ZwKsSas2EOXIx24coWV27Ge3AnYSU309dyuyiC+7XZPNlXztP9VTzZU86TXSU83VUkwSA8hQDhydZsbhclcDNfhI4kKYe4nBdLeaAb0XZGuGnOQnf8EHQnDEV73BC0xw9l6bghGE4ZxfJpI8h01adqrSUVvsspXWPB9hA3toZ4UODvSLSHBZ6Wupy/cJYPnxv48Okj7xre8aH+HR9/YzD8XUGQYBCxUaog6rl97RoL56kyfEh/lBV6MXawAiZzZ7HSSAtfEw1iHZeRZK9Fgbs+ZV4iUdSj2k2HGvelbPdaJuUHwhsc8DPlcOByTmy05ly4I1e2eHAzyZu7qau5m+rLjYwArueHcac6nWcHK3l5bAcvDtfy4qDwCuU8313M812FPNuez6OqTO6VJfN4axa3SlPZGbmOWDc7bBepozF+HBMV+zNJSYnJykpMUlZi2uBBTFVWYvawIcwaPADL2RNIdNSjdp0NVYE2bA91p3bTSqo3eZPmbUegpQlXLl7kI595Tz2vPr7i7cdXfKh/S/3H99R/hUGEjX9cDvF3B6HJM4gauv7tGzJTEmjV/N+ZMHogWvOm46yzmEALA8IcDEl01iVbQOBtQsVKYyrcdSUQtomwsHKZFBYEBAf9G0E4GWzDxWhXbiWv5mF2AI/zg3hStJmHJVHcKo7lZkUKz/aX8uZ4La+O1vLqcA11+8p5trOAJ9uyeVSdwe2SRC5kR3G1KJHDCaFE2JniqaOJnaY6BrNmoDF+LEunTGTOsEHMHT6IGcoKzBioiMrA/sweNgj1UYPx0phK5RoLtm1wYFuwK9s2ebEz2o+8AHc22Flw+foV+FTPy0sXuJFfyP2Tx3jzQcDwc4/wOwBBZMwChE8f3nHu1DGaf/9/mD5xBAaL5uC/wpQoV2sSvVaQ6WVK6erlEgilbvqNHsFdpzE/WKnLXl9DDvibcWSdBSeCbTkX4cr1hFU8zFrH88JQnpdG8nJrEq935/JsZw53t6bzoDaLZ3uKeC6Sxv0VPN9TypMd+TyqTufJ1nQeVadxKiWUvVEB5K12IsBoCWsMtHBeNB+jqeOwnTcdP8PFBBovYb3JEgKNNPHWnofFzPEsHTMI9cHymI/vT76rLrUChI0ukkfYsyWQ4iBvQpwsuXnzIo/PHqPQxY6jzs4cjArn9uWfePfxAx++VhINvwcQGvVJuME3r0iIiUB16jhcLQyIXuVKkq8Lab725PlaUbrGkgofc8o8jKhZacJ2byO2r9Tl8Lrlkhc4GGDO4XUrOBiwgp2+5hzdIOp7F04Gu3I+zp/bxfHcrszgYW0uD2vFFHQKj6pTeVqbzbPdJbw+XMNLqa+Qx4vaDO4Wx3Aoeg2FvraEWywl3FwLP515OKpOYL2OKrluRuxaZ8+BjU4cCnbm6CY3joS6sifQlgInHSK0p7J6pjJhOpMpW23OzlB39kT4cjAhiG3h/kTYm7AtPoj9we6c8LVku6cZcYaLuVZeyqePn/jQ8EmqJurrP31Rw1/dz/8/+tbQ/5V+RRAa+PSxHupFrnAFO3Mj1ns5kha8lowgHzLXuVEQYE95gD1Va6wp9zKj0suYrV4G1HobsNPPnAI3fRKtlxC7Yikhy5fgb6TJSu15uKhPx27OJJwXqBDvbkPGGneOpEXyY1YE1wuiuFUYxaOKRJ7tyOPNoWpeHqjk+fYsnlTEcTVjA7Xr7ChaaUaKnTZRpgvYrK9KrJkalStNOLLRjh83OXAh3InrsZ7cTvDhRpwXlyJcOLHekt0rdckym0ms7mSyHbTYEeTMoZi1HInfQG1UAJGOJoSbLKHc3YjtHrokaavgKN+ZU+EboP4T7+o/8bGhgfqPn2iQ9LcBQeijKFl/wei/pF8ZhAY+icTxwzvy0hKJXLeK7MgN5IT7kxvqQ1GQK1VBrlT721PuY0G5lwk1q0zZ6WdFppM+Pktm4bpYFcMZE1AfP5KxinKMV+7PginjMVs0n6XTJmI8exq26qr4GWhSFujK/rCVXEoJ5Eb6eu4Wx/J8Ry5Pt+fysDyOO7nBXEpczQ5/Cyq8DSnx0CPWRJUsW01qvI3Y5qnHgTXGXNhsz6XN9lyNcORKuANXwh2l91cjnLi42Y79vobkrphPps0iagNsORqzlhMJG9iXGMwWD0sSLfUoczMky0KVPNMFhE3sz6kAFxC7nj7Bh4bPkncQ9+dvCYLQtwb/z/QPAYGPHzhQW0XCpgDy40LJiQokP2IN5SLbDnGnap0j5autqVxlQa3fCoq8LfHUnI3WpPFMGjSY8QMHM1ZJibHKyowbqMz0kUPRUpnIkomjWDJmKAYTR+OuPoPNJhrSHMDxTU5cjnHjZloAj0pjeFASy50cMQW9luuJKzkT5sD21YYUOi0my3oBeXYa5NkupNBuEbWeeuz1MWSbmw6VTkuocdVmv68ZJzbYcDrYnuMbbDgQYEWNtwklHkZsXWPNofBVnEzYyLH0CLL8XEhwNCHVTI2KFQs47G9HmcFMTqyy5PPL57yv/8SHehBEiPvz7b38W+hbo/+S/i4gCEkw/Ez1Eu2NH5YPH7h39SKZsaHkJ2wiL3YdJVvWsTVqDTWbPKle7yR5hWo/Wwq9LXBdpIrWpAlMHjiEYbLyjFUYwOjesswZqIDWSGVMJw3DRmUMVpNHYDFxOLbTx+K3RJU4cy1KPU05usGaC5usuRzrwZ2MQG5lrONakg/XEj25IPY3bLJh91oTip2XkGI+jwidqWzWmkr4shkEL5pA4JzhRCyZRILBLGLE1xaNJ9FwFsWO2mzzNmbH6uVsX2NJ9Spzqldbsi/Eg+MJQRxPC6c02Js0TwvSzRdwJtCay9GrObHSiAsbnfj8+rnkDSQQ3jdIJbboQH57L/9W+tb4vwoIfylB+p/joPAKr58+IjcxivzETeTHbaAiaSPb49dRG+5N9QYnaoOc2b7RjVhbXWYoyTNFaQBj+/Zlqnw/1AYPwGL6OKLMlpDnbEyuvR7xJhrEmy4iaLEKocvmEq4/nyw7PSpWmrJvjQk/rjOT1jNeifXgUqwH5yMduRjjxNkwG46uX84OH30yLeYRungifnNH4TF9CK4qw9hsqEaFnyM7gtzZEejC3nWuFDkaEr54GmGLp5BiNp9CR22qVhpT6W1C1arl7N7gyIm4AE6lh3EgMZiCNbZUexpzPdydm3FruBBgxbkgez48vc/HT/D+KwgCgn8qEIThm7Lgn2fCTR/0kzQbV1OcT1rkekqSQtmVHc3u5GB2b/FjV/hKdoW6Ux7giLv6VNSHKaEzdgjWKiPxWzieVBsNtq4x5eAaM2nvgZgezjDXIGLJZEI0RkojdpPGBJJM55PjuJidPgac8jPjzIYVnN3kwJnNDpwOteHHzY064m9GqZ0GoQtGs2rqQDwmKxGkNY0c7xUUbfAkd8NK1lsuw2POFKK1F5Jnrk/ech0yTRaRaraAFLO5ZK1YQLGLNtU+RuwKsOBkuDNnktdzOiuSIh8LkvVUOOplyIX1dlz2N+fcRgfePbqD5CCl0CBC598XBKFvAfi7g/BRZMN/IQHDBxoa3vJeSpDg5PFTpIQGUhYXzJ6sKA6mb2ZPrD+7wr3ZE+ZFmpspltNGYjdrDGs1p5BkoUaVhzYHA0w5vsGCk4GW7Pe3otDDkEiD+XipDMd6ZD9sxw/Eccpw/BZOIVZ/BmV2i9jvqceJAEtpOvlchAvnxVL2SEdObbTi6Foz0g1n4T9zGF7jlQlaMIUty5fitFCF0f26sVxfm2UaGgzuLYvOdBWclixGTa4XFsPk2awxmYzl8yh2Wsz21UbsXWfJnkBzjmy04URiACdzYkh2NsRlWDdyNMdxwEGLawEWnN3owMs716mvbwwNn7+A0PB3BkGocfLrVwBBGP1bCETC+OLmJc7VFHDzxHE+vXrH4zsPyYvcRFX8JvZmRnMsO4o9sQHsjljF3ggf4ux08dZUIWHFYopctNnuvYwdXkvYu2oZh/3MqHbUIkpnMhuXTSPaVIMIQw3cVcaxVFEWbaW+mI5QxH3CABKWTqXWcQnH/Cz4KcyZqwne3Ezx5VrCSs6HO3JotQlblkxmnepYEvQ0yLczQ0euG+tXmDBusBLf/Z//Q9u2XZg7cx4etvaUJCZgPGUsliMV2bJUhW3u+uz1M+VEqB2nwh05sN6Svf5mHIpdw9nSFAoCXfAY04f46f0p05rEpVVGnFpvw5OrF2io/4yoqqVE+lcCQegfAoLwEJ9f1rF/sx/H7ZZwwNGEH2M2cbGmiszgQIqig9mdtYVjubHsjV/H7ihfdoV5k+KoJ0FQ6aXPbl8DdnnrUOutRb6dOmFLJhGmOZFKdz0OB9tR42tNlY8dpR42xBstwVMkj6MHsmLEINbPHkO+0QwO+RhzMdyV+xmBPCvcxKOc9VyL92T/KiPidWZQ5mbFgXWrCFowg2wnc47lJuFrbc5QOVGmDmVWfwXyvB3Zs96DEns90vRmUrR8PifWi8kvZ06HOXJSLF4JsubIBiv2Rq3ix8osqqMC2aKtQuniCew2nM01X1OOrLXg7rlTovPMx49IVZXUgf2VQBD61UEQVcPn58+oDvKlynYZtY6GbPe2Jt/bhkRfZwqj1rM3W4AQw8GkDeyLWk1tsCtZLvrkuehR5W1Mhac+OXYabNKZxNr5I9ikNwtfvQUsHN0fT4ulzB43lIWTx+NrbkaAjgaRBosI056Du8poXMf0JW7hSHa5a3MxwoUnuRt5XRlDXWmYNFl1NMCUGjddbiYHcyxoJZUuZuxf58zhqLUcit/I1tC17NgcyJ5QX46F+XJ4oxs7fcwotV0kNYmOB1nzY7gzp8OdOBFqz7GNNhxZb0XtJg8O5ieyLyOWClcTavVVOGQyn9v+luzzMeb6icN8rv9MfRMIX3Oov76nfy81NZ1+HRA+1vP54QNWTBiNdocf2Kw+kQL7ZZSvWkHaGmdyN/mxNyee47kxHEkOYn+kD9vW2VLiJdYuGpJpvZhowzl4zhiG9YSBrF0ymwWK3Wnz3Xc0//6PjJ86meETxuMXuI5TBw6Qt94P5+lj8F8wicOi5++iR7bBNA746HM91oO6ok28r0nkZUUk9zLXcibEUkoYL0Z4cX2LP8c3OLHfz4rdkmzY42/PPj9b9q21ZZuXGfm2i8ldoS5tgxMe4FyUG2cinDkTLkpRB2keRHiEnZs8OJSzhWOl2exca88+CzX2Wy7kzkYbdnnqceXIfqm72FhNiTL71wdB6O8GwrfJomgtf378hEiNhbi1a4nX6L54qQwjyc6Egg1rKd8SwY7sRI7mbeFoykYORHhR4WNEmYcumSsWEqU7k7XzJrF8+CCMhg5jTIeuLJupjpOVC106dOcP//Yn6aFbHnYrOL93G4eyk6gUzaq17myPWMuJSG92uGpxbI0RN7d4SiC8rUngVZVYyh7IhQh7Tq0Xmbwd50JdOBxoTZWHLoUOiym01STPQp0sURmYzyfbcgFlzjocWmfL+ZiVXI5fxflYT85FuXI2wkXyDAIG4RX2hThxJC2UAxV5lK60YqvpLPauMuBcoCk73bR5cuFH+FJaCwgaRDL9DwBB6LuGhgb+1qpv+NYjNPC57hU1Hq5kTRxOqtZkEg1V2bh4BqVBqyiODqE6M4nD+QkcS93Iwc0ubPPRkzabip3PMfozCV6sgsvkkej0k0VfSYH1yxZRtdEPf4Ol2M6fReIaH3YmhnM8bTO7Qn2oDnChwNOCXetsORhgyeE1xpxZt5zr0a48zQ3iTU0ib7fGS1PX12KdOb/RgkvBVtyIduVavBfXU9dwKWE1p8PcORnqyukIT87F+HAhbjVXEtZyVSx/j1vNxVhvLsau5EKMJz9FuXEu0pUzYU4cD7Zl3wZbDm7xY39hOlkO+lSbzuKEvwm7XBZxcpMLH54+kg4zF4b4XYAgJYtv3nIhMZbjpovZ66jF+TAX9vhZk+NlQXXcJsrTkziUn8TxtGAObnJk1yqxWHUZFe46FNgvJt1Kg2i9OQSpqxBvsIBC26XsXLmcnatXcCjEm1PxoRwI9ZZ2H6UuX0CiqRrlniYc32jD8fVig6w154MsuRbpzOOs9byuTuTdthSeF23mfoo318NWcGm9Edc2W3Ij1ol7GWt4nLuRh9nBPMgJ5X5eGA8Lw3lYECZtiBG7o+6mbeCumMNIX8d1sctqixfnxeZaAcJGGw6IndRRq9ifl0KuszEnvA3Z76lDrbsOd0oT+PThvTT7KAzxuwBB5AyfPnzg6fYKHq535YK/uXRAxemNVoQvnUqmrzOlybEczk/kRHoIB0Id2OWrz541+uzzM2KfvzG7fA2p9tSh0GEJ2Zbq5FmqU+24lGr7xVRaq1NqPod8k1lkm6hS4rSMg8GunI1by7kod85uduRssI0Ewo1IF55krudlZRLvatN5WhjO/ZTV3Iy04fIGXS6t1+FGmDkv0lfxMieQF9nreVUYxsvSaJ6XRPOsJIpnxVE8L4rkmTiAQ0CSuYGbyWu5Eu8tnb0gStSTwbYcCnbgYOxa9uSmkOtqyn4PPWmOYnuAHW/PHpDu1e8OBOkJL6cOcCfCh/MbVnBqtQE15qo4y3fEfeZ4CmJCpNBwIiOEfSEOUu9/r58xBwJNOLxhOUeDzDkaZCGtIt7ta0Sp42KyrdRIMpxBooEKRfaLpfUJJzZ7cClpnXQoxs2UtVyN9eBimANngyw5v8GcG1HOPEj142VlIvW786Q9DbdT/LkS5cClTcZcCTXi+mYL7sa6UJfhx5v8EN4UhvOmJIY3FQnUlcXzonQLz4uieZIXzoOsYO5lrOdW8lquxntzOcaT8+EunA6153CoE/tj1rArO5E8JwMOOi7ipJ8ph2ID+PziIR8/ff7nBqFBLLaQFlwIIBol4Hh36RQ3EgK5KAxtM5+seQMJGd4T59GK5G5ay+H8eI5nhLI3xIE9/svZs9aYfQHGHFpvxokQS6kd/FOEHRdjXLkQt5KzW1bxY9warmRs5GZeBHcLY7hXvIX7RbE8zA/ncW4QdxK8uLzZVsoBLm604HqEI3eTVvO6KomGw+W8qc3mflYIV2JcuBxuyfUIK25G2nIj3IG7MR48SfXnVe4mPpTG8r4ymdeVqbyqSKauNJ5nhVE8zQ/jUdZG7qX6czPBh6uxXlyMFBtpHTka5sa+mLXsykkizVqLWlMVDrku5lRmJHx4/6XDKsrrJgj+6UD4a9V/auDj7YtcSgriarALx1wXs89mOgcsJpNrOZf80FUcLkzkSHoIe4Id2Bdgya41xuwNFB07a67EuXEn2Zt7qat5kBXIo/xQHhVF87AknicVqTyryuBpZTpPKtJ4KhatVqXysDCKm3GeXNlsw/XNNlwNseKmACFhFa8rE2g4Xs2HfSU8LQzjbsJKbkXYcTPSnltRTtyKcOFOjCcPE9fwMMmPp+nreVEQzcuyFF5XpvGqLJlnBTE8zt7Ew/T13ElcxY1Yd65EuXI+3Jmzm505HuHJvi0B7CpIJ37FUor0JnPQbgE3qnKo//SZdw2fpUUpf4bgdwCC0OcHN/gxbj1n1tlzI8iOi94G3N5gy/aVZmyN9OdISSqH0kLZt9GR/YEr2ONnxmFxCkqUM7eSfXiUsZYn2QE8zQ/mRWk0L6pSqavJ5mVtIXXbi3heW8CLHYW82ltK3bYsHuRulpLDy6EruB5my7VN1tyKcuZeog91JdE0HCqXQHheFMG9pFXcjnbiVrQTNyOduBHuzO1oD+7FreJe/GoeJgfwNCuU5wXRvCpNoK44jsc54TzKDOFBqj+3Yj24Ln5XhBPnNjvyowAhyocDScFsL8whzVGPUuOpbFtpxqtLZ/j4udEbCCOIJXw/h+Ef0Uv4dUGoe8Lp5DB2exryMMKLmwEOvE7eyP419tSGruJYWQYH0zaxL9iJ/eKkkvVWHN8k6nVXbiau5GGaL89y1/OyJJw31cm83ZnH292lvNlTyeu9Vbw5UMOHI9t5d6iGp5Wp3Ez240qYA1c323Ar0oGbkWL7vCv3k1dLxm/Yk8/bXfm8KInmcWYgDxK9uRfvye0Yd65HuHAtwpVb0Z7c2eLD3bjV3E1Yy8O0DTzN3iR5AgHB44wgHqX4cXeLJzejxGltTpwOseNEsCNHo305lBHJ9oJMwm10KHDV5dGhbdDQuDJJGEDML0hzDF/ay40g/Pow/Kog8P4t17eVc9zPkrOrzHgc7c+NYDeO+tqSY2/MkcJUDqSFsSfEiX0h9uwXpV+Y6Nw5cyXWhduJnjzODuBFSQSvt6bxbk8p7w5s5fX+bbw+uJ23R3bx4fge3uyv5GFhDFdjPLkYaiOBcCfaidvRztxL8OJB6lqpWvi4K4v3O7N5WZHAs4LNPM5cx8M0Px4k+3Jny0quhDlzJcyFm9Fe3Izy4FqEG7divHmQ5MfDlEDuJ/txJ34Vt2M8JAiuRThxVnQVA604vN6OAzF+HMqL51BRMge2lfDs6UM+fXzHp4bGmy8M3whC0yqu3wsI9Q3UXbvGyU0rObVmOUdW6nEuyJbj/nbEas9jX1Y8+9M2szvUmX2bndgbYsvhzXaciXLiktjJlOjJg0x/Hhdu5kVVCm8ECIe28+bwbl4f2cPbY3t5c3g7L2qyuS0O1gxz5HyINVfC7KUTVe7EeXI/2ZeH6QE8Kwijfk8+H/aX8UocxlkUxfPcEJ5lB/EsawOP0/y5IUrBYHsuhjpxLdyVi8H2/LTBhkuhYt2iK1fFcTubHbkYasulTXacD7HhaIA5e3xNOSBA2BLA0cJkDpdlcfHUQT5KO5zqpcRZ3PwmEBrby3+uHP7pQahvAD7BjYpMLkWv5Li3DvtW63MrwZdyez22J4WxN20zOze5sifchT2hdhzaZMPpSAcubxEbWby4n+knJYpPy+N5sT2P1/uqeXt4J2+P7uHjsV2821cmJYmXoj2lI/NE6Xgt2o07CT7cSVzNvVQ/HmWup64onI8HSnl3YievanN5VryFZzmbqMsL5WVuCC+yN3I3fjVn1ttyIsCaa2Gu/LTBmsPeBhzw0uWorwmn11vxo1jsErSCc8HWnN5gxT5fE2p9TNgX5MC+GH/2Z8WxuzCdM0d280Fsavn4iXcfGtcENBpeqMkgTbOPv/6cw68LwqfP1H/6xIfH1ziXtIEHISKpsuNmiidH1pmy2cWYvVmR7IvzZWeEG/vDHTkSuoJTm6y4IEq6Lc7cSV7Jg+z1PC6K5ElZAk+rM3i+vYDnO0t4tS2DZ/kh0jmLp4NtOeq/nAvhLtxJXiN5gftp/lKZ9ygriLqiMF4druDj7fO83FnF66psnhdG8yw7hMcpATyI9+VSqCu73A3Z6qjDtXB3rm5y4sQaU8pXzKPIfA4VtgupdlzE3pW6HBC7tH2NqfEyYJuvOXuCXdkRvobaxDC2ZiXyowD14yvefxAgfCmpf8Eg/yj9SiB8klQvyqVP4v076n46yJmolVyOduNmtBPH/I2IcdBld3ok+zIi2RHtzf5wZ44Er+BYkBnnQsUROZbciLLljhQiAniQE8yDvDDuF0RyJzeMG6mBXIn14ly4MyeDbNjjY8iPIfbcjPfmYapI9NbwIHWNlAfUZQfxujyOD7sLeFWVTn11Jq8LtnAzbh0/bV7JkUBHSh30SdSdzU4vE65GeHE+xImT61awx9uYImsN4pdNIU57InmW8yhzXEyFqw5l7vrsXO/AjmB3igNdKQ3zpzghgrPH9lFf/0aC4N3Hxo7in/ssjR7iW+P8mvoVQBCG/yypEYTPvP/8GerruLI9m9Prrbmyzpxj3nokWWtREbmO/YUZ7IrzZ1+YG4ekuf3lnA4y5cJGUy6FmnEtwobbce7cSvThVpKv1Oe/GOPBWXFqargLl6LFe2d2eOlzyM+ci5uduR3jxc0tXlyL8eC66A/E+vAkbhXPktbyMGUddVnh3IgI5MA6T9IstVk9cyQOoxUI05zKsQ2O/BTmwalgZ05vcuVEqJs0PS2mt2N1VYhcOolkE1UyViwk31WfHSHuVG5wI8PHlpz1K8mJCOb8yUPSAHgnQkP9J94LAKTGm3j9ol8w0K+lfwgIQvWfPvDw+mlOr1vB9dXGXBNby530KVrvze7CLLZHB7Iv3Jt9QU7sC7TksL8xJ/0NOLveiPPB5lzcZMOFMHsuhDlybpM9p0WMDrbmfLgTF6Pc+HGzE1XOWkRpjKLASJVaq8VU2C6hwl6LHS56nPIw5cJqUy76mXAlwJqr6zzY7WqDp9p0bKaPwmnacIIWT6PQUZf9ATbsWbuC7astqfaxIMdBjzQbbZKtlpBorkG8mRpp1otIshLH/hiyNdiDjJXWbHE2I36lHfnRm7l27hT1De95+/ETb38Owjet+G8N9GvpVwVBhIX3n0WIENca+PT+Nbe357N/jS0HnIyJNNCkKCSArRnx1CaEUxm8ipr1Lmxda8E2b112r1zKgVXaHF6jxxFx9G6gGccCl3PYz5QDa4w4uNaYYxuspBVDR9ZZcdDfnEzTmZjKtWNGqz8wovW/M06mBUsG92X1rEmk6KpSbDKLUtMFxGmrYjS4L5N7tMN4wkCC9edKx/dsXW1Bja85RW4GpNtqEWOqzkad2QTrzmGjrirRZupEmaqR5qBDos0SMtxNyPJeQYDBAoLMlkgLbwpjw7h35Sca6t/93kFolABBHBjRdP2T1Ft4ztszh7iZm8rDQ/t5cfEsJQnh1KbGUhDkQ5G/E0Wrl1Pkpk2ZkwbVLovY5qrJDo+l7F2lz/41Ruzy1mObx1JqvZaxd40Je9aasHuNCfuk+QpT4swWYj9zIjOV+zCiR3v6/fB/GPDdd6h3b4X9qN7Yj5RDvXc7xrT/E3MVe+C9eCZxNkspX2tFpZ8VWc46bLFUJ8ZikQRIqLEGQQYLWLt0JmFmC9ioP5twkwVEW2qS4mJMmKUWnhrT2GCiSXaAB4Vx4Ty5fZWGj28lEL6Ghr+A4J8ehD9LmpVs+HPNLC1vr/8gtVjF5g5RU799+oCK9C0URK8jeY0DyV4WZHgZk+G4hLTlqmSYzabIZgEVjouocllMpbMmRXbq5NuqUe62lBpvA6q8dKlaqUelly6V3gaUeuqT56pHsq02m40W4DprLMuUZVHp2Y4JvdoxQbYTk3t1RK1/Txynj2ej7gKirJaQ4KJLgssyYh20SHTRI9xSkxATdVZpzsBFbRJr9OYQaaNJmLk6Gw3mEWWlRZyDPmuWzmSdvhqRNvqkrXGlMDmausd3afj4jrcfGqRtbn8Nwe8IhAapo9ZUO/8ZBKmj1tRde/uKfVVFpAWvImmNPVGO+mxx0CbBRoNYw5kkm84lz2YRhXaa5Nmok2Exj5Tl80ixXECSuTpxZguINVUjbvkCtpjOJ9lqIanWmmTYL6PAw5wCzxUkWhuwQWcBtirj0BrWn4WD5Vk8RAGTsYNZozGLOKtlpDkakmKvR4qtDslWWiRbLiXaSJ2N2qp4z5vO6oUz2GSiQcyKJWw2UydIfx5JzkZEWi1l9WIV1uvPJ8Jan4KQtVQVZfDm1RPqxYzje7FGUBhdLGP/vYIgeYKfT678NQhiSe/VsydIDvImzc+JSFtdolZoEm2+gBiTuSRZLCTFQp1U8wWkWS4kwWw+McZz2aQ7S9rQ4j1vIr4LpxK4eIb0TKYIw7lEGs0jxnQhW8yXEG+pQ8zypYToqeOrMYvVmnNYu3QBAUsXEGuhR22gB8fC/TiwwYsdqx2pdLeixHk5xc7LSbExxmHGRHQG9sd81EB81SYRuHg6G3Rms8lkIcnOJqzTnUug7lw26M8nys6YvBA/ivKTef/+JfXiwPL34uhBMdn0LxD+Wj8D4XNDAy8fPyQnch3pa52IXqFD1HINgnVnEmU6n0wXA+IsFxG3fCHRxmps0p3NJj1VNuio4jlvEvbTRuMyawKr1adJSV2Y4TyiTNTYYqFJkM4sku30KPS2ZneoD0di13EyIZQf4zdzdksoF+NC+THEl4Pe9ux1tmKrlRFZhouJ1V1A4CIVNEcOpfMfmjOhVy/sZk4kSGsm6xZNJ1hnLpHLtdhkuojAZXMkEIIM1UnxsiU50JtjJ/bQ8PnLg03eic/auCXwXyB8q5+B8Ensgfjwnr2lWWSs8yDBxZR4Ky2CtWcQuGQqkTZL2GyqRpTRfMIM5rBRZyYhenMIWDoLV9WJ2EwZje2UsfioTSNIR5XNxvOJNVMn3XIJ2RZa7FvlwIlAD86sX8m5jb5cjdjIjZgwTgSspnSFMcna84lYNIOghdMI0ZlDiMECYm31MR2vxKS2P2A0WImUFdrkOesTZ76QMAM1NhmqE2G+FH9tVTboq7Febz6hpotJ9LQla1Mgj5/dpuHzx0YI3jXuCv8XCD/LEX5J4hQ2PjVw9cxRkjd4s8V1OVFmi4kwVMNn/jhcFoxlvb4qEYbziTBRI0R/DkG6qvioT8FBZTTm44axbOQgrGeOx0tDhYBlcwg3WEDa8qVkmCwhaek84hfNIlJtGpvmTWPj3OlsmDMDv9nT8J0znS0rDMlZ7Sxl+8cq0inbvJZEO1Pmy7RmtcpYqpxMORpgyXZfMxKtNIkxXyxVEZvMlrBOV42AZfMIM9cm3EqPjXambC/Jpv7zW+obBAj11L9v3BX+1xD8jkAQ5eInsZD1GzVOujTNun3g06d6njx5ROaWzSSsspfcbKjefPw1p+MyfxxrFk8nTH8+G3RmsnrhJPyXziBAaxYh+urYTh/NmD6dUejYnMEybZjYuS3ain1xmzIey5FKuKuMxWPWBDwWqxDiYkrcBk+yIteTHR3CvuoSTp45xMVzJ8nYEs2WhAjs56kSP28B+QsXs8PelEPrrDmzyZEq7+WEizJSfwFBRhqEmGmxTn8hftrzCTJaTNgKQwI9bTl79Qz1NFD/oaERBHFo1l8B8C8QGiWqCTFimmBo+MDH+g/sqiwixteZMFt91unMIUhblVWLVFgxbRj+unMJ0J7JqoWT8Fuigq/GNAIWz2SV2mTWLZuLu9pkVHt3QPG77xjR4g94ai3A12ApyYE+FCaGU5wazZ2rZ7h/+zKPrl/mwoGDXDl8nCM7dxMfvhmdWSqsmTWPuPmL2GNtR7WlOft87NgXYMXxEEfS7ZYRoKPGWu15bDBezEYzLfx11fHWnIWv9jw2muuSERXKqzev+Cw8wDvx7Kv/GwT/AuGvQRBe4XMDV8+fJjrQmwg3S/yWzcNfcya+mrOwmj4C61mj8NeZxdol01mrOY0N2qoELJombY8vcDRgq481hc5mhGsvxHBAP9xVVfCYPw/XpUvIio5ghZEelcX57NtRS0VuLs5Gy3HWMSbadRXFwZvZFhTMcTtPTtq6stfWjqIVpuxcbcuutWLbuy2Rppq4q89k1ZK5bDDRIshMCz89dby15rBaV51gu+Uc2lrFp4+f+fzhcyMI/1dv8C8QJBAaGj7+lV6+eEpOchy+1kasNVwkHYezevEsVmnNxmLqMFYunCIliUHLVAnTn0eM4QKil6lS4WJMtasJ25xN2O9sQbXOUrYt0SdFVZPAiark2HsR5upJWnAoJfEJbC/I48cdO/ipciu3ckq5uiGG294bubLKjyNOztTYWFG72lXav7g3wIZiTzMCl6lhPWsya5YtINhcmwDjRbhpzmCtyWJ8zZaRvimYNw8fw/vPfBTrD75UCr+cJP4LhP8rCMIz/HjqOBs9HdiwwgAfzVl4L5yO9yIVXBdMRn/CYFwWTsN70TQCtWaycelMQpaokG+vx06fFVTa6VFrbUCZvhZVBvrsNrdhr4UDR+1WcsTOm+NrN3IkNJyjIeGcCY7kyqY4roXFczcsjheR8dz2DWCnhQWVtpZUr3Sk2suS/YF2ZNjrE6irjs3syfjpLWTD8qX46KnhpTufVSZarHNcwZ2rN/j88bOUHP4cgn+B8F+C8Av61MCHj+8pyYhn1XIdvHXmsXrJLNzVJuG9ZCaGU0egN2kILgsmN3bzdGbjrzGFhOUaVLibstNrOXnGCyk2W0qOjgZlOjocsrDhupsvr3w38WpTHG9SsngYEsWLjdG8WB/J49BoTnl4s9/Onm0GhpQY6lJgZUiBg3hqywpp+jneYilrtObhME8Ff0NN1ptr4649B18zLdyMFnNke6UofHj//hMfpAOyhIH/Un8Nwd/usM3/P/ptgPCfqP7zZ95/quf+zfNs8LDGWUsVZ7Up2M8eg/3ccVjMHInBpEEYTBzEilkjJc/gv2QGwctUSbXSpMR5Gbl2WuRZaZFjsIhszQUUaWlRrW/EfitbfnRbyeWVa7jm6sM1B3cuWzty1MiU7bq6FGhpkrR4Pgna84lbNo+sFTpUrzSn2ns5CSv0WKe/GO+lC9loqU+AmQ7uOmqsNtMmPmQN79484cOHj7z/UM/7L4du/+WN/0sP8ddnTf36+o2D8Ik3DR+4c+sisRt9cdRVZ8X8KZjPHI3lrFGYqYzAeNowDCcPQW/CQKxmjMZDfRq+i6YRbbKADJvFpNtokrFiMekWS0jW12CLxlySF6mTuXgxeVra1Jias9vChq0mZhQZ6JG4dCFBC6azVngZ9YkEaE5hi5kaRe4G0iN7yn3MibPWw1dHHT/TZUS6WbPBxohAKwN8jJZSnpXAuw/PqK9/L7WUxYHk4km5Tc+Ebrzx/wLhr4z9n+mzNG3dwNW7N0hPj2O1mzUey3UxmTMJw2kjMZk+Ev3JQzGYMgyDKUNZNEIRnbGDsJ87GW+N6Ww2nE+ChTopKzRIttIgzkydLcYLCV40k4A5UwhQnYLP5DF4TxiD98TxOIwexvLhShiMVEBnRB+MJyjiqDqCDbozyHZZRsVqE0pXGlHkZUaUhTYrteYRaGVEhKc9gTYmrDLWJnC5IWH+XvitW8m508f58OaVtPlXNMn+EoZ/gfBXBv/P9LnhM3WvX3Hh+kWys5JYpDoVf0dLLNVnojdlJDoThqA+QhHN0Upojx+I2jBFNEcps2L2JFznTyFAawYJFhpk2i0m03YxqVaLCFumymr1SdjOHonulP4sHifH3ME9mKncg+lKPZiq1AO1EXKYTB6Km+p4QpfOJsdumXSYpjimt3q1OYWey4mxWoa/4WI2u1qzzskSp2WLsFZTJcJmBWscLendpxOR6/05c+gAr54/bXye05fHGkkPMpHyhm8h+BcIvyixcOVpXR3X792iojSfGWOG4qyvReAKU3Qnj0Jz7EBUhymwcNQAlk0czOKxA1k6djDLVcZiM3s8rnMnsMlQTWoDp1ovIc9JnyxrXaL01FinoYKryijsJg3FctwQjEcPRm/EIAzGDMVq6lhcZk0kaOk8ClzM2LbWju3r7Ni2wZrCVWbE2y6Tuoj+pjpsdrfDwUCT5WozcVCfS4ytDc5GOnRo/z0mixYQ4+/DkW0lvHhwS3pEQVOo+BcI/xkI0tfq+VwvpqEboOETb8VT5+/d4+6j++yqrWLW2BEsHDcCXxM9DKaPQ3WYPJOV+zB7mDyLxw1kydhGLZswBIuZ47CeOVYqM0PEOgGxnsBiMTk2ehQ5mpJuqk283iI2L56H7+yprFWbjfusqayYMAqP2dMJ1V9Mnrs1O4O82BHkxq5N4vBPGzI8TYhYoU3Qcm2CbU3Z4GiBodo0jIUXWjSPeIcVOOhq0qVjCyYqKWI8T4VQDxtqC9N5cPMK9e/FI/+a9jH8Unj4HYPQ+LXGPsJncbyYgOH9R67dvMm9J48lEM6eOo7mrOkM7dYJ3SljUR+pzCTFHih1a8egnh0Yr9iduUP6oTlWGfVR/dGZMBjruZOwVZ1A4LK5RJlpkONsSIW3FaWu5pQ6m1PpbkOFpy2Fbtbke9iS4rCcBFsTMtysKVztxO6oAPZuWUepvxPl/k7kelkRY61LqLk2UY7mBNuZsHz+FHRnj8Ny/hT89DXI9LLCRG0KfXp1YmAPGSb3l8Nw9jQ2OFtRkZXMnWtXpMUpjYtyhAG+heFfIHwF4VP9R54/e8a9Bw949PQpjx4/4vbVy6y0W4F821aoKMujotSPifI9UZRpS68OLenVoRXyXdowWr4Hc0crs3C0EnpTRmAzdzI+S1VZpzePeGsd8jxE1r+CfGcTanwdORDqS806D2pCfaja6ElFoCvlvk6U+tizbZ04HNyTbI8VZLpbEWdnyDqDhfgbLCLIQg8PHTV0pwzHVHUCdgumEWCoQYaXFYumDKerTDuUesgwuIcMExX6YKY2gxBPB2oKMnhw6zof3739MhPb1Ff4FwhfvvZnEMTjba7ev8vjp8959uw5z54+4eHNG2wryGNQdxmGde/CFMU+TFGQZVivLvTt2JourZvTqeX39GjXnMF9ZJgxoj8a44egM3kYptNHYTVjFJ4Lp7DFZikF3uaUrlpBqfcKqvwcyV9pSf4aG0oCHChda0eeqxmZ9qI6sCbLzYIoCx3CLJYRZLoEH515+Oips9pwEcbTR2E8fTTms8bjtmgmIcuXkuphwcwR/enYsTVyMp3pL9OZYb26ozpMGQ9jbZKD17C7qpSn9+9KDwwXay8a84UmGP4FwlcQHtS94OCVS7yoe8XLZ8958fgRz+7c4eDWrSzX1KTHn/6DCX16Mk2+N5P7dWd4j07IdWxDt7Yt6NjqB9q2+CMy7VswULYL0wb3Y+FoZSmZtJw5ilWi2SSWrVkuJMVOhzR7XZJslpLrYUquu4n0cJB0Oz3S7PVJczIi0lKLIJNFrDNZjLu2KnaLpuOiMxfzuRNYOlaZ5TPGYjVzAl6aqmy21GWLozGTB/ejTbuWdG/XBvkunRjcszsTFOVZvmA2iYEryYwKJis+mmsXfgLx2b8syvkdgVD/JSFs+As1wtAIwscP77j06D5bf/qR56/e8ObFS+oeP+biyZPEbNiA2/Ll9Gr+A6N7dmWKbDdmyvVgWr/ujO7RCaXO7ejRtjntW/+Jdi3/SLuWf6B7hxYM7CPD9CHyLBk/WGo+Oc4bx5ql0wk1nkuMxUJiLTRIc9AlxUGXRHtdwpdrEGQ4nzDzxawzUMN7ySycNVQwUx2DyZxxmM6dwIIRchhMHY65yhjpORGrl6iy0Xgxmyy0Ga3Qg2Ytf6D9Dz/Qu0N7BvbqwSj5vqiOGspmL2fW2ppitFCVpMhQXj9/2gjDe7GCu+npLf8YGH5lEMSo/yCNfFEh/BwEMcH09t0bTt+9SeGPJ3j66jXvX73h3rXrFKSmEb1hAwkhISyaOpUBbVsxuWdn5in0ZL5iL2bLdWdCzy4od2mDbMcWyLRpRrvmf6Rt8z/S/A/f0bN9c8bIdWfmoN4sGa2A8UQlPNTHE2Qwh1CjuYSbqhNsMI8gIzX89eayRmc2Afrz8dKcgcO8yZjPHM+yycPQmzqC2Uq9WDZ+IObTRmI5ZTiuquPxWzKLQJ25rDNQZ3ivzrT84Xta/+GPdG7RHMXu3RihIMcYBTmWqkxl6bSxmC6cjae1GSf37ZEeYiJOX214/+clbI0w/Hf01wb9/6tfF4RP7/j86TWfG95+SQzFxhfxh4iu2ztev3nF2cf3SD26n9uPH/H47j0O7dhJZU4+5RlZZMdsYYPXSqb2l2eiTDvU+nVnkXxPlir3QXOQLLMVujO2R0eUOralb9vWdGvRnI7Nv6d9sz/QvV1zhvbtgsqgPmiOUsRkyhCc5k/Aa9EUfBZPY6WmCi5qk7FTHY/T3Im4zp8sLXczmDCcJSMGsnDwADQGKrBs2ADMxw7GfvIIXKePZuXc8QRqTiNAcxqeC6YwpU9PujVvTZs//ImW//4fdG7ZkmEKcoxWVERjymTmjBmG4fyZOBvqULAlhtcPH0lHBTQIHoTEjOVflZX/mf52MPxqIEiHQ3yqlw7VaqhHkmgZND4q9wPv3r/m7auXXHzykPg92zlz9TL7d+9mb3UNW/MKKUnLIGlzGKnhUTgs02Zk+5bM6dWZJQo90R8sh/5wOZYNk0NzYB9m9u3OKJkOKHVoS+/WLeja4ge6tWpOz7YtUO7ekfEKPZmp2JOFwxXQnjAEo6mjsJw1AcuZ4zGZOhrjyaMwnDiCJSOHMH9Qf2YpyjG9X1/mK/Zh+djB2E4aJjWjXFVG4jl7ND7zxuE9bxwOM0YzrW8P+rZpR8cfmtPyP/6D77/7jv4iPPRXZMUyHZYvWojurOlYL9EgPsCPy6dO0SD2g355gIfY7/DXBv/P9L8ChMb9jU2STlX71MB7MbXcQOMHf99A3fOHPHp4m7q6Z7ype8Gdly8oPHGEDbGR1Gzdyt7KrZRmZJMTl0BccCiZsXEkBYdgNm82an27oTOgN/qD+mA2UqFRoxQxHK6IhpKsFD6GdG6HQrvW9GnTmp6tW0lQyLZtiVKH1gzt2oHRfboxWUGWOYP7s2CYEvMGKzJHWZ5Z/fsxXa4PKvJ9JBBUFeVYPFAeq/FDsJ80FIfJQ3GcImAYgcesUbjNHInlpCFM79sDhbbtkGnegrZ/+hPf/5/v6NyqOQNle6E1ayZ+To6Yay7EbKEa6xzsqSrIkzyhgOGvDf1f6TcPQtPG1z9fEyB84APvec+LhgbuPXnBuZOn2VuYyvnDO3lZ95z3r19x+/Fj1iclMkt3CZm52ewsKacsI1tSTlwi6VGxlGTlEBsczLJhShgM6oPVWCXsxinjOH4gDmMHYDt2AJajB6A7uJ/kHUYK79C+LfLt2tGvXQd6tGhF91at6NuxPX3bt0WhYwcGde3C8B7dGNmjK6N6dGVMj25M6N2dmXK9WTRIHt0R/TEaqYTF2EHYTBgsPSHGdvwgnKYOw23GSJymD8d03GBm9OuFcscO9GzZio4//ECrP/4Hrf/4HwyT74fG9GkEuLmwxskBo4ULcDMxJjlyMz+ePMydq5d48ehB4ySVZOBvjf5L+l8Bgtjk2rTIpPH6m/dvuHLtAvvLszieG8uzHbk83lHA/ZMHqHvxnAe3b7Nz9x6c16xlxPwZrA8LoTwzR8oRhPITU0mL2kJWXCKFKalE+bhjNmUkthMH4TF1GD7ThuMzdQhuk5VxnKCE9dgBGAxTQE2hFxO6dmRE544M7dxJMpRC+3YoCAg6tEWxfWsGdGjDwE7tGNqlPaO6dZSSzxl9u7NQSRb9EQpYjB+I1fiBrBinjP3EwThOHobNhEGSV3CeNgyHqUMxHqPELLmeDOncUfJA3Vq1pN0Pf6JDsz+h2E0GYw0NQn18iI/YhJamGtZGeiRuWs/h8gKulpfyY0khe3LSeXz6OJ9fPJOeC9n0VJe/VJMBG5PGj9JqrsZrf/Fkvf8H/R1AaNr9/FnatSTKoo9v63nz4jnnjx/h+NYKDqaEkeNlyqHoldzIi+NkaS7Hjx5h19atbKvcSkhYBMPnz8DOy5Xy7DxJNfnF5MYlExsaRnpsHGXpaVQlhBNprYf7tCF4Tx9CgOpI1s0ZhZ/qCLynD8Z1ovAUAzAdIY9m/16o9OzCBJmOjOrcnqEd2zGkU3uGdBFqy5DObRnSqQ1DO7VhlEx7KazMle+G9iBZzEbJYzNBGbsJyjhMGoTTpME4TBqMzYSBOEwW3kEkj4MwHTuAOQo9GdyxHf3atKJn65b0at+aXu1a0q3F98wfP4649evJSk/Cyc0WjxUmbN0Syot91dwpKuBE4haOJsWQaGNFiqszB7MyuXfhR94/f8KndyLB/rJVUBheSrIbQfgggdDYjxAntDWd0vatsf9v+puDIM5JavgEn8ShKO8/8vjOE66evsaFQwd4sH8nTyqK+DE9jvg1jtiZqXMgdhOniguoKC5md/VWagpLyU3NZPLiecxZqkFGYjK7KrZSmZFLWXIGWSlppMZvoTw9gRslSdzPiyLHchG+M5UIUh9FyMKxhKiPZf3ckfjNHoaPijIuk/pjOaIfy5R6oSYrg0q3jkzs0p5xMu0Y270947p3YEy39oyUacfwzm0Z1bkNk7t3YIFCNwyH98Nm/ACcJw/EaZIyjhOVcZo4EMeJA7GboCRB4DR1MPaTlLEcP4D5/XsxuFM7FNu3oW/blsh3bscAmXYMlGlH//atSQ4OprAgi9DwdQTZGPHTlnW8L07gVEYyDlMm4jNtCoHTVQgcN5H106azSkedMC9XagsLuPTTGepePqWej9R/atodJvKuRhCkbPPDbwaExgMgnj2v4/KFqxys3c6R8iJe76vhdlkee7fEkm7rgNv8eaiNGITx5EnErvGlqqiAXVVV1BSWUFVQgpmzLdPV57J582bKc/Koys2nIjub2uxc9iRt4U5hEh9KkniRn8zl1DhiDNQI15xM1MJxRC6cQNSiKYSqTWC96kjWzhyGx+SBWI9SwGhwHzTlujGrZyfJ2OOFenSUYBjbrT2jZNpJIEzs2k4CwXiEHLYTlHASIExUxmG80p81QRmXqYNxmiLyBiUsxyuhodyHkV3aM6hTe6mMHdK1AyO7t2N87450/e471tiuYGtpEUVFOcyYOITCdd6cT4jmalkxvrpLUendndl9ZZnVsxfmU6YSaGuFk/4y9GbNRn/GTMJX+XL7p/OND1xvqOeDdMJ7o1f42CCOGBAg/L8/WvhvBoJoH4u49PbdW+4/eMDRI0c5uHsfh0ryuV6aTsO+Srav80Vn1Fhmd+jOvJ5yTOjeG7VhIymMi2NbcRHby8vYVVbB9uJyvFb7MHHuLMyc7KguLKIsI42SlHgOZ6RzKz+Vt6VJPM5L4mJyLHuCAzkc5EmK7mySlkwlYfFUErRUiFk8nYiFkwmeNwY/8Si/KQOxH6uI2bC+aCn2YPaXcxEm9uzAuG7tGN+tfSMYXdsxtUcH1IVHGNaXFWMVsZ8wAIfxA7Ab2196FTmI8A6uUwbhOEnkDgOwHKfE0iFyjO/anqEi3+jcjlHdOjChRxtmyHVG/vvvcDfVZ3tRIRv9/emj1JNNzpbczUvnXm01ZdERjO3Xh3GyfZgip8BUxQFErllDTVYWGaGbCLCyxmDGbCw0FpMeHcO9syf5/PQen9+IvOET7wUYAgDRmJKaU/99GP5bIPzSHMG3+vj+Pffv3uXE8RMcOniAmsoKtpcUcK00m/rKTO5kxRC0TAutoWNYqDiEWX0HMFFWgdlDRpAZHkVVTi6Hdu5ge3Ep2/KLiAgNZZqGGpN1l5CTmkJ5chz7cxJ5UlnI64oMnpcns78gn50p8VzLi+Ft7mYubHQmQWs6SVrTSNGZRdwSFaI1pxKhMZGQeSMJmDUUn2kDcRrfGCq0lHswZ4BoVcswpXdHJvXswNRenZjWqyOz+3RGU6kHRsP7YjVGHrsJA7Af31+S4/gBOAkvMUkZ18kidxiA+RhFLMb1x2CkItN6dmRUl3aMFiGoVydUZNugKteesTI/sNralL35BdgaGtNv4hBWmSzgWtRaLhZmsb8wn0B3D8Z26c3UXgqM79kP4/kLKU5OoTwzg/KMdLIiI0lat4ENNg4kmGpxeqMnF8t38e7hG+mxge9FWS7K86/9iP8eDF9BkEb0F4nGT4OQ6AZKreDGeYK/0JedzZ/qP/Cq7jnnfzrL9toaduzYwbaSUnZnZ3O7NIs3Nfm8qikk2cMD7THjWDRkOHPl+6MiK8dkWTnUxo4jNyaWgoREju7bQ1VBITW5BZRl5bDQSA/FBTPwXuXJ4dx06qoKeV2Zya2tmewtjOdoegyPCxP5WBDDm9JU7hWnU7zGnQitaWzRUSFOdyZxOipELZ5E+KJxBKuNwn/2MFapDMF14kBMR/Vj6fDeLBrckzkKMkyX7SxpRp/OzJXrymLl3ugN6YPJ8L5YjJLHeoyC5BGcxg/AZZIyzhOFlLAeo4jZSHksxyqyfJwys/t0YXy3jkzq1QWVvl2YI9eBhQNkmNm3Aw66i6Rqx9/Jhglzp+JoqEXkciN2pCSzt6iI4oREzGbPZVS3HpJHUJVXJszDi8LkRKpysqjOzqYqM5NtWdkUR28i1sseT90l5K5y4e3R3TQ8ecIncQaDdOD3fw+CryB8HfEChE9NHcCP0tzAl74nn7/MEwjDS/ooll295dGDOxw9vJ/qylLystLYlpPB6ZICHtdW8X5rCQ/TtlBub4X+kFGoKQ1FbcgwZvSTZ2rvPkzt048ZgweRHLKR1IgwdlRXs6uqhu2F5RQmpuG5yofR82diYaDBjbJMPpZlc7c8k9MVmRyvyaOuNpcP5cnU5cZwtTibvekp1CTEkeVhRbypOtE6KsQsmUzMkkmEa4xj04IxBM0bhd+s4ayeMRzHSUpYjlPAdFRfdAb3Qr1/N+bKyzBXTgZ1xe5oD5RFf0gfDIf0wXRYX6xGymM/tj8uE5VxmzQQV5E4jlfCerQi5iPksRnbXyopNeS7MqV7J6b37oKqnAwL5DqhOaArmkP6oDlpBO7G2kQFeDNl4UwsjJZhPEOFvOho9pWUsCMnh6QAfzSVFDDu3w8nZWXiTI05XZDNj9XFHMhPZ1dGAnszEjlclMmBonTcDNTw0pxMjLU2cWtXcbC6iid3blP/7o1kK2nD0DfH+jad7dh0pN93AoCmmUDxjR8/vufdR3Ee4Fs+fHhDw7tX0nE2H96+4v3rl7x99YI3L5/zuu4ZL58/5sHdG9y9dY0rF89yeOdW7pfn0LAtnwfpkeQ4uRExez4Z4ycQPG4yy+SVmTlwKFP6KjKtnzyqA/ozTb4faaFBJIWHsKN6Gwd3HGJ/1T7KUrMpikvB28ycUEt1LiSs4U1JEnuLCzhbW8XrPZU01GbxoiaLQ1nx5KemUJqTR0lKGkURm8nytCXZeAHx2ipEaYwnUmMcmxeMZePcMQSqChhG4KsyDJ+pg3Efr4jN6H4YD5dl2aDeaA3ogbZyTwkAk2H9MBsuh8UIeWzH9Md5gjIekwfjOXkw7qJ6GDsAm1EK2IxSxG3SILymDcdiuCJzZbsys3cn5sl1QVNORvqZmoNlMVs4HR9rM9zsreg7eQgrHMwwVptFqI8X+8tL2ZqZzs7sLDK01ckf2pfCwX3InDCcd9nJNByr5d3+Ut7tK6Z+TyEfdhZQt7uIrblhuLnokZkaSpiXE666S7HX0aIwLYl7Ny/TUP+az/VvqX//hvqPH/ggSk4hcfCnpHq+kx4cICZ/PovNGG959fI5L149p+7VM16+fMLruke8fPaAF0/v8/yJeH34Mz3guXT9nqSnD27x6vp5Dq6258CCcTgNH4aDYn8SxgwjR3UqvoOVcBw6BJ1+fZjboytz+8oytUsXKjcFUx0dxp7CQo7U7uBwzXYOZGRxMTuNo5v9CDCcR4SbBbvS47lWnsHrHfm83ZbFi91FnK8uoCQ1mbToWDJi4qjIyKYoMYWyyM3EWRuSaKQmPRI4YclEohdNIEx9PMHzx7Fx3ljWzxmN/4wR+Io+xJSBuE0UQAij98V8RD9WjJbHZoyCBIDwBBIEU4bgNXUonlOG4DZeGfvRilgPl5OAWDV9GL4zR+IyYRBLFXszt29n5st3QUe+B8sUe7FAsRfh61bhZWmGk5kRCjNH4BfgToCVCZrTJnOyOIerxek82VbMEX8PvJVl8ZkwGuP+/clyd+P69mpubCvnZnUxj/Zs5dn+HTzcV8uDYzspiQqgItqfp4equFiWRn6AO5tWGONnrENNQjSvrl+Cd6/5KM5x+vjxixqTSwmEz69e8fDMMQ4VpbGnIJ0jtVs5f+oojx7e4vGTu7x4dIvnD2/x7NEdnj26y/PH93nx5AF1TSA8ufuXenSHexXZHDJVJ2XhHCx7dmPVUGVyZo0lb8Y4slVnsHnccPyGK7N6xEAcFWV5nJfG3dJcjqYmcra8hGt7dlB3cC/vdpZzJzsGZz11ls6dTFVKBA0HivmwNY3HlemcKsmkOiudgvQschKSKU7NoDQti8qcAgrj4skKXEuUsSYp+rNJ155E/OKJRGlMIGzhBELmj5MUPG8sQaIRNWsY/jOGsGr6YNwmDcB54gCcJja+Ok8YgNvkgXhMGYTX1MF4TR2Cx6RBOI/tj+3wftiPlGfllCEEzB5FwKyRrJkxEutRSixS6IqafBf0B8ii3luG1QY6lBflsdbJDncTXUZOHkxKmD+5/j4M69WDXalbOJ+bxONtxdwoSGde+xbo9+yKUa/ueE4ax7n8VC4U53A2L4trVRVcr6nkanUpV0vzOZW+hWCjRRxI2sj9PYW8OFJF3f5KrhQkk+lhS8gKA3amx/Pu7i3pMUIfhOp/BsKPhZkUO5sQNl0J2/5dmdWzO/NGKJOREMHjJ/d4++gm7x7f4YUA4PF96gQEXyWu3f0rPX1wneNbC9muMwcP+V5oDRlO0rRRlE0fRfHEkRQPG8DO8UPZO2ko26coU58RxsfKLG7EhfOupoyG4/v49NNRnuyt4lJNIV5rVmK0RIU8L10atqfzuDKNg1nxVGRmUp4jEss88pPTpdfchBTy4xLJS0ihMDGFPH8fUkwWUGQ4jQydycQvnkDkwvGEqo0jdP5YwheMI0pdvI4ldP6YL32HoayaMVjqTq6cJjSEldOH4KMylNUzhuI9bQiekwbiMm4ADiPlcZ+gRKDqGMnLBM0ZJb16Tx+OwaDezO/TEV0lWRYN7M/RikqOHzvJZj9vVhouxHbeNI4lRbM/ZjMDZTqTFr5ZyrH25OVwuqqSMGN9kpRlKR7aj4xhfXmQ4M/HnQW82buVd/v383L/Tl7t38bH3bW82VHNjk2BrLcx5Gh5Gpf3FnGjNocH27Kp25nPlfxo0m0MSLW35Nyu7Xx8/VLajvcVBDe1OaycN4PwRXNJMdYhykhLOlPAeeIISvzd+fziNq+f3m40+heJ942691cQSCA8vMvLu3eoK8omavI41vSTJWviQHwGdEerT3dMBshhO2QgK8eOwbqfPDvX+rM3LJIDmyJ4smsXz08d497RI5yvKeN0YRJnM6NJdjNjpZ4qFZEh7MvNoiJVGDqVmoJSthaVUllYTHleAZlbEsiM3kJRSjo58UmUpaQTbKYnPQS8wHA6WcsmE685geiFjQDEaIxni+YEtiwWoWM8YQtGE6I2ko3zRbt6GAGqw6RKI0B1OOvmjiRwzkgpv1itMgzvKYNYOUkZP5VhbFowXipTwxeOJ3zhRAJnj8Z+3CAWK/REvX8PXI0NuX39HmdOniU/OJAEC23c50/iXEYMd0pyWDZmPFvWrOV0aT71RWk01JZyLNiPNUqyRI4fjv/IgaRYG3KvPIc3u6p5v2cbdXureL6/ije7q3mzr5ZXx/aQvGElJ3KTeXdgJ293b+N+ZSE/Zidwqyafs7nJbDLVZbORDld21/L53euvu6++W6+vwYnUWB5XlVBXU8rzrXm8Kc/jcVYCKcaaFAb7UnfrIi8FBE/vNUoA0KRfAOHZ4/s8vXeXz/ducSokkIxJozipM5fQ4XK4TBiGw5hB2A1Vwk65P/odOnIqIoqrRcXcLirmfF4213dUc2l7LY8O76RufwkNtXkcig/B1mgJmgvVSYgS5WYaxckZFKdnU5KbT25aOhX5hZRm5VCelUt+UiqpkbGkx6eQsmEDesMUyLJUp2D5XPIMZpClN51U7cmkaE8mWWcySdqTSFgygS2aY4lZNJbIhaMJUx9FqNpICQyhTepj2KQ+jo3Cc8wdxbrZIwicNYyNc0YSqTGBmMWTiV08iUiN8WyYMwIflVHYTBC7qsbw47FjPHz6muvnr1CxeSNBS1TZbGPA9eo8nu6rJcDMki1r/diTmcKj9FheFGVyOz8T62GD0O7VEx05Oeb0lWVbdATnSwq4VFbAufI8Tpfn8WNxLsfyMjlcmEN6eBDhXi5crCznyfadPN69m0d79/Bw724e7d/JxeJMMq0MSbU148eCLN49uCUVBN+V+C3nYU0mj3cUcr8mn+sl6dwqzuB6ThLXsxKpWe3Jzi1R0gYNyfBNMHyR8Ap/qbu8eHKHx49ucvfxHe4c20PojLEcU5vFwWmjqJ46iKopA9k2fYj0mju8N88i1/J+V7HUfbxXkMyL2kI+HtlJw9EaGvbk8mp/FSeqS7G0sWbQjMmYWFpQmZpDWVo2SdFbyM3KpiAzh3KxgCUzV4IjPyWdgtR08lMzSI9PIsDLg2XjlIjWnckuey3KzFTJN5hOtt5Usgynk22oIj1IPEN3Cqk6k0jWnkjc0vHELhGaQJzWROK0JhOjOZHIReOIWjSeyEXCC4wlatEE4rWmEqc1VXofqjaCELXhUhIapq/FxSOHefLkGa9fv+fSkeMUhAazUkcTs0VzKU6O5nBNKZnR0VRmZ7OrvJTsxHhqs/M4UVKFk5YeY7v1YbbiYGb2UyZyTQCleXmU5eeztaSM8vwiCtKyyE/NpDAti7ykNNS1NFkXEcKe4nKOFVVxKr+Sq2U7ubdjOy8P1XIjM5atVvrUrDDgVFY0Hx7d4LsY23lczA6ibm8p749s5/WBrXw8tJ3Xuyp4t0e4oK0cTY2nJCGOV4/u8PKZAOAuL6TXbyH4MwzPn9zi0dMbPL13mcLQANxk2pOtqsK6CWMJHD+aDRPHETptMhsmTWBnkB/HS7M4kZ/G4dR47myr4uNPP/Lg0F5uH9jNmdqt7CkrJyI8AsWZE5kwZybpUXGUCm+QmUtWUio5yWlU5hdTkpVLcUYORenZZCUkk5mQTFleAQXpGbhbLsd3hRmnijLZusGVIvsllFvNo9hiDkXLVSkym02ukQrZhtPJNJhGqt4UUnSnkLJsCqm6U0nXUyHLYBZZhrPINJxJmr4KyXrTSdZVIcNwDik6KkQvGE2E2mhWqY4k2duJZ1cu8/zJM548f8nr1+/YX1ZOSlgwc1TGo6k5j4KsFKoLcnC3t6MsL5eLJ0+Qs2ULBfEJbMvOxd3AlAnd+zJPYQizZAew0nwFVUXFVBQUSuFwa3EZZTkF0ufOTkyhIDUTE1tLtCyNKMrIYmtmHjWpOWxPzWNHVh7bc3LYnpvFZltLVs6ZRbyZOXsy0vguytuIHWnB3N23jeu11ZwvL+ZyRSk/FeVzriCPY2kp1MREs97BgaKkON4+FSHiLi+fN+YIvyTxdQHC8+e3ePb0Fk9vXiJoiQYGffuhJTeAxX0U0BSSVWB+V1mCbW0pzkwiPz6aXJEwJSZxee9+Dldu5fC2nWwvrWBv5TZ2VtVg6uXM0BmT0TcxlugvTskkJ0H0EPKpLCimSECQkU12YioZcUkUZeWye2stNy5eprywmBAvDw7lJHO4IJWasDVUr7Ziq9MSqm3VqVihRonFnEYwLOaSv1yVXNPZ5Jmqkm82h3yzueSbikcPq1JoNocC0znkGM4m22gO6QaqxGmrEKE9iwQbA85VF/P6wT0ePX7Ko6cvePziFW/evOfEthoKctPoP1qZJaZ6lJUXkxITRatmf8LJ3oZbVy5RkppKRkwMlbm5+FrbMbGHHPPkBjOjhwI2i3WoFiAUFlKQkUVZbgHVRaVkJaSQG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Microsoft.Win32.SafeHandles.SafeFileHandle.CreateFile(String, FileMode, FileAccess, FileShare, FileOptions) + 0x15c + at Microsoft.Win32.SafeHandles.SafeFileHandle.Open(String, FileMode, FileAccess, FileShare, FileOptions, Int64, Nullable`1) + 0x95 + at System.IO.Strategies.OSFileStreamStrategy..ctor(String, FileMode, FileAccess, FileShare, FileOptions, Int64, Nullable`1) + 0x50 + at Spire.Pdf.PdfDocumentBase.Save(String) + 0xa6 + at Spire.Pdf.PdfDocumentBase.Save(String, FileFormat) + 0x3b + at Spire.Pdf.AOT.NLPdfDocument.PdfDocument_SaveToFile(IntPtr, IntPtr, IntPtr) + 0x98 - Line : 3141 +INFO:werkzeug:127.0.0.1 - - [18/Oct/2025 19:51:48] "GET /myclass/api/Prepare_and_Send_Inscrit_Bulletin_Note_By_PDF/6TncY6p2FiZFzzgGrjEWhhCGzqwJznbilw/684f106ae266de5f7fd519e0 HTTP/1.1" 500 - +INFO:root:2025-10-18 19:52:07.738135 : Security check : IP adresse '127.0.0.1' connected +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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\n

Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

\n

Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

\n

Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_172.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 19:55:22.027473 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 19:55:22.027473 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 19:55:22.027473 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 19:55:22.028471 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 19:55:22.028471 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 19:57:22.603355 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 19:57:22.603355 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 19:57:22.603355 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 19:57:22.603355 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 19:57:22.603355 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 19:59:20.939276 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 19:59:20.939276 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 19:59:20.939276 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 19:59:20.939276 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 19:59:20.939276 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 19:59:52.656318 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 19:59:52.656318 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 19:59:52.656318 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 19:59:52.656318 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 19:59:52.656318 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:00:12.689116 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_791.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 20:03:10.282715 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 20:03:10.282715 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 20:03:10.283708 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:03:10.283708 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:03:10.283708 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 20:07:06.250352 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 20:07:06.250352 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 20:07:06.250352 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:07:06.250352 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:07:06.250352 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:07:18.052625 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 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+DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_645.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

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0RhuKMU/Zqs5woAbCtKhprveBAHIQQQGjEdhNo44S15MLyMfCwfVuRhT0EydN0YcEVHPLYVJKLESJ5v9wzEYLueXZKvhVCEXveRMAeA/Px8bN68qU1BVgDgt99O4bffTjcKI9we9Ho9fvzxx1Z5tIvFYjz99LN47733MHLkyGaF+Z1apkwmw7Jly7Fly1b07t2n4TeJxAzLlz+NTz/9PwwdOqxTNd/W1gnRvR3ISSAQYOLESQgPH2ky7ZUrl3DixIk29aOioiJ8++23JpdGxGIxFi5cBDc39/taPjGB3laTBsehl42hx7yCCIVqZbufq+J12F6cgoomBu/nrJ0RZPNfr1pbkRmed/ZtZFrhAeytK8dNRXmXdpRJPTzxumsgHJvMxOtBWFt0E6cr8rrNSe5mXSUO1hSj6RRmtJkVJrn4QMgO2+g0wbNnzx5kZma0fXIoFGLjxg0oLi7uUBkKCgpw8OCPrcrv6aefxcqVK9u8vUwkEmHkyJF4//0P4OPjCx8fH3z++edYvXp1qxy/7mfs7e2xbNkyk+vDHCfA9u2RyM3NbXXf++mnn5CUlGgy7ahRYzB27Nj7vi2YQG8HjhJzND2NRglCja793q+xNSU4r6oxuD7LxbeRQxcHYJyzF3ybrJcrQDhQnNqlWrKQ4/CYey8ssHOHWWMFHtm8DmtzopFW1/UBOAjAn5X5KDTyrk+5+MNFYsE6aSeRkpKCPXu+b7emePNmEg4fPtyhEKDR0dFITU0xmW7WrNl49tlnTWrlzQscDqGhofjiiw2IjPwOERFTWhURjQEMGTIU06fPMNlPUlKSceTIEeh0OpPPzMrKwu7du0xq9NbWNli0aCHs7Ozu+3ZgAr0daHjeqJBpr26qIx67i1INDkSZa2GHUCOmY0exBZ519DTIf0d1IdK7OHqZrVCM1Z4DMNnC1qDzXNCq8K+s6yju4m10PBHO1hSj6fTJSyjBKAcpBGDaeWeg1WoRGbkNJSUd07A3bfoKOTk57fs2dDqcP3/epLm7Rw8nLF68uMN7jzmOQ79+/eDl5cWOVG0DVlZWmDdvvkmnL4FAgG3bvjVp8eF5Hvv27WuVZSgiIgJDhgxljcAEevuo1KjQ1JvVHByshe3bKpGsqMBxheG+2kecvI2e7iXkODzo4gP7JuvZubweJ0symvFT7zxczSzxludABIvMDETnobpybM6L7ZT1/ObIV9chpd7wCMiJFrZwkJizDtpJXL58Gfv37+vwc8rLy7B3795WaWVNKSsra5Uz3oIFCxEQEMga7S4SHByMJ56Yb1KjLikpxt69P7QYEjYxMQH79v1gMs8ePZywcOGidltlWp7cARUV5Q2n8rX3Lz8/v83+J+2Febm3VTsnHtdrDDUWO04At3Zs3eJB+LEkDalNBOAEiSVG9fBo9j5vSzu8aNsTa+/w8tYD2FKehWluAW0Kj9vmjg4g1NYZa2XBWJ59HcX035VzFYDPy7PhZW6LJ9x6dcladrlGiXwjE4Yhtq7d7mnf9rr7e2h9tbW1iIzc1qqDSYjIpDa7Y8d2TJ48GSEhIW0uhylzOxFhxIgRXb7Hl9EyYrEYjzzyCH7++RiysjJbtILs3r0LERERGDBggMHvarUae/bsQVFRocl+9cQTT6BPnz5d861yAqxY8Qr0el2HnuPr64fffjvTJZOObhPoZdp6FKg7LwCDABxczCzuujk1X1WLPYoSg+v9Le1bfVb2nWQpa7C/qgAcGpvspzvIWlwLFnMCTHf1x9rqgkbRJG7yOpwty8Z8Wd8uNu1wmOTkhdWqGrxSnNKo9BVE+KgwCd4Wdhjp0PlepyX1ChQZrJ8T3Mys7+0Rjwjf5NyAo7DzYuALOQGe9Q2DuJPPqb969QqOHv3J4Azxpnh5eWPgwIE4cGAfWtqDXVtbi507d6J3795tOme8oCAfdXUKWFg0P1kOCuqNgIAAJlHvAXx9fTFv3jy8++6/W0ynUNRix47vEBQUBAuLxuNcVFQUDh48aFKYe3v7YPbsR7t0371QKOzwRFEg6L6JZhcJdA4v5dyAGdd5Fd1LZIadfSbAWnj3NLA6vQ4bc2OR3WTGZgbgIXt3CNvxvqfLshHbZLvbEJEEU1x8TdeJTQ88Y9UDXynKGq5pAewsy0SEqx96iLt2RigRCLBQGoQMVQ2+qCls9FuiXot3c6KwwcwS/padG0zB6Bo9ocsOPelEiY73aks69Ym9OQGepsGdOxkvK8WGDRtMCnOe57F8+XIMGzYcly5dQn5+y+cPHD16BFOnTsXo0aNbXZbS0lKIRC1/84GBgd2i/TBaI7wEeOihh/Dzzz8jKup6i2mPHTuGGTNmYNSo//aHuro67N69GzU11Sb3tS9duhRyuZxVeiNFq4vI0muRrFN32l+9TtPla8MtoeJ12J2fgA3VBYZailCM0T082mw7KFTXYUd5lsF9D9r2hIeRuO1NsRCIMMvFz3CSoFHiXHlOt9SLg8gMKzxDEWFuWN5f1XX4JCcaFdrOjX1dayTMKzgOTmLmkdxReJ7HkSNHceWK6UAeffr0xcSJk+Dj49OqEKB1dQps2vQVampa77hZV6cEZ2Ki7OHh2Satn9G1uLm5Y9GiRRCLW7ZE1dersG3bNigU/z2O+dKlizh27IjJvhQWNgSTJ0d0W1S8+16g/69AADJU1ViXcRUri5JhTDStdPGDczuOiv29PBfntapG05QAgRCzXP1bPTkYaN8Ts80ar5frAPxYkoG6Dq79tBZPCxu86dEffY2Ent1eU4xv8+I71UnO+EE4BP09HoTj70BOTg42bfrKpJlQr9fjqaeegouLMziOw4wZM+DhYTqi1p9/XsCxY8da//21ok27y+GI0XrGjh2HkSNNB5v5/fffce7cWQBAVdWtMwBMOU9yHLB8+VPsKNv7UaDzAIo0KmTXK1r9l1VfizhFOc5X5OHTzGuYm3QGaytyUGvEQjDD3AaP9AyAoI3OX1VaNXaUZRg0xmRrZwRZt/54RjuRGR4zYp7fVV+Nq910aAsHYJi9G/4h7Qf3JtqUGsDHZRk4UprZaSfS2TSz7FKkVbEvugPodDrs378fhYUFJtOOGzce48aNx+11czc3NyxfvtykZkVE+Pbbb02a529jbW1lMkpYfn4etFota8B7CDs7Oyxa9KTJbYQajRqRkZEoLS3F6dOncfbsGZP9Z8qUhzBixAhWyUbosgXpXgIRLDrRw9lGKGqXh3AdgBezrqKtxth6AHVEBpHbGmnHIjO84z0Yju1Yu71YVYCjTbZeuXIcHnX1b7Nn+IgeHphQlIyTTQTajyXpGNFDDjHX9fM2DsBUFx8kq6rwUWkmFHfUWwnxeCcvDr4Wtgi1delwXs05lZVq6u/tr43jsKdnENwknXeQjYDjIOoks2NycjK2bv26VWkXL17SaM8xx3GYPDkC33+/2+TRqTdvJuHAgR/xwgsvmJwAODk5Q6fTtWi+TU6+CY1Gw9bR7zHCw8MxderD2Lv3+xbTXblyGXv2fI9ffvnFZH+QSCRYsmQpbGxsWAV3hUAnMrayTXjPMxQDjIRIbb8pgYNlO7wFedw66rOzCROZ4VOvQejbjndU6nU4VGoYMGGUhR0G2bm2+XkuEgtMdZDiZElaYy29rhxP1pQitB3PbA8WAiGelwcjW1WL7YpS3Kkzxes1eCv7Or70D4dnB7fUuZlbw47jUN1I4+eQV19zj39uHAY7yuBzDx65Wl9fj61bv4ZSaXpnyvTpMzB0qGEgjx49euDZZ5/Ds88+Y2Jew+Hbb7di8uTJ8Pf3bzGtVCqFlZUVeL75iXViYgJSUlIwaNAgNqLfS8JFJMKCBQtw8uSvqKgob1GGfPLJxyaXV4h4LFiwEH379u22dxCLxR32cpdIJN0WpKjDAp3jjOvNPcUW8DL/35xFPW5hj1Weoehv69KuTXQxNSXYcodneoNA5ATYXZDUrmdW67V/6cl3bh/j8WNJGvrZukDUTR3KXmSGVV4DkJb6B8422bb4s6oGn+VE49++Q2HdgaNebSSWcOMEqCZ9IwvB77UlWKjXwkbIHKTayqVLl3DkyE8m092KCDbPYKvR7bFg7NhxGDduPE6f/q3F55SXl+O777bj7bf/0eLZ1dbW1vD19W9xLzrHCXDu3DmEhoayvej3GL1798YTTzyBL75Yb1IxNIVM5oE5c+Z0WzheIsLbb/8Dw4cP75gyKhB2m9MmCyzTBgaIzPCUkzcecQtAD3H74oVriceh0nSjv0XWVSCyrqJTy7y9uhCLlFXw7UatMNDSHv/2GIDF6ReR2sQj/bOqAnjlJ+IZWT9I2mkqlptbw0tsgZtqxR02IeB4fS1K1UrYWLKYzm1BoVDgq6++bDFy153aef/+oS0K/KVLl+G33061qJVwHIcdO77DlClTjWr7t3F2dkb//v1NBpfZtWsnHnroIbYf/R5DKBRi9uxHceTIT8jKyurQs5544gn4+3df+xIRpFLZ3yoCIfNybyUPmtvgcNB4LPUIbrcwB26Fed1eXdRt5c7l9ThSkt5pDmmtZZi9G95072PUj2JdSSpOdWBbnaVQhCFGHAdreT1Ol2d3+7v+3fn1119x5cplk+lcXFzxxBPzWtSoAWDgwIGYOXOWyefxPI9vv/3G4AzyRhqHSISRI0eZPNylrKwU33yzFXV1HQ9mFR8fh+zsbNYxOgkPDw8sWbKsQ7sR+vULxrRp05kF5n7X0C0BvO3iB3+L1mlt5eo6PFV00+B6oroOefW1kFm0fxlBT4QjJekope7dZrOlIhdz3YPQ08yq2/IUchxmu/ohS1WNf5VnGUwy3smNgZu5Nbza4SDGAZjg6IGvKnJQcofw5gB8VZqBSc7eXRr69n+JwsJCbN68uRWnoXGYO/cxBAUFmXymhYUF5s+fj6NHfzLpfX78+M945JFZmDRpUosTBF9fvxbDiQLA3r170LNnTzz33PMmJx3NER0djVdfXQWxWIznn38BEydONBnYhmFCaxQIEBERgUOHDuL69WttF1KiW2edy2QyVpn3u4YuBjDWQYZHXP1a9bdI3g9rHQ1jqOcQj49ybqCyA0FS8uoV2FmVj+7eNZuk1+BUaVarwvLwnRi+x1IownMewVhiZahNX9fV44PsKOS3MzxwiK0zhjbZf08AonRqHCpOY3vSW8mBAweQlJTQKi1rzpw5rdaQgoODMW/eApNroxzHYePGDSgvb95pSiaT4eGHp5l0LOJ5Hhs3bsTGjRtRU9M2B0mdToerV6/i7bffQnLyTcTHx2HlyhVYv/5zlJSUsI7SQZydnbFs2fJ23Tt06FBMmDCBnX7HNPS2I+EEWCbrhzO1pTjTZBvYr2oFduTH4wWvgW12XCMQfi5JQ3qTMK/2nADP2Uth2UkhbXkiHKkpwhVt4y1cm8rSMa2nH2xELTuUEBE685w0J7EFVnsNRGrKeZy/o0x6AD8qKyHKiQLa4QZoLRTjmZ6BOJd1DdVNpiAflaShv7UTwnvIOz3yv5Z4XKjIx0A7V9iIJH/rvp6eno6dO78zbRHhOCxevKRNGpJEIsHjjz+OI0d+QllZaYtpY2KicfToUcyfP99o5C+O4/DYY3Px88/HkJaW2uKzNBo11q//DElJiVi+/CmEhIS06JDE8zyqqqqwb98+fPnlRlRW/teHpa5OgfXrP8fVq1fx+utr0K9fPxaZrAOEh4cjImJqqyLB/dfaY4nFixfDwcGBVSAT6O2jp7k1Vkv74M+sa40iwykBbCjLxHB7KQbZ92zTM0s19dhZnm0QaW6JjQve9A2DWScF8CcCfPMTsbAgvtG2sQsaFc6X52KKq1+L9ws5rtM7hb+VA972CMXTmVeRfoeTnB7AHmVVu587zFGGCSVp2K+sbHQ9n3isybmBrebWCLRy6DShXqfXYnNuHD4uTcc8O3e87TMYtn9Toa7T6bBz5w7k5+ebHFz9/PwxderUNmtIgYGBWLhwIT755OMW7yUibNr0FcaNG9dsbG53dymWLFmKN99cY3ItVq/X45dfjuPcuXOYOHEiJk16EL17B8HVtScEAgGICDU1NcjNzcWff17A/v37kZubY/S5PM/jwoU/MG/eE1iz5g1Mnz4dlpaWbJBsB7a2tliwYAHOnTuLujpFq+6ZNGkShg+/O0FkOI5DcXERQsOCJAAAIABJREFU0tPTO+2Zjo6OXTo5YQK9GcY4eeHVqkK8W9U42loqr8enuTHYaOUAhzbEDj9dloUYXVMvYg6PuPrDojOP/OSAsc5e8ClORnITa8Dm4hSMdfKEZYvburrGrDXaUYZVymq8XpSEmjtiF3TEMG4rkuBptyCcybiIiibxEC5qVXg+9QI+8QlDSDu3F95JmUaFT7Ki8FFVPgDCF1V50GcQ3vEJ+1tq6lFRUdi1a6dJIc1xHJ555lk4O7cvpsTMmY/g4MGDyMhoeVDMz8/DgQP78eKLLxoNO8txHKZMmYITJ34xGU3sNiqVEocPH8JPPx2GSCSCubk5HB0doVSqUF1dDb1e1wrfgVtUV1dhzZrVuHEjCq+/vgY9evRgg2Q7GDhwIB599FF8++03Jvuera0dnnxy8V2bQHEch7fffqvTrDJEwLp1n2LGjBldVmZmP2oGM4EQS2T9MExkZiAMvldVYV9BEvhWiqNqnQbfl2agrkn6+Zb27QpMYwpniSWecjDUdH6vr8W1qqK7Up9iToD57kF42l6KztqRyQEY7SjFuy4BBi3BA/hNU4elaX/idFk26tsZS17D3zKxP3vzLD6qymuYgqgBbKjKx/sZV6DQ/73CjtbV1WH79kjU15uOrDd06HA88MAD7V6/lMvlmD9/fqvu3759O5KSkpr93d7eHmvWvIGgoN5tHEgJWq0WtbW1yM7ORmlpCTQadauF+Z3PsbOzYxHpOjKumplhzpy5kEpNL988/vjj6Nev310tr16vh1ar7aQ/TZeXlwn0FvCytMNb0r6wbCLSOQCfl6Yjprp1zjIXK/NxSmPo/DXXxQ82os4POCDkOEzr6Q+XJjPLKgB7SlKh4fV3pT6thCI85xGCKZ14nKqIE2CerA9W2bkZ/f26To0HMi7jzZQ/EFdT2upDYlS8HimKCnyYcRnh6Rew30gUOi2AWr0WujbsWrgX3HouXryIQ4cOtkKA8XjmmWcahXhtDw899DD69DEd3au8vAzffvst1OrmHU979eqFd95ZC0fH7tWQiQhTpz6E55573mR8ckbLBAYG4rHHHmsxjUwmw5w5c9gOg7aOh6wKWma8szeWV+bh/2qK//txA0jkdfg8NxbrrUa3uI5ap9dhb3EalE22V0VIrDDEwb3Lyi23sMXTtm5Y22TJ4KiiDE8ryhHcCTHV21UuM2v8y3MgclP/wDVd5xyrai0UY4XXQBSmXsCuJuvpt1vs05oi7FKUYpalA8LtpRhg6wJzkQSWQjEshUKoeR4qvQ4KrQpRNaU4W5mHSGUl1H8Ja2O2mNfspVjjPRj2RhwNtc0I+cNlWXCuLu6SunU2s8TEHh4tpqmsrMTWrVtb5a0+bdoMDBkypMPlcnFxwZIlS7Fq1QqTWvGxY0cxffqMZk/q4jgOYWFh+PjjT/DWW2+26iCZjsLzPGbOfARvv/02c87qDIVDKMSMGTNx/PhxJCYmGKlvPZYsWQovL29WWUygd7KJSCDEsx79cT7pDK7rG5tMtisrMKrwJhbK+jV7oEpibSl+UFU1ES/AbCdv9BB3nelOzAkw1cXXQKDnEuHHolT064R15fbS16YH3pKH4Jns6yjsJGuBm5kV1vmHQ559Ax9WGT/Jq5jXY6OiDBsVZbAVCOEjEMFdIIQ9J0Qd8SgjPa7rNKhvhcb9vpMPnvfo36yFxeiBOMTjleKULqvX52x7tijQeZ7H8ePH8ccf502uCwoEHBYsWGg0xGt7mDRpEvbsGYzLl1s+Z12pVCIychsGDBjQrCYsEAgwfvx4ODk54R//eBsxMdFd9x2JxViwYCFeeOFFODo6sgGxsyb2cjkWLVqEN95YY3BcamjoQEyd+hALItMOmMm9Ffha2uNVtyDYGjG9v1ucglSF8T20WuKxvzgNyiYCYoTIDOOcPLq83H1snLHU0nAQ+qGmECmKirvY6ThEOHniTRd/dKa7i6uZJV73GYwNroFw4wQtTlhqeD2idWr8rFFit7oWhzV1uKCtNynMewnF2O8xAC97hnbJcklHMDVBKygowMaNG0wKcyLC44/PR0hISKeVzdraGs8993yrYlqfOnUSp06dNDHhEKB///744osvMGPGzC4xzbq5ueOTTz7FqlWvMmHe2X2V4zBx4iQMHhxm0PeWLVsGV1dXVklMoHfdQPlwTz8ssnGBsImmncnr8Un2Daj0OoP7UhQV2FFraF59xEHeLZHMLIUizHU1PM0qidfjRFnmXa1TMSfAPPcgLLbr3GUHO5EET3uEYL/fCCy0doJrJwWjkHECvGjnhoO9xmBmT39YCP9exi29Xo+DB39Ebq7pkLv29g54/PHHOv0QjGHDhv11hjpMTii2bt2K4uJik0LBy8sb7733PjZt2oIRI8I75RAMe3sHLFiwELt27cb06dPZmnkX4ejoiMWLl9zhZEiYNOlBjB49hlUOE+jNDA6d9BwLgQjPe4Sgt5EtX7uUFThUlNIoxhoPwvHSTAOTcm+BCBEu3bc2NNDBDbPMbQ2u76/IRV694q62jZ1IglWe/THF3KZT20zIcRju4I5NvcZgl/cQPGZhD6927vPvLRDhFTs3HPAPx8cB4ehl5Qjub9jX09LSsGXLllvHHbfwx/N6LFiwsM2e5K3BzMwMy5cvh0QiMVmOqKhrOHLkp1adwmVtbY0JEyZg06bN+Oyz9Rg9egxsbe1aHTuciCAUChEY2AvPP/8Cdu/ejbff/gf8/Py6NDrZrfrmm/2jLoh2SIQW8+xIvPX2EB4ejgkTJt5SQCytsGDBQtjZdd/hSqbqorP/upoOqxm2IjMscZA3GkwIBEdx92/tcBSbY4W91EgZO2efsK+VPd6Th+BsVaHBbzGqakzUqhvWxau1GlQQb1AeuZk1/Cy7z7HGXmSGBa4BkFcXNhJEBCBNWQWZuXXjwVEkwQJHeaMDTngQLLtor7WnuQ0+8h6CvnesLXMAHDqh/5gJhBjv5IlwRxmSFRW4VF2IazUlSNHUIUWvRSHPAw2BeDmAE6CPQIheQglk5jYYZ++OXjbO8LO0g6CNA7uVxByrHeXoTr92XwvbZgVHcXExli9/yrTlRCzCjBkzu2z9sn//UHzwwUcoKjK9fVIgEKKurg7W1tamrWgcBzs7Ozz00EOYOHEi0tLSEBcXhytXrqCgIB95eXmoqalGfX09hEIhrKys4eTkDC8vT3h7e2PkyFHw8/ODs7Nzt4QYFYlEeOONNw3Wjxu/vwBOTk6dluft6H3Tpk1r2Rolk3VbRDwrKyssXrwEp0//hhkzZiIsLKzbvpeIiAgMGzasW2VUr169uvT5HBELes24f9AToUSrQq1WDZ1OC9xeM+cE4AQCmIskcJJYsDPV/4dQKBSorKyERqOBXq8Hx3EQiUSwtLSEk5MTc766y+h0Oqxfvx4TJ05A3779WIUwgc5gMBiMvyu1tbWwsrJisfKZQGcwGAwGg8GmQwwGg8FgMIHOYDAYDAaDCXQGg8FgMBhMoDMYDAaDwWACncFgMBgMJtAZDAaDwWAwgc5gMBgMBoMJdAaDwWAwGEygMxgMBoPBBDqDwWAwGAwm0BkMBoPBYDCBzmAwGAwGwxARqwLG/QARQaVSged5mJmZQSxmx6MyGIz2odfrUV9fDwAwNze/Z47gZRo6475AoVDgrbfewpNPLsKNGzdYhTAYjHbB8zyOH/8ZTz/9FNat+6RBsDOBzmB0EzU1Nfj552O4cSMK7u5urELu0DTy8/PATlFmMFpHQkIC/vnPf0Kr1WHRoidhZWXFBDqD0Z2kp6ehvLwUwcEhsLOzZxUCIDn5Jj788ANs3rwZPM+zCmEwTFBYWIj3338PcrkcH3zwATw8PO6p8jGBzrgvyM7OgZmZOQYOHAQLCwtWIQAOHTqEr776EsHBwRAI2FDAYLREfX09Nm3ahIqKCnz00Ufw9va+58rInOIY//Oo1WrEx8eBiODv7w+RqPXdnogamaNNCb4707dXSLYlzzvTcRzX6F6O48BxnNH0CoUCN2/ehFgshlQqa/Tb7XuM/Z+IjD73Tg3f2O/tLa+xe1tTnq56bnO09f1bWz9tKWNb3rm179KZfa8tfd1UPbVU562pY2PlNHWfXq/HrFmzsHjxYsjl8ja9W3dNmJlAZ9wHAr0eUVFRUKvr4ePj0+pBLTs7G9euXcOVK5dRUVEBBwdHjB49GsOGDYOTk5NB+sTEBJw+fQapqSkQiyUYP34cRo8eA2tr61blqdfrkZ6ejtjY2L/yrISjowPGjBmLsLAwgzwBYNu2bSgpKcaMGTNRX3/rPePj4/6yRgzE2LFj4ejo2JD+woULOH/+HOrqlLh27Rp4nsePPx7AmTOnAQDTpk1Dnz59UVZWhm+//QZCoRDLly9HYmISzpw5jczMTDz77HMICQkBEaGkpARxcXE4f/48iouLYW5uhpCQEISHj4S/v7/B4BgVdR3nzp3HgAGh8PHxxcmTJxEdHQ2VSgl/f3+MHj0GgwcPNvAa1ul02LRpE+rqFJg9+1FoNBqcPXsGsbGxePDBB/Hww9MAAEqlEjdu3MCFCxeQnp4OgYBD7959MGrUKPTp08dgMpebm4vvvtsOHx9fPPDAA7h27RpiYmKQn58HPz8/hIeHIySkv9FJIM/zKCwsxLVr1xAVdR25ubmwsrLGyJEjER4eDjc3t0bvX1paiu+++w48r8eyZcthb2+49BMfH4ejR4/C3d0dCxYsBMdxqK6uxtdfbwERYdmyZUhPT8eZM2eQlpaGxYsXY/DgMOTkZOP48V8QHx8HgEN4eDjGjx8PZ2fnVk8i8/Pzcf36NVy9eg1FRUWwtrbCyJGjEBYWBplMZjDBOH78Z0RHR2PKlCmwsbHFpUuXEB8fB41Gg5CQ/hgzZozBfXdOsuPi4nD16lUkJyejqqoK7u5ueOCBCRg8eDB++ukwSkpKMXXqVPj7+zfKNy8vr6GcxcVFsLa2xogR4Rg9ejREIhG+++47mJubY8GCBbC0tGy4T6FQIDo6GteuXUVmZiYUCgV8fHwxadIk9OnTG9u2RYKIx9y5jzX61nQ6HZKTkxEVFYUbN26gsrIC7u5SjB49GuHh4Q153Eaj0SAq6jr++OMPJCenQCp1x/jxD2DYsGFtUibaqw0wGP/TpKSkkFTqRoMHD6KSkhKT6Wtra2nz5s00YEAoeXjIKCDAn0JDQ8jDQ0YeHjKaNGkixcREN6TneZ6OHz9OgYEB5OPjTWFhgykgwI+8vDzotddeo6qqKpN5VldX0+bNm6hfv74kk7lTYGAAhYQEk6ennDw95TR16lSKj49vdE9lZSVNnTqFAgL86MknF1FAgD/5+nqTn58vSaVu5OEho2nTHqasrKyGe/bu3Uu+vt7k5eVJcrmUPD3l5OfnQ35+PhQWNohSUlKIiOj69etkZ2dBU6dG0CuvvEy+vt7k5uZKvXsHUWpqCmm1Wjp16hRNmjSRPDxk5O3tSaGh/cnHx5tkMnfq3TuIjhz5ifR6fUPeOp2O1q1bRzKZO82YMZ3CwgaRXC6lwMAAksulJJO5k5+fL3300UekUCgavWtWVhYNHTqEQkP707/+9U8KCupF7u6u5OhoS6dOnSSe5ykhIYFmz55F3t5e5OXlQSEhwdSrVwDJZO4UEOBPn376KdXX1zd67qFDh8jV1Ynmzp1LU6ZEkIeHjAID/cnDQ0YymTv5+/vS5s2bSaPRNLpPpVLRd99tp8GDB5JcLiUfHy8KDu5L3t636nX06FF06dIl4nm+4Z7Y2Fjy9/elmTNnGDzvNl999SXJZO60cePGhnsTExPJxkZCEydOoFWrVpKfnw+5ublSYKA/xcXF0qVLl2jQoIHk6Smn/v1DqG/fPuThIaO5c+dQbm6uyb6nUqlo//79NHJkOMnlUvL19aaQkGDy8vIguVxKI0YMp7Nnz9Adr0I8z9Py5cvI01NOS5YspuDgfuTl5UEBAX4kk7mTXC6l4cOH0dWrVxvlxfM8ZWdn0TPPPEN+fj4klbqRp6ec/P1v9VkvLw+aO3cOjRgxnAIDAyg7O7tROX/4YS+Fh48guVxKHh5SCgrqRV5eHiSTudPYsWNo7dq15OrqTMuXL6O6ujoiItLr9XT9+jWaNu1h8vLyJKnUjXx8vBry9/f3pWXLlpK3txfNmDGdysvLG/IsLS2ld9/9N/Xu3euv79KfgoP7NXw7Tz75ZKPvS6vV0ubNm8nPz4f8/f0oLGwweXl5kI+PF61bt460Wm2XjnVMoDP+5zlx4gS5u/ekpUuXGAiKptTV1dHatWtJLpdSRMRk+u677yguLo6Sk5PpwoULtHz5cpJK3WjatGlUXV1NREQVFRU0YEAo9evXh44fP07p6el05cqVv4TWYDp79qxJYf7GG2+QTOZO06Y9TPv376f4+Hi6efMm/f777zR//nySy6U0f/78hjyJiJKSkqhv3z4kl0tp/PhxFBkZSbGxMRQbG0s//LCXhg8fTnK5lNaseZ10Oh0RERUUFFBcXBz961//JKnUjd566y2Kjo6m2NhYSkhIaBB427dHkru7K8lk7jRp0gT6+ustdOnSJfr999+pqqqKDh48SAEB/jRo0EBat24d3bgRRSkpKXTjxg368MMPycvLgwIDAygmJqahvBqNhubMeZTkcin5+/vRq6+uovPnz1N8fBxFRUXRhx9+QIGBAeTu3pP27t3bqI4uX75M3t5eJJdLaejQIfTRRx/RH3/8QefPn6eysjKKioqioUOHkJ+fD61cuZIuXvyTbt68SfHx8bR161YaNGggeXl50KFDBxsJ2c8//4xkMnfy8JDRiy++QOfOnaP4+Di6evUqrVmzhmQyd/L0lNOVK5cb7qmvr6fPP/+M5HIpjRo1krZs2ULR0dGUlJRE165doxUrVpCnpwc98MD4RgJ1z5495O7ek955551GE507hd0rr7xM3t6edP78uYbr+/fvJze3W20xbtxY+vLLL+nixYv0+++/U35+Ps2dO4fkcint2rWTkpOTKTExkZYvX0aBgf4UGRnZYt+rr6+nDRs2kIeHjEaODKetW7dSTEw03bx5k6KiomjNmtcb8s3JyWm4r6qqiiZOnEByuZQGDhxAX3zxBV2/fp3i4uLoxIkTNHPmDJLLpTRz5kyqrKxsuC8jI4MiIiaTVOpGDz44iXbu3EFXr16luLhYOnfuHL344ovk4SEjuVxKY8eOaeiPKpWKPv10HXl4yCgoKJA++OB9unTpEiUkJFBUVBT93/99SgMG9G+YGH711ZcN7Xzp0kUKDu5HMpk7zZ8/nw4ePEg3btyg2NhYOnr0KD3++OMkl0tJLpfS6tWvNdxXUlJCS5YsJrlcSnPmPEpHjhyhuLg4unnzJp09e5Zmz55FMpk7rVy5oqGcSUlJJJO508SJE+j8+XOUnp5OJ0/+SoMHD6LRo0dRXFwcE+gMRnvR6/W0adMmkkrd6LPPPjOZft++fSSVutHcuXMazbxvU15eTk888QTJ5TLat+8H4nmeoqOjycNDRkuWLG40YUhMTKSUlBSjg/edg/jWrV+TXC6lBQsWNNJI7tQSpk6dQq6uznT48OGG67/+emuiMnPmDKMDxZEjP5G/vy8NGjSQioqK7tCUtfTPf/6DpFI3OnbsmMF9Op2O1q5dSzKZOz399NONBvJbmmYM9enTm8LCBtOZM6cbCcjbgvv9998jqdSNVq9e3TDYFRcXN2iQBw4cIJVKZZDv999/Tx4eMho/fhwVFBQ0/BYZuY1kMnd68MFJFB0d3ahOi4qKaPr0aeTj4027d+8itVpt8E6HDx8iLy8PmjDhgQaLiUKhoMWLnyR3d1fauHGjwWSvtraWli9fRjKZO23Zspl4niee5+nEiRPk4+NNERGTKS4u1iAvhUJBS5cuJXf3nrR58+aG6++88w7J5VI6evSo0b5QUlJCERGTqU+foAZLiV6vp//7v09JJnOnJUsWU0ZGRqP6rq6uosBAf+rXr2+jNs7JyaHo6OhmLQG3+95vv51qsDrduHHD6Lu89tqrJJW60fr16xuux8fHU9++fWjo0CF04cIFgz4QFxdHAwcOoMBAf4qKiiIiIqVSSS+//BLJZO60evVrlJeXZ5CfWq2mf/97LUmlbvTmm2+QVqslnufp119/JblcSmFhg+nkyZMGbazX6+nChQvUr18fcnbuQadOnSIiosLCwgYr0rp164xayyoqKmju3Lnk7t6Tdu3a1fC8zz//jKRSN3r55ZeorKzM4L60tDQaM2Y0+fh40aVLF4mI6JdffiGp1I0++OD9RmmjoqIoOzu7xbGgM2CurYz/aTQaDa5duwatVgMfn5a9UsvLyxEZuQ1isRivvLICnp6eBmkcHR0xZsxo6PU6XL58BTzPw83NDebm5rh+/ToSExMbnHWCgoLg7+/fokNMSUkJvvnmGzg5OWPVqlVGt8E4OTnh0UfnQCKRICYmuuF6TEwMBAIBHn/8CfTt29fgvgEDBkIu94BSqURZWWnD9ZqaWsTExAAgo/nV1NTg2rWr4Hkezz//fCMHII1Gg71796KiohzPP/88xowZa7BGKhaLMXPmI6isLMTJkyegVqsBAJmZGaipqUZYWBgiIiJgbm7e6D6hUIjJkyfDy8sbMTE3EBsb2/BbQkICRCIRXnzxJYSEhDSq099+O4XLly/h0UcfxSOPzIJEIjF4p2HDhsPfPxD5+fnIyclpWG+/dOkSevRwwpw5cwz2E1tbW2P06NHgeR75+QXQ6XRQq9XYuvVrCAQCrFy5En379jPIy8rKCrNnz4JIJEJ8fDx0Oh1qamqQnJwMsVjSbByEysoKZGVlQSaTQyaTNZTx4sWL0Om0ePHFl+Dt7d2ovgUCIfz8/FFSUoQLFy5Ar9cDAORyOUJCQlqMiKjRaPDNN9+A4zi88sor6N+/v9F3mThx4l/r+/FQKusAAEVFRaiursIDD9xaG27aB273faVS2RB4JS4uDt9/vwseHp545plnIZVKDfKTSCSwt3cAAISGhkIkEkGpVGLjxg3geR4vvPAixo0bZ9DGAoEAffv2hYODI4RCAXr16gUA+OOP35GUlIixY8dh2bJlsLOzM8jT1tYWYrHoL3+L3gCArKwsREZGwsnJGa+88gp69OhhcJ+vry9GjBgBrVaLxMREEBEcHBzA8zx+//13pKenN6QNDQ2Fh4dHlzvHMac4xv809fX1iI6+AUtLKwQG9moxbVpaGi5e/AO2tvbYt28fjh49YiQVh5SUZAgEAiQl3RLePXr0wBtvvIk1a1bj2WefwcqVKxERMQW2trYmy5eSkoKiokJYWVljx47vDITcbW4PDpWVldDptOB5QlpaOiQSCWQyqdF7BAIOAoEAYrEYFhaWjQR2XFwsvL19jXrr1tTUIDY2Br169WoQLLdRKGpx7NgxCAQCnDt3HqmpaQAMg9IoFHXo0UOK/Pw8qFQq2NraIj+/ABqNBoGBgc2+p6WlJYYPH4HU1BQUFRUBAKqqqpCamgorKyuDSZZWq8Xly5chEomQkZGJd9/9d7NOjqWlJairq0NtbS0AIDs7G0VFBZg5cxYsLMybqUMhAIKdnR2EQiGSk5ORmJgIgHDo0CGcPXvW6H35+fkgArKzs1BdXYW6OiVSUpLh4OBgdKJ4u40rK8sxZcqUBoGlUCgQExONwMAgowLQysoKzz33PJYvX4o1a15HYWEh5syZY9SBsinZ2dmIiooCxwlw7NjPuHDhgtF0hYVF4DgOCQnxqKtTwtLSCteuXf1r0jjAqNPb7X4nkUgaHMESExMgEokxe/ajzXqJ19fXIzk5GRzHoWdPt7/uS0RsbCyGDBmKyZMnNysUq6urkZSUgCFDhjU4qp05cwYcx2HWrNmwsbExel9tbS3S0tIgk3k0OCpevXoVhYX5cHR0wqZNmyESCY2OBdeuXWuYrBARgoODsXjxEkRGbsOzzz6Dl156GePHj4eZmRnzcmcwOkpWVhZKSorRu3cfODg4tJg2MzMTZmYW4HkeBw7sazGtWCyBg4MjOI6DUCjEo4/OgUgkxtq1/8Krr67CyZMn8eqrryEwMLDFrTfZ2dl/aXDV2L+/5TxFIjGEQhE4ToCysmKkpqbA2toGMpnxwbGmphbFxUVwcXGFi4tLo0lETU01pk59yOhAk5KSAqWyDqGhAwwEb1lZOYqKCiAWS3D69KlWlFcIiUQCnudx+fIl8DyPgQMHmahbMQBqGETLysqQnZ0NV9eeBoKgqqqqYbJz5colXL16uYUnc5BIxBAIuIb2Njc3R0hI/0YTnjs9qktKigFwkEqlEAgEqK6uhkJRC57nm5nw3fn+IlhaWkIkEiMvLxd5eTl48MEIWFsbFyxJSTchFIowaNDgBqGVnp6G6upqTJr0oIE3NXBri9WECROwefPXWLv2Hbz//rs4ffo3vPzyywgPH9li3ysqKkJ9veqOdyET/d0BQqEQGo0GyckpsLCwaHZyUlFRgaKiYtjZ2cPBwQFarRZZWdkg4iGTyZoVyrW1tUhMTICFhQV8fX0bnqXTaREQEGBUU75NamoKRCIxBg0aBFtbW9TV1aGwsBDm5uaQSt2bvS8nJwdVVZUYNGgw3NxuTSIKCvIhEolRV6fAnj27Tfbz2+UyNzfHypWrYG/vgA0b1uOpp5ZizpzHsGLFSri7uzOBzmB0hIKCAhAR+vbta3SbUFNtj+M4PPLIbKxevdqo6bbpYHp7e5WFhQUee+wx9O/fH5s3b8Lhw4eRkZGBr7/+Gn5+/kbvJyKo1bfMkXPnPo7XX3/d5PsIhUIIhUKUl5cjIyMdAwcOanZrUmZmJioqyjF27LhGwXSysrIgFkswYMAAo5pydnY2RCIxgoODDQS+Wq0Gx3Hw8/PH119/DUfHHibLbGdnB51Oh5iYGNjbO7Q4sOl0OkRH34BOp4OX160lkry8PJSWFhvdIqTT6aBQKCASibB79x7RMj2ZAAAgAElEQVQEBgaaLI+lpeVfZtIE6PV6eHp6GhV8t7bARUOjUTcsTRAReJ7HkCFD8dVXm0weysFxHGxsbJCamgqOEyAkpL/Re9RqNdLT0yEUiuDhIW8oT25uLgQCAYKCgpoNiCQSiTB58mQEBwdj584d+Prrr/HKK69g/fr1GD58RLNl43k9iAijRo3BJ5980qr+bmNjg8LCQqSnp8HOzh5ubsbbsqSkBEVFhfD29oZUKgXHcTAzk0AgEECpVDabR0VFBWJibmDSpMkN5vHb6VuqayJCTk4OOI6DTCaDUCiEWCyGWCyGVquFRqNp9t68vDzU1dXB09Ozob/rdDoAwGuvrcbcuY+Z3FMvFAobJikODg54+eWXMXJkOD799FP88MNeZGVlYdOmza2ynDCBzmAYQa/XIyYmBjyvh6+vr8kT1pycekCn0+LmzSRYWlo2a6IzZs4VCAQQCATo06cPPvjgQ7i5uePLLzdg586d+Mc//mlUI+E4Dl5et9ZECwryYWVlZXJQvU1iYiLU6noMGDCw2YEuMTEBHCdopPHV19cjISEBOp3WqHalUqkQExMNrVYDDw/D393d3WFuboEbN66CCCatHv8dNHNRUlICb29v9OzZs9lB+fz584iLi8WQIcMQEBAAAEhIiAeARu9x52TB398fmZkZqKysbHV5FAoFrl+/DqFQ2OwkQKVSITo6Cu7u0ob4BRYWFjA3t0BVVRV0Ol2rB+jy8goIhUK4uroaba+KinLEx8fBwsK8YQKo0Whw48attvD29mm2790OjiKTyfDqq6/By8sbb7zxOj7//HMMHhzWbL93dHSERCJBamoKVCoVXF1dW/ku5cjPz0NY2JBmJ5O3td6goAiYmZlBIBDAyckZt5asUqBWq41ah65fvw6JRIJRo0Y1TDatrKz++kYKoFQqjVoqNBoNrl+/jvp6JYKCggDcWo93de0JrVaLmJhYhIUNMRDMer0eFy/+CZ7nERIS0nDd1tYORDxSU9NgZWXVapO5Xq+HUCiESCRCWNgQfPnlV1izZg1OnDiOkyd/xWOPPd6lYx5zimP8Twv0qKjr0Ot5BAX1Npk+NHQA/P0DER8fh2PHjkGr1RrVAhQKRcP/4+Li8OWXG1FSUtKQzsrKChERERCLxUhISGjx4JOAgAC4urri/PlzOHHiF4OY6jzPIyMjw+BEp4yMW9pcSEiIUQGh0WiQnp4OkUgEX1+fhoFMrVYjLS0VSmV1wxplU00xKioKjo494OfnZ/C7lZUVpk+fCWtrO2zfHom6ujqjA35hYWET824xqqqqoFarUVtba1AnPM8jOTkZ//nPR9Dr9Zg79zE4OztDp9MhLS0NIpEYvr6+BgOypaUlBg0aDL1ej02bvmpYd2/6Tv/f3nlHx1Vdi/u700fSqGtmNCpWteUqG4MLmGLAlICBAAmmOLz3CxB4TkKABMgjgVASQhJqEpKQAAYC5NEMxhhsggFj44JxxUWWbFnF6l1TNeX8/pA8nhmNpJFkmZLzrWXW4mpuOefuu/c5++y9T2VlZdg929ra+OKLnUyePDWs8E4ohw5VcvhwLdOnzwgakaKiIoqKiigr28fy5cuDM7lIV3ZbW1vYseTkZAIBP9u3b+83W/T5fGzYsJEDB8qZPHlKsBCR1+tl69bPSU5OCSuuckT2amtrWbp0aVjwlVqtZt68U8jMtNHQ0NjvOUIpLh7PxImTqK2t4a233ooq7w0NDTQ3N/cbKLpcLkpLpw/oOj8S0Dhz5szgOzvttNNIT8/gueeeZfny5djtdrxeL16vF7vdzooVK3j00UdQFBVW69GiPIWFhZhMibz//mo2bdrYT3b8fj979uzhk08+ITc3n3Hj8oJ/u+SSSzAa43j66adZu3Ytbrc7OGNvbW3l6aef5rXXXiM+PoHi4vHB8+bOnYtWq2P58rf4/PPPo8prZWVl8LvsHRhs4E9/+mPQo6AoCmlpaVxxxRUEAoHgOvtYov7Vr371K6n6Jd9EmpubeeKJxxGi16DX19ezf//+fv8qKyuxWq2kpKRgMBh4//3VrF+/Ho1Gg9VqRQhBe3s7q1at4qc/vZWGhkbmzJmDoij85S9/6fuIXZSUlKAoCnZ7NytXvsvatR9z8cWXMG/evAFddiaTCY1Gy4cfrmHdunXExcWTkpIMKLS2trJixdvcdtutOBxOZs6ciUajoauri6VLn6WmppobbrghqmFubm7iqaeewu128z//syTovuzp6WHZsmW0tbWTk5NLTk4OXq8XlUqFRqOhoqKcJ554jFmzZnP55d/p55LXaDRkZGTw7rsr2bRpIx0dHVgsFnQ6HXa7nc8++4z777+fd99dybx584JejhUr3mbt2o9pbW1h48aNpKSkBF3xTU1NrFy5kjvu+BlVVYdYtOhKrr/+BvR6PS0tLfzjH3/H4XCyZMmSqFHKNpuNLVs+Z/v2bZSVlWGxWDGZTLjdbsrLy/nLX/7Cvffew+zZs4Pu/m3btvHqq69y1llnc/7534pawWvDho2sXr2KSy+9jNNOO63PbawnNTWFlStXsnHjRnw+LxaLBZVKRUdHO2vXfsIdd9zOvn1lzJ07Nziz8/v9vPnmm+zZs4eMjAxsNhter5eamhpeeulFHnjgPoQQLFp0FXPnzkWlUlFVVcUTTzzOpEmTueKKRf1mpsuXL+fuu39BRUUFU6dORa/X43K5WLNmDStXvsO0adP47nevGNCDo9FoSE1NY+XKd9i0aSM+n5+0tDQ0Gg12u51169bxs5/9lN27dzN79uzg/V9//XV27tzBdddd32+gcUTGXnzxn9TUVHPdddcHg/nS0tKIi4sLDl7Xrv2YgwcrWbv2Yx555BGeeeYfOBwOfD4fixcvJi8vL+iFAVi/fh2bNm0mOTmF1NRU/H4/9fX1LFu2jDvvvJ329nZmzpzJpZdeGpTbnJwcGhsbWL/+E95+ezmbN29m7959rF69igcffJAVK5bj9XpJTk7myiuvDK6FJycn43K52bRpAx999BFJSckkJycjhKClpYVXX32FW265GZMpkcmTJ+P1evnFL+7iX/96ORi8KYSgra2NpUuXsm/fXhYvXhw1K+KYIjOVJd9UNm3aKAoK8oPFJrKyMqP+u+qqq4I5yE6nUzzyyCMiJydLZGVlivT0ZFFcXCiyszOFzWYRJSXjxQMP3C+6u7uEEEJUVlaKSy65WNhsVlFQkCcWLrxQzJx5gsjMtIhFi64Iy6UeCLvdLv7whz8IiyVdZGVlitTURDF58kRhs1mEzWYVEyaMF48++kiwqExNTY2YMWO6OPHEE8KKdoSye/dukZeXKy66aGFYdbSenh7xxz8+IdLTk0VWVqZITo4XBQV5Yt++vUIIIVasWCFsNqt44IEHgsVoouWpr169Wpx44kyRlZUpzOY0UVRUIAoK8kRmpllYrRliyZIl4sCBA8F85yVL/kfk548T119/XV+VN6swm9PE1KmTRVaWta+dxeKhh34rWluP5vyWlZWJoqKCfu2I5LPPPhPnnLNA2GxWYbWaxbhxOWLSpBJhtWYIq9UsLrpoodi8eXMwl3zp0qV9eeJ/jXo9r9crHnjgfmGxZIg33ni939+WLl0qiot7q6KlpSWFvC+LyMnJErfffrtoa2sLy69+8MEHgzKXmWkREydOEOnpyeKUU04Wp512qsjOtonly5cHz1mz5oN+hYEi86d/8IMbhMWSIQoLC8S5554jzj77LJGdnSlOO+3UqHnl/d+lVzz//POipGSCyM7OFBkZKaKwME+MG5ctMjMtIjc3W/zv//5cNDU1BgvKLFq0SBQW5ocVDQrl8OHDYv78M8QJJ8zoJ/9ut1u8/PLL4sILLxAmk0YkJRlFamqiuOCCb4lnnnlaXHHFFcJqNQfzyENrMdx0043CbE4TWVmZwmrNEMXFhcJqzRCFhfli/vwzRHa2Tdx33339nqe9vV089tijYs6cWcJgQCQm6oXVmiEWL75GPPvss2L27FmitHSq2Lt3T7/z7rrrLmGzWfp0QUrfPc3CZrOKqVOniL///amgXO7atUvMn3+GsNksYtq0KeLiiy8SU6dOFhZLurj55h+HFYUaKxQh5EbIkm8m69Z9wqpVq4b8XXHxeL73ve+FzTB27drJO++sZNeuXTgcdtLTM5gxYwbz589n0qRJYWvdjY0NLFv2Jp99tpnGxkZyc3OZOnUql112GWZzbOuSHo+HTZs28emn69m5cyddXV1YLFamTJnCggULKCkpCc4i9+3bxz//+QJms5kbb7wp6rr7+vXreffdlRQXF3PNNYvDZmkOh4M1a9awd+9euru7MRj0/PSnP0Or1bJixdts3ryZ008/gwULFgwaN1BeXs4HH3zA1q2fU1dXh8lkYtKkyZx88snMnTs36Dru6OjgyisX0djYwEsv/Qu3283q1b3uU4/Hg9lsYcqUyZx++hmUlpaGzZY3b97E8uXLo7Yjmqv7/fffZ8uWLRw4UIFOp6O4uJhZs2Zz6qmnBiP9fT4fzz23lMrKSi666GJmzZrV71put5snn/wzbW1t/Nd//Xe/5Qe/38+uXbv6atFvo7m5GYvFQmlpKXPnnsyMGTP6zagdDgerV6/mo48+orx8P3l5+UyZMpkFC85h3brevOVrr72W4uLxCCFYvXoVn3yyjtmzZ7Nw4cKobW5vb+f999/nk0/WUlVVRWpqKtOnT2fhwoUUFhbFHAPS25bVbNu2nfb2NjIyzEyePIl5805lxowZwYC8xsZGnnqqd7vdW265NWpqZnV1Nf/4x99JTEziJz/5SVTvR0tLC21tbXR0dJCcnExqagpqtYZFi65g69YtvPvuKk488aSwczo7O1mz5gM+/PDDvowUPVOmTOGcc86ltbWVjz/+iPPOO5+zzz47anzGkaUQt9tNUlISGRkZNDc3s2jRFWg0Gv71r38FAzFD4yjWrl3L2rUfU1ZWht3eTVFRMVOmTOWMM86gqKgo2D4hBFVVVbz99tts3LiBjo4OiouL++rqnx1zfMdokAZd8o0lEPATCAwt3qHR6pFrmw6Hg0AggEajISEhYdAdnBwOB16vF71ej8FgGFERiUAggN1ux+/3o9VqgwFBkb85Egw1kIGL5TdH2njE/XrEUAkhgkF+seBwOOjp6UGtVhEXF99Pge/evZvvfvc75Obm8vrrbxAXF4cQgq6uLgKBwIDtHE47Io2xy+VCURTi4uKiDniOtFOtVg/4To/0zWC/EULQ3d2Nz+dDq9UOKiNHn8+Fy+VGr9cHjX60ew3nXbhcLjweDxqNmvj4hBHttBYICOz2bvx+PxqNhvj4+H73FUIEi9cMtNGIEAH8/gCKAmp17HHXW7du5bLLvk1qahrvvbdqwIC73ra6URQV8fG98ubz+YLPHaucALz11pvcfPOPOfHEk3jhhX8OmEng9XpxOp34/X6MRuOgWzAfkYkjvx2o5sJYIKPcJd9YVCo1oynMpNFooq7ZDjQoiHVXtcGfWTVkQZpYFHysBjlSKQ9HGR4hPj6+X5W1UOrr6+ns7OCEEy4JKkJFUWLq2+EMLI5gMBiGVKKxtDOWnbEURYmpgFD48/VGyg91r+G8i6GMTGyyN3RbFEUZsl8URYVGo+o3OFqx4m3Gjx9PScnEsHcaCATYt28fDz30W7xeL1deedWgs9lobdVoNP2ey+Vy8corr7BgwYJ+O9/19PTw2Wef8cgjvUF4V1yxaND+02q1w9IFw5UJadAlEsnXgq1bt6JSqZg9e86I9+iWfL3ZsuUz7rjjdpKTU5gzZw4nnTSLrKwsmpub2bZtG//+9/s0NjYwf/6ZLF68+JhsM/r228u59957eOGF55k9ezYzZ55IcnIyhw/XsmPHDt577z26u7u48sqruPDCC78R/Sxd7hKJZMzw+bzcdNNNfPTRh7z44stR16sl33x6C6v8lQ8++DdNTY1h6VuKopCRYWbhwoX84Ac3DlinYLhs376dJ598kg0bPqWzs6Of98NqzeTaa6/l6quvibnmhDToEonkPxaPx8POnTvx+31MnDgpZrel5JtHbx2HKqqqqqioqKCrq4vExCRycnIoLCxg3Li8ES35DIbf72ffvn3U1dVRWXkQp9NJamoa+fn55OfnYbNljfmGKdKgSyQSiUQiGRayUpxEIpFIJNKgSyQSiUQikQZdIpFIJBKJNOgSiUQikUikQZdIJBKJRBp0iUQikUgk0qBLJBKJRCKRBl0ikUgkEok06BKJRCKRSIMukUgkEolEGnSJRCKRSCTSoEskEolEIpEGXSKRSCQSadAlEolEIpFIgy6RSCQSiUQadIlEIpFIJNKgSyQSiUQiDbpEIpFIJBJp0CUSiUQikUiDLpFIJBKJRBp0iUQikUikQZdIJBKJRCINukQikUgkEmnQJRKJRCKRxILmy7hpm9dNvdtOmauT9h4XakVFgkZHviGBHEMiaTojakUZ8PweEeCgowN/wD/iZzCqteTHJxN6l2aPk6YeB4ijxyxGE+laQ/j9AwEqHO0IEeg9oECy1kiWIWHI+3oCfiocbWH3MGn15BgTw57FKwIcOAZtLIhPHvX7qnJ2Yvf1jPh8tUpFQVwKOpUKd8BPpaOdgBCjeyhFwWYwkaLVD/vUCkc7Hr8v7FiKzogthvd39N204w8Ewo5nGk2kRsjKUKzvqKelxxX8/3i1ltNSbOhU6pivYfd7aXLbEQzepwoKBkWFolKhV2tJ0RpQhrh2t6+HGlcX4sj7UsCsTyBDZxzyubp8PdQ4O8OOZejjMevjwo45/F6qnJ1H7zECUnVGMmN8f6G0e93UubpHJYpDvfcuXw/17m72Ojtp97oAhQSNFqsujmJjEsk6I4ZhvO8j19vlaKfb50EA8Wod4/Tx5Mclkaw1oB/G9SJlu9njpMzRTr3XSXePGxAYNXqy9fFMjE8lZRjPKxAccHbi8XmDxwxqDflxyagUZcjzy+xt+EJ0oFalpiA+BU3IuQI45OzEGaKjdGoNBXHJg9qRI5Q72ukJ0Qeh+ir0HrWubrq87pHPnhWF/PiUYb3rr7RB7/B5WNVUydKWA7zncfR1kxLSZTBZY+DyhHS+lVFAaaI5qmB2+Xq48cCnfOxxjPhZfmay8OuS09AqR1/axvZaLqreFva75fmzWJiRH6HkPEwuXwshAnS2IZG/j59HnsE05GBmyr4PIUR5/S51HLcWzg4TPoffy7UHN7DZbR9xG28zZfCHiWeO+r39q3YXd3YcHvH5J+niWDHpbMw6Iy1eN1eXf8K2UQwQjhj0jwtP5rTU7GGf+vChLfzV0RZ27Lvxqfx5/Kn9Bm/R+LjtMN+u3IQ9bLAlWJ0/hwUZeTE/R1OPizsqN7PeF6IkFBV7dWdSYkqLfcDl7uaEPR/QIwIx9Bug0rBAa+A0YwoXmAuZZEof0AAccnUxrewjCGnrdYkWfls4l7QhBlPVjnamln0Y8o3DmznTuThzQvjv3HbO2v8xDRGDrOHwkqWEK8eVDvu8HV3NzD+wPuwZh4fg3wUnc1Z6br+/OAM+3m06yKvNlfyfuzOqvrOptVwel8q5abmckpJNkkY34J2cAR//bj7ES80H+D9X5PV6rzlOrePbcSlclJ7HzGQbiYNcL3KStLOriTcaK1juaGG3zxOlTwRZah2XGlO41FzASck24tWDmxCfENxTtZWXupuDx2xqLf8qmMO8FNugvS6E4NaDG1npPjrgutSYxNMTzyQ5pF1+EeCx2p080VEXPJaoUvNG/mzOTMsZ/B4I7jy0hTdC9EGovgp9ltcayri1+cCIZXSqWsuKKeeSq48fMxt73FzujR4nP9u/nkWHd7IqaIiVCE2jsNvn4d6Ow8yuWMed+9dx0NkRdd6hQQFl5P9USnRdp4743UDCkBnxu7Webh6v3o7d7x2yLywRz64MMIrUjbaNI1ZS/ftlNM+hVY5tu1AUElFG3DpVlPu/4mxnWWMF/iFmiY09Lp6s34NdBPpdQxnmA33aVstWX09Q9kEBIXi7qYIAYlhvyBpr36FAwM/7Hge/7KjlhPK1/PrAJtoGmXlYIq7xj+5mXqzfizeWAUSkrEd5a4oCCaOUCWWEwqAoo5fFaPe2+708cnALl9fs4BV354D6rs7v44nuJi44tIUb9q5hU3tdVO9Vt9/LHyo/5+KabX3GnCgGV6HK7+Wx7ibOrNzMkn0fsamjnqHe0mG3nfvKNzCvfB0PdtaxOyiT/d/lYb+XP9qbmH9wE7eWfcyhCA9MVA9dxLutC/h4oGYH1e7uGM4N1z3qAZ4sUm93iQC/qt1BubNj2PfQKmOjB3WKMuZ29rgYdJffxy8PbuQfjpaQselQA1/BY92NPFi1FV8gwFedHuC5rgaer9+Hb7TuZMmXwh+bKyhztA8623i1YT/LXJ2j/yYCPl5pOYgrytfwUlcDDW7H8Wm0ENzfUcsvDmykO4bBaK9iEzzUfIBVLdXDHHj8Z+AXgmdqdvHL9uq+OWBsvOLu4mfVW2mJGFx5RYB/1OzinrYqlGHoln+6OvhV9TZaQ5Z0Itlnb+XG/Z/w687DeMRw9KzgKUcr3y/7mE/b64bdR6s93fyhajudo/XUDcK6Hie/q9pGm9fzHyN7x8XlvqWznlfsrWHHUlCYqDMyXp+ARwSodHdT5e+hPkRg81QafpQ9Fa1q6HGHCchV1DHP2kJd7ceKdiF4vLGciXEpzB+BK7j/JxNOPJD3JbcRwACMU1RoY3ySBEUdZg40itLr4Yg0mEBzhMJKRCFeiT7qP9bj3V2+Hv5yeDe/LZob1ZW4x97Kn1oOHpN77ehsYrW7K+rftvs8rG2rYZFt4oivn4FCiqKEyVAAcCFoFYJIFfdidxPnNFVySeb4mOSyLuDn14d3UhSXREl8yjGXsQwULMOQX/UxnP3kKwrxMc51vIh+krjP2c4TrZURcgwTtQZKDIkIBBVuOzU+D/UiwJGFhnhF4ee2yf3iE6qdXfym9SBKiE6IB6ZqjRTpE9ApCgc8Diq9LupFAG/Qi6hiibVkwHiHSlcXPzqwkX/3OPrNRFMVhSxFTZrWQADo9Lmp8vtpj9BKa3xueg59xpOaU5hqSh9WP7/Q3cDEhjJuzJoS03r6SPg/ewsT6vbwk9zSMdGH4xVVr8cxBhIVFWM9Rx9zg+4XgnfbaukKEwSF32dO5LuZEzD2rd35RIBd3a280VjOsq4GykSAe6wTmJyQHlWh+CIE6xR9PC9MPCvmDtMoqjF5wfsDPu6v2UGWIYHxcaMISBP92zhdH8/rJfPRxPjcY2XQC1VqXi6Zj00XF5sbSFFIUveueaVp9fyx6JSobsVDjjYur90e5lT7QUo2V5iLovq/hopXGAl/627grNZqLjbnhynqbr+XPx/eTVmMs9ihvom3WippHWS29XxLJReYCzHFuAYayZJEK/89bkaYBPlEgE6fh71dTfypqYKNvqNmvQt4sbmC8y2FMQdUbfS6ub9qK48Wn4xZaxy5qAuIDP1cnJTJXfknxTy/PZaBRo/ZpnLKMGIh4tXaMN30RWcjNRHBrHek5bEkd3pwoOgXgjJnOx+2VLO0rYrtfi+3JGczPy23nw7b2FFHS4iX0gjck57PDTmlwesFhKDc2cGqlkO83FbNLr+X7yfbOHeAdjj8Xu6t3MxHEcY8BYXz4pK5MXMi0xIzetsmeoN5N3fW81JjBW84Wgj1Y63zebiv8jP+OnE+acMICu0Ugocb91NkTGbBEGvdI8WO4I/NByiJS+bCjLwRTwNEn4cu8ujL409jnDExRs+WMmiMxNfCoAsEO93dYZ9lqdbAxZZCTCEfgkZRcVKShZmJZq60t7C6tZpLLcVRR94KfWvoocYLhTSNfsD16OPJhz0OnqjezgNFc0nW6Ed2EYV+M2ANKlI1hpg8FmOJGkjR6If18QaVkUrNjAFG8vFRXH65ujhmJmYct7Z5heCR+j2ckGgmty9qOoDgvZZDPNXdeEzucdDZwf91Hb2WHphvMPFeyJriux4727qaRhT0B5Cq1gafP5ITTBlMNZm5qvwTdgeOBqJt97qpdHUNa8b9qqOVaYf38JPc6SOOrFaUvnXMCDlJ1eq/FPlOVGtGJNtHNN4OVydhjmRFxTXWCWHKXKPAtIR0piakc7G5kFcaK7g6c0LUgUlnRPBvhqJwiWV8uHFQYHJCGpMS0rjUXMRz9Xu5LnvKgIP6T9pqecfZHvQOKIAJhfstxfx31hTiQj1UCmhVKs5My2VOso0ZNTu5r/kATSFa/TVPN5c1Vw7bq1QZ8PNg7Q4KjYkUxiWNyfusFQF+fXgXecZEpiakjVQdh0XWHyFZoxuFrBx7jotlcIvw0epOfw9Ob8+As7lppgx+mjcz5gjNryJ/7m5mad1eer4G6/+SCGXX4+Sl+n3Bd1fl6ubh+n3HaIALq1qrqAwxpFPVWn6bdxKlEYO/V5sq6BlF2uJgTEvM4Fvx4cqtQvhxD3O90Qv8vqWSt5sr5Xp63/v1RL4zEaC5xzmgociLS+L2/JlR014DCJyB8Oh/p4A6j33Q691TOIcsffQBnSvg5+mGMlpCZpwCuCU1h+9nTw035hHEqTVcl1vKjSlZ/b1KzQdpHWZalwA+6nHy2+qtwz53OPfY5HXzUPU2mgaJJ/gmMOYGXUEhN8IdJwJ+flm5mY3th49ZwEIwAvFLwhYxslYQPNRUwcdtNcdUzX0FHBDfAK0bPsgap6goVevCPoY/tVWxpasBrwjwUv0+NnnDFcG5UZSlLoYljpYeF8vba8N8L+clWpmSkMb1qeGpTy/am6mIIUp3pMRFzixE8D/hRiXC1Rgp660iwG/qvuCL7tYIY8SIs8G+XDFXRnGmQl7EUpQK+MWhLaxoPECjx8lwhvgqFBIjBnotCH5VtZW3Gito8jiHzMyIZL+9jU0hAwIFOFGj579sk2JautCr1NyUU8psdfiE690ee7++FXMAABcqSURBVL+6A9HIUFSoI9aTX+hu4YX6fUNmTgQzVIbAgEJ8xPf4oqONpw5/gfsYDpIVvloKecxd7ipF4VyThRe7m8LcUM8723m+Yh3n6U3Mj09joimdUlMGmYaEIdd+BaLf+nKd38urDeUxGbx5yZlk6hOOaTtPiUtmoi6Bh9qq8PSpxSYR4N6a7RTEJVE4zPV0IXrdv6E0+3t4rbEipgCgk5OsZI3BGjP0rn2903KI1Bg8KHnGJE5KNH8F51FHmajRc7V1Aotrd6CI3lXbwwE/T9TuYrHXw8NtVWG/X2IyMy/RwqrDu8KOxxKuuK6tlvdDZms2ReHijALUisK56XkkNR+gs0+ptQvBe82VlMSnHvOgIXfAT21EtH6WSoUmZBmsVw4FkdnhVyRn4exx8jd7S/DYNl8Pv636nCcmnB7M5ffFGDUdbQ19j7ub1xrLhxwMKyicl5ZLwjH05n3U1UhzDGbXqNJwdlpumBFUgBmmDNKaKmjte/oAsNrr4r3qLZxYb2RhfDpTEjM4wZRBuj6BhCFyuScmWqC5PGyg8ZHXxUfVW5lRt5sL4lIoTbIyM9GMWZ8wZG74ju5makLejQDON2XEvBYMYNHHc1myjU2th8LexsbOeqYnWQY9N1tr4O7UXO5tPOol6EHw+6YKJselcFZ67oAptxoltlmoW6XiWWsJd9Xvpa6vrQrwcOshpsSlstBcMKxASkF/fQwKK1urMHcPHT+SYzAxO8n69Q+KU4CzMvKY21zOWq874gNVeM9j5z2PHdoOUajWcaExiTNTx3Fyio30AaIzFZR+a+hbfB6uqNka05v5XHfqMTfoCYqaH2RPocxj5xVH76ccANb7PNx/6HP+UHxKTEVLQmfi2giB2+PzcGXNtph6/bPCk/sZ9EpnJ+4hineoVArF8SmD5rBXiQA31u0mlgTE35uLx8SgCwQVjo4hUxo1KhXFkWvCEQNGBYX5qdnc5Wjj123VweNvujpZW72V9r4PWQEma3T8IGsKdVFmIkOl/dj9Xt5sDR8cnGJIYkpfTEGOMZEbTGZ+39UQ/Ptz7TVcYZtI1jEsRuEK+Hm5bm9fsZOjTNboyYyQGeVI/mzIqx6nNbLAOp5dFZ/yacjg5E1XJ0XV2/l5/okYVZpej4UYesIbbQ39DVcHb1RvjUnWG5Osx9Sg39tRCx01Q/7uQl08J6dk9ZvVnpBk5bL4VJ5ytIZ7K1DY4nWzpe/66SoNF+hNLEjJZn5aDrYBBuAzksxcH5/B3x0t/f62zedhW1cDdNVjVmk4X2/irJQszk7PI3MAmamM4q4vTcgYlrFRgJMSzRhbDxHqu7LH4NJ2iwCXmwtp8br5feshnH2apE4EuLt2J1lGE5PiU6Oe2yNEzFUmv5WeR4ffyz2N5XT1hVd2CMG9dV+QazRxwjD0kkJ/fawCftiwLyY9eG9qHrMSLWMe43Vc0tbSdEYeK5jDzQc3stHrpmeALjvg9/K4vYXH7S2c27CX/82eximpOcMYScXwO2Xs1vnMOiN3586govwTPg+JIH7O0cr4w7u5LXf6l+oO/F3NDv7Z3TTob0rUOj4tvQBVTAFOSgy/GBsB9gvBbVWf8+EQLulzdXG8PPW8CK9P/2cyqNRcZ5vMp/ZWPuyL/PUA9SHuOYHCTy0TmGxK43AUgz7UGvLu7haedx6tSBUPLM4oCAaT6VVqFmbk8/uu+uAzlvu9rG+t5ju2icPqyR3uLpY17A8tSEgAQXWPk+32Ft52deAM+aMBuCY9f8jqb0d6YlJ8KndnTeX6qi3BiG4X8Nf2Worjkrk6ohrc8Zb143Fv1QDSHafWcE/BbFwVn/KqqwP3ANdvCfh5ztXBc64OTm7az83WiXzbWtzPQ5mg1nJvwSxcBzbwqrMdzwDXawq53pnNB/ihtYTzzYX9Bhz9iwgJsnTDz1LQa/TEKwquEDmq83rwBALohwjcNag03Jg9hT2uTl53tgf9IRu9Lh6s2sYfik7GrBtdsJlWUfE920R2OTt4vrsp6Gna7vPwYPU2Hiueh00fN+LrB4ajB4/TWqnqeH0apYlmXpl4No9bJjBLoyNpiE5Y1ePkvys383bTga9VsE1JQgr3ZE/rl0P7p5ZKVrYc+lJbEhAB7EP88wv/16avY2mPCMQuPXlGE7fZJmEeYMnnelMGl5gLR1SBr0cEeKc5PDd5gkbPnJSssKtNMZm5RH/U9ekCnms+iDswvLKoT7s6uLRmO5fVHv33ndod3NZUzgvOdjqECCokDfAtQyLnZRQM6x7z03K4NaMQU0gLmkWA39fvZUtnI//J2AwJPFlyBi9nl3K21kjGEAp9g6+Hmw7v5M9V26KuI2caEnhywum8kDWN07RG0pXBh8prvG6ur93BU9Xb+5UE1kYZrI9kb4WAEP2WSowqVdQqnNEGS1ZdHHePO4ETImIE/ulo5ZnDu/Ecg4DiVI2eu8bNYJ4uLqy/XnN18mTtLlx+/zdK7o5bLXcFsOjjuCF3GlfaStjZ2ci6zkbWdNVT7uuhKooQHxQB7qrdycxECzlGU8h4sn+OtkVRmKkb2i3pR2BQj12zVSiclz6O/3V2cHNTefB4gwhwX+1OHlJrY6yU11u0Ikw4FRWzdcaYZr1a1di10URvvWNDDEFgpq9ZpsKZaTl8v7OBB9vDXa6lah03ZU0ecR5pjauLv3XVhx0r1sWxvbspYu1dIVWrI3Qa9pnXyca2OuZHqRc+WuJR+JYxkYeLTo5agESIAO4BlL1OUfH9rMmUOzv4a3dTcICwy+/loaptXG0uimmSHW0NvUilYYLWMOS3ElAUVMe43sIcjY5U9dDvOVNrHHRGlKDRcXHmBBaYC/iiq5l1HfWs625kn9fFvojALAG0CcEDzQc5JdnGScmZUb+ly20lLDDns7e7hU2djazpaqDM62J/lECvViH4bUslc5JtzAq5Xv90QIUdznZOTssZVj81ubtxRMhGukY/ZAxUvKIKxjpNSkjl/uxpXFu1haaQa/2+pZKpcSlEVn0wKKphv+98YxK/yT2BKw98GmZn/txWw7S4FDwxDGai56HDfK0xWEtlMNI0huPicDruu62pUEjS6Dk1LZd5abn8yO/loLOTnZ0NvNFWwzJPd1hFpP0BHxvbasjOmhTsj2h56LN08Sybel5Mxm6s3R9aRcX3siaxzdnBUntzUCB2+r38rHobnbGY9Ch56FN1cSybcm5MBWOitTGWgUQs49V8lZpnJ5xBdgzrumPZ1bFMKsQwfSJGlYbrsiazwdHKR33rwyYUlliKKR1mJayjrjnBhy3VNEUo3WXOdt45uKnf7yOVWLMQvN58gNPSco5pRbQZah0/sYznIkvhgPUSFEVBO9iATa3lttwZ7C1fF1yqAHjD082eut0xuiP7r6F/N8nKA0Unx7QD27EOGPx15hTOsBTGps+GuLdCb+GZ2Sk2ZqXYuKWvAMzuriaWt1XztquDdnFUStsQvNtSxQlJ1gFrcCRr9MxNyWJOShZLRIAKZyf7u5t5o7WKFRHXqxcBVjQfDDPo04z9A3R3dTXhCfhjriXgF4J3O+v7LZ+ajUPnkvcQCH67KhTOSh/HrY527myuCP6mHcGPD++gK+L9+xDD/qYVYFZyJndbJ/Lj+j04+s7vRHBL3Rcxedyi56EL/l58Kvkx5M8ryvGJiP9Stk8N7aQEtZZppnSmmdK52Dqe02p2cktIeU0fsNnVwWVCDGmIVShficIy9H10d46bQVX5Oj7sMwwBYKfPM6oeU6GMWIHdapvMEn/xEH3Ym1ISiyJTfYl9rVYUfpcznd8EBq/cplHUUQtCDEaBMZGfZE6irHor9SLApQlpfMdSPOLNbjq8Hp5uOxRFsUGslaxXOtu40d7CFFNsRXZOV+uYbjAFVV+dz8Nyjz3sfvlaAwvNBUMUP1J6DcsgOrQgLom7c0qprdxMecjSwD5/zygkvVf9fRnfs0phTGT7SHsmxKcwIT6FCyxFnFVfxvfqvggbdK9zdxAQYsjBm9I3eZgYn8LE+BTOMRfwSl0ZP2vYE1ZCudrdjSvgD84kJyVmMF2lYXvIu/qLs43vdTQwJzUrprbs7G7mY0d4mqJeUTE3yTrkuZHR4lpFxfezJlHm6uDZkMyJyigBvD4hRrTNrlpRuMxazA5nO090Hq09Xz/KFDZljGTlK2/Qyx3t7LW3cn5GwYCVzkwaHd+1TuCW1sqw6Zcj8PVc5xgfl8wd2dM4dGgLhwK+Lz0SoOQ4Vlwb+8GgwuSksUuHOyd9HNd0NfBeVyM/zpoStl3jsLwIwKftdewcZdGMyoCfVS1VTDbFFo18eaKVHxbNCf5/a48L774PWe6xB+XwDXcXC+rLuD5n2qhn/vNSsrjVOZ5bG/aGBUn9p9LocbKmrZqFGYUkaKL7OPQqNQvNhcxqOcjmkGwBh9/Xz59W53Gwoe0wZ2fkDbjsY1Rp+HbmeN5oqWS59+j13EL0pRD2GvQ8QyJnJqSxPaRaoRq4r2YbfzOayBkifa25x8XD1dv7uflvSEgn0ziy7KF0rYE7cmewv2I96wcowjNakjQ6fppbSkV5d9iWrN8kjktQXIPHwc8PbuLi6m08cHATe+2tAxZDqPc4+vlSC7VxQ6e+AF+xHP/elL3UbG43F4cFDo12RCgZJSKWHGM1P8yexq9sUyiNcVasV/q7Kz0BP882VRCqovJUar5lSOTCIf7lhcRBKMCyjsPUu+0xDyRCSdMZuSOnFBEhQHc3lbOls2H0MwNF4ZrMCSxJsn0jhoujocvXw32HPuOq2p3cXPYx2zsbBtyBscvXQ0dEwKNZYwj70O1+L48c+pzLa7dxS9ladnQ2DZjj3+BxcDjCa2XWaEkIqS+gUhSuzZxEQYgnzg+82+PkR+Xr+LyjIWogsl8I9jnauLtiAy+6wrNL0hQVi60lGEcRuzM+PplfZk0lS6UeM1WebTBxZ04p49XaY3KP/7jCMh0+D7879Dmvu7tQgPvaa/hnZz2Xm8ycnZJFtjERrUqDXwQ45Ojg4fo9YecnANNMaWHuzmiFZer9XpY1VMTcvXEaHaenZh/TTR2iKzoVV2ZOYJernb91NhCrryFaYZkWfw9vNlTEPJuKU2s5PS3nmLexSwjeaz5EWoyzVpWiYn5azlenlG+M/ZdrSCDLUhRzf0eLTt7Z3cyGiHzvW9LyuTZ7ypDXe7OhnP+q3x000Ot9Hta0VnNN1qQRNXtWso0n0gv5cchaZbMIcF/VVp6PO5M0nXFU3Zqg1vLDnGl84e7mvQF2khtI1iO/izJ3N282VsTsXjXr45mbnHlM3J+fdDXSPoytRPPjU5jeF1/hFQGW1n7BM32z32ecbbxdvo6r49M4IyWb8fEpaNVaBIImt52n6vexP2QLUTVwnik9KHM9gQDP1H7Bw10NgMKzzjbeKV/L1aYMTk3KpCQ+DZ1GQ0AI6l3d/KOhjM8jNhDKi0/rpxenmNJZkp7PL5oPhOWRv+Wxs6liHf8v0co5qTmk64woikJHj5t32mp4q7uR3VE2KLorLY/SUXrMFBTOTMvhTlcnP2oogzHwaSrAKUmZ/DJzIj86vIuOGOVroMIyq1qrsHQZY1Q7CnNSbFh1cWOm2sbUoAtgeeMB/tZXKONIdxwM+PhdZx2/66wjRaXGrKhwiAC1UVzrM3RxTDWZ+734yKC4z3weLoup6Eov5+lNzEq2jrlBP+LquT13BhX71/JvjyOm0o/RCsvs9nn4Tu32mO97oT6Bk1Iyj3kbD4kA1/cZmtgkWUV1YsZXqDZ/7Ep/OK7oyFlNAMHbzZU0hCiCOJWac9LzYoqWPyt9HLOb9rMpRIG+21bDRZaiEfWlWlG42jaJT7oaedVz1OX4YY+DZ2p3cVv+SaM2iLkGE/flTufggQ3sj3V/9ShBca+5OngtpsIyvdyWZGNWspVjsUHl3R210FEbs5Z70jopaNA/ba/jty0Hw3LPm0WAx+zNPGZvJk5Rka1S40Nw0O/vZ7RsKjXzUnOC7djQWc89zRVhgcJNIsCjXY082tVIsqLCqlLjRXAgyprzVJWGC9JyowyyFW7MmUa9x8HjXQ1hgZgNIsBvOuv4TWdd3+BXGdCrpaV3Z7/rckpjKn08FFpFxTWZE9jrbOfJrrFJfVQpCpeYC/nC0c5D7TUxa4xohWVuaoh9j4ckRcUHRtOYGnTVWKvNS63FPGQuIneAl90e8FPm90Y15jmKivuyp2HRH/sOON6u63EGE3flzqBAffziEL/cPdnCO/s/caXgoLOLtzrrw1T2TQkZFMRYBjjLkMD8xPBykS+5O9nR1TTiZ0rVGbgtp5SkkO/RBTzeVs2HrdXH5Js/IdHCLzInkXAcP7IvT77C7zw3OZMnsqYxWaWJ+v05RYD9fi8Ho6yTp6LwG+tESkKqpM1OsvBM1jQKB3Bld4gA+/zeqMY8TVH4iWU84wfYYSxOreWXhXO4O3UcSYO5TwYw5umKwi9Tc7mrYBYmjfaY9WiyRs9tuTNYcIyreUZ6k27OKeUyY/KI9eRws+T1x0FOx1znJ6i13JQ7gzeK5rEk0UJhDKM4LTBHo+eZcSdyamrON8YYnJKUyS+tE0mRC+H/Efy75RD7ItZHz08bhy7G7W8V4NvpeVgj5GVZU8WoNpg4MTmT+9PDi8gcFgF+V7uTeo991O1WKwqXWwq5LSXnP+6d61RqLsssZlnJfH6eks2kGL1jpWotj9om893MkjCvkEGl4ZLMCbxTcga/SslhYoxr1NPVWh61TeGakHTfaCRqdPw0/0ReGXciF+kTiKU2mwE4RxfHs7kzuT3/pGGVtI6VfGMiP8+eRskY1tPI1Mdxe+50pn2Nd/WM5LhMF9WKwsxkK9OSzNxkb2NzZz2fdjVR4bHTIgJ0iAAGFHJUGnJ1Rs5LyeHUtJwB61erUJiki8M2CsOYpo3r556LU2u5KmRUKISIupWgWlE4RxdPIKT4RE4MQq1WFC61FFHl7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'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 20:15:01.770774 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 20:15:01.770774 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 20:15:01.770774 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:15:01.770774 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:15:01.770774 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 20:15:12.106022 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 20:15:12.106022 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 20:15:12.106022 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:15:12.106022 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:15:12.106022 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:15:55.889302 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

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+INFO:root:2025-10-18 20:17:11.140270 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:17:11.140270 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:17:11.140270 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:17:15.515195 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM 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16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_476.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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+INFO:root:2025-10-18 20:18:11.466140 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:18:11.466140 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:18:11.466140 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:18:11.545764 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM 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16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_926.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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+INFO:root:2025-10-18 20:18:41.846219 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:18:41.847222 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:18:41.847222 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:18:42.021400 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_095.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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+INFO:root:2025-10-18 20:20:10.026343 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:20:10.026343 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:20:10.026343 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:20:16.738261 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM 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16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_156.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 20:23:59.109036 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 20:23:59.109036 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 20:23:59.109036 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:23:59.109036 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:23:59.109036 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\base_class_calcul_note.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-18 20:25:56.573311 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-18 20:25:56.573311 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-18 20:25:56.574309 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:25:56.574309 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:25:56.574309 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:25:56.954145 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 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+DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_989.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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0RhuKMU/Zqs5woAbCtKhprveBAHIQQQGjEdhNo44S15MLyMfCwfVuRhT0EydN0YcEVHPLYVJKLESJ5v9wzEYLueXZKvhVCEXveRMAeA/Px8bN68qU1BVgDgt99O4bffTjcKI9we9Ho9fvzxx1Z5tIvFYjz99LN47733MHLkyGaF+Z1apkwmw7Jly7Fly1b07t2n4TeJxAzLlz+NTz/9PwwdOqxTNd/W1gnRvR3ISSAQYOLESQgPH2ky7ZUrl3DixIk29aOioiJ8++23JpdGxGIxFi5cBDc39/taPjGB3laTBsehl42hx7yCCIVqZbufq+J12F6cgoomBu/nrJ0RZPNfr1pbkRmed/ZtZFrhAeytK8dNRXmXdpRJPTzxumsgHJvMxOtBWFt0E6cr8rrNSe5mXSUO1hSj6RRmtJkVJrn4QMgO2+g0wbNnzx5kZma0fXIoFGLjxg0oLi7uUBkKCgpw8OCPrcrv6aefxcqVK9u8vUwkEmHkyJF4//0P4OPjCx8fH3z++edYvXp1qxy/7mfs7e2xbNkyk+vDHCfA9u2RyM3NbXXf++mnn5CUlGgy7ahRYzB27Nj7vi2YQG8HjhJzND2NRglCja793q+xNSU4r6oxuD7LxbeRQxcHYJyzF3ybrJcrQDhQnNqlWrKQ4/CYey8ssHOHWWMFHtm8DmtzopFW1/UBOAjAn5X5KDTyrk+5+MNFYsE6aSeRkpKCPXu+b7emePNmEg4fPtyhEKDR0dFITU0xmW7WrNl49tlnTWrlzQscDqGhofjiiw2IjPwOERFTWhURjQEMGTIU06fPMNlPUlKSceTIEeh0OpPPzMrKwu7du0xq9NbWNli0aCHs7Ozu+3ZgAr0daHjeqJBpr26qIx67i1INDkSZa2GHUCOmY0exBZ519DTIf0d1IdK7OHqZrVCM1Z4DMNnC1qDzXNCq8K+s6yju4m10PBHO1hSj6fTJSyjBKAcpBGDaeWeg1WoRGbkNJSUd07A3bfoKOTk57fs2dDqcP3/epLm7Rw8nLF68uMN7jzmOQ79+/eDl5cWOVG0DVlZWmDdvvkmnL4FAgG3bvjVp8eF5Hvv27WuVZSgiIgJDhgxljcAEevuo1KjQ1JvVHByshe3bKpGsqMBxheG+2kecvI2e7iXkODzo4gP7JuvZubweJ0symvFT7zxczSzxludABIvMDETnobpybM6L7ZT1/ObIV9chpd7wCMiJFrZwkJizDtpJXL58Gfv37+vwc8rLy7B3795WaWVNKSsra5Uz3oIFCxEQEMga7S4SHByMJ56Yb1KjLikpxt69P7QYEjYxMQH79v1gMs8ePZywcOGidltlWp7cARUV5Q2n8rX3Lz8/v83+J+2Febm3VTsnHtdrDDUWO04At3Zs3eJB+LEkDalNBOAEiSVG9fBo9j5vSzu8aNsTa+/w8tYD2FKehWluAW0Kj9vmjg4g1NYZa2XBWJ59HcX035VzFYDPy7PhZW6LJ9x6dcladrlGiXwjE4Yhtq7d7mnf9rr7e2h9tbW1iIzc1qqDSYjIpDa7Y8d2TJ48GSEhIW0uhylzOxFhxIgRXb7Hl9EyYrEYjzzyCH7++RiysjJbtILs3r0LERERGDBggMHvarUae/bsQVFRocl+9cQTT6BPnz5d861yAqxY8Qr0el2HnuPr64fffjvTJZOObhPoZdp6FKg7LwCDABxczCzuujk1X1WLPYoSg+v9Le1bfVb2nWQpa7C/qgAcGpvspzvIWlwLFnMCTHf1x9rqgkbRJG7yOpwty8Z8Wd8uNu1wmOTkhdWqGrxSnNKo9BVE+KgwCd4Wdhjp0PlepyX1ChQZrJ8T3Mys7+0Rjwjf5NyAo7DzYuALOQGe9Q2DuJPPqb969QqOHv3J4Azxpnh5eWPgwIE4cGAfWtqDXVtbi507d6J3795tOme8oCAfdXUKWFg0P1kOCuqNgIAAJlHvAXx9fTFv3jy8++6/W0ynUNRix47vEBQUBAuLxuNcVFQUDh48aFKYe3v7YPbsR7t0371QKOzwRFEg6L6JZhcJdA4v5dyAGdd5Fd1LZIadfSbAWnj3NLA6vQ4bc2OR3WTGZgbgIXt3CNvxvqfLshHbZLvbEJEEU1x8TdeJTQ88Y9UDXynKGq5pAewsy0SEqx96iLt2RigRCLBQGoQMVQ2+qCls9FuiXot3c6KwwcwS/padG0zB6Bo9ocsOPelEiY73aks69Ym9OQGepsGdOxkvK8WGDRtMCnOe57F8+XIMGzYcly5dQn5+y+cPHD16BFOnTsXo0aNbXZbS0lKIRC1/84GBgd2i/TBaI7wEeOihh/Dzzz8jKup6i2mPHTuGGTNmYNSo//aHuro67N69GzU11Sb3tS9duhRyuZxVeiNFq4vI0muRrFN32l+9TtPla8MtoeJ12J2fgA3VBYZailCM0T082mw7KFTXYUd5lsF9D9r2hIeRuO1NsRCIMMvFz3CSoFHiXHlOt9SLg8gMKzxDEWFuWN5f1XX4JCcaFdrOjX1dayTMKzgOTmLmkdxReJ7HkSNHceWK6UAeffr0xcSJk+Dj49OqEKB1dQps2vQVampa77hZV6cEZ2Ki7OHh2Satn9G1uLm5Y9GiRRCLW7ZE1dersG3bNigU/z2O+dKlizh27IjJvhQWNgSTJ0d0W1S8+16g/69AADJU1ViXcRUri5JhTDStdPGDczuOiv29PBfntapG05QAgRCzXP1bPTkYaN8Ts80ar5frAPxYkoG6Dq79tBZPCxu86dEffY2Ent1eU4xv8+I71UnO+EE4BP09HoTj70BOTg42bfrKpJlQr9fjqaeegouLMziOw4wZM+DhYTqi1p9/XsCxY8da//21ok27y+GI0XrGjh2HkSNNB5v5/fffce7cWQBAVdWtMwBMOU9yHLB8+VPsKNv7UaDzAIo0KmTXK1r9l1VfizhFOc5X5OHTzGuYm3QGaytyUGvEQjDD3AaP9AyAoI3OX1VaNXaUZRg0xmRrZwRZt/54RjuRGR4zYp7fVV+Nq910aAsHYJi9G/4h7Qf3JtqUGsDHZRk4UprZaSfS2TSz7FKkVbEvugPodDrs378fhYUFJtOOGzce48aNx+11czc3NyxfvtykZkVE+Pbbb02a529jbW1lMkpYfn4etFota8B7CDs7Oyxa9KTJbYQajRqRkZEoLS3F6dOncfbsGZP9Z8qUhzBixAhWyUbosgXpXgIRLDrRw9lGKGqXh3AdgBezrqKtxth6AHVEBpHbGmnHIjO84z0Yju1Yu71YVYCjTbZeuXIcHnX1b7Nn+IgeHphQlIyTTQTajyXpGNFDDjHX9fM2DsBUFx8kq6rwUWkmFHfUWwnxeCcvDr4Wtgi1delwXs05lZVq6u/tr43jsKdnENwknXeQjYDjIOoks2NycjK2bv26VWkXL17SaM8xx3GYPDkC33+/2+TRqTdvJuHAgR/xwgsvmJwAODk5Q6fTtWi+TU6+CY1Gw9bR7zHCw8MxderD2Lv3+xbTXblyGXv2fI9ffvnFZH+QSCRYsmQpbGxsWAV3hUAnMrayTXjPMxQDjIRIbb8pgYNlO7wFedw66rOzCROZ4VOvQejbjndU6nU4VGoYMGGUhR0G2bm2+XkuEgtMdZDiZElaYy29rhxP1pQitB3PbA8WAiGelwcjW1WL7YpS3Kkzxes1eCv7Or70D4dnB7fUuZlbw47jUN1I4+eQV19zj39uHAY7yuBzDx65Wl9fj61bv4ZSaXpnyvTpMzB0qGEgjx49euDZZ5/Ds88+Y2Jew+Hbb7di8uTJ8Pf3bzGtVCqFlZUVeL75iXViYgJSUlIwaNAgNqLfS8JFJMKCBQtw8uSvqKgob1GGfPLJxyaXV4h4LFiwEH379u22dxCLxR32cpdIJN0WpKjDAp3jjOvNPcUW8DL/35xFPW5hj1Weoehv69KuTXQxNSXYcodneoNA5ATYXZDUrmdW67V/6cl3bh/j8WNJGvrZukDUTR3KXmSGVV4DkJb6B8422bb4s6oGn+VE49++Q2HdgaNebSSWcOMEqCZ9IwvB77UlWKjXwkbIHKTayqVLl3DkyE8m092KCDbPYKvR7bFg7NhxGDduPE6f/q3F55SXl+O777bj7bf/0eLZ1dbW1vD19W9xLzrHCXDu3DmEhoayvej3GL1798YTTzyBL75Yb1IxNIVM5oE5c+Z0WzheIsLbb/8Dw4cP75gyKhB2m9MmCyzTBgaIzPCUkzcecQtAD3H74oVriceh0nSjv0XWVSCyrqJTy7y9uhCLlFXw7UatMNDSHv/2GIDF6ReR2sQj/bOqAnjlJ+IZWT9I2mkqlptbw0tsgZtqxR02IeB4fS1K1UrYWLKYzm1BoVDgq6++bDFy153aef/+oS0K/KVLl+G33061qJVwHIcdO77DlClTjWr7t3F2dkb//v1NBpfZtWsnHnroIbYf/R5DKBRi9uxHceTIT8jKyurQs5544gn4+3df+xIRpFLZ3yoCIfNybyUPmtvgcNB4LPUIbrcwB26Fed1eXdRt5c7l9ThSkt5pDmmtZZi9G95072PUj2JdSSpOdWBbnaVQhCFGHAdreT1Ol2d3+7v+3fn1119x5cplk+lcXFzxxBPzWtSoAWDgwIGYOXOWyefxPI9vv/3G4AzyRhqHSISRI0eZPNylrKwU33yzFXV1HQ9mFR8fh+zsbNYxOgkPDw8sWbKsQ7sR+vULxrRp05kF5n7X0C0BvO3iB3+L1mlt5eo6PFV00+B6oroOefW1kFm0fxlBT4QjJekope7dZrOlIhdz3YPQ08yq2/IUchxmu/ohS1WNf5VnGUwy3smNgZu5Nbza4SDGAZjg6IGvKnJQcofw5gB8VZqBSc7eXRr69n+JwsJCbN68uRWnoXGYO/cxBAUFmXymhYUF5s+fj6NHfzLpfX78+M945JFZmDRpUosTBF9fvxbDiQLA3r170LNnTzz33PMmJx3NER0djVdfXQWxWIznn38BEydONBnYhmFCaxQIEBERgUOHDuL69WttF1KiW2edy2QyVpn3u4YuBjDWQYZHXP1a9bdI3g9rHQ1jqOcQj49ybqCyA0FS8uoV2FmVj+7eNZuk1+BUaVarwvLwnRi+x1IownMewVhiZahNX9fV44PsKOS3MzxwiK0zhjbZf08AonRqHCpOY3vSW8mBAweQlJTQKi1rzpw5rdaQgoODMW/eApNroxzHYePGDSgvb95pSiaT4eGHp5l0LOJ5Hhs3bsTGjRtRU9M2B0mdToerV6/i7bffQnLyTcTHx2HlyhVYv/5zlJSUsI7SQZydnbFs2fJ23Tt06FBMmDCBnX7HNPS2I+EEWCbrhzO1pTjTZBvYr2oFduTH4wWvgW12XCMQfi5JQ3qTMK/2nADP2Uth2UkhbXkiHKkpwhVt4y1cm8rSMa2nH2xELTuUEBE685w0J7EFVnsNRGrKeZy/o0x6AD8qKyHKiQLa4QZoLRTjmZ6BOJd1DdVNpiAflaShv7UTwnvIOz3yv5Z4XKjIx0A7V9iIJH/rvp6eno6dO78zbRHhOCxevKRNGpJEIsHjjz+OI0d+QllZaYtpY2KicfToUcyfP99o5C+O4/DYY3Px88/HkJaW2uKzNBo11q//DElJiVi+/CmEhIS06JDE8zyqqqqwb98+fPnlRlRW/teHpa5OgfXrP8fVq1fx+utr0K9fPxaZrAOEh4cjImJqqyLB/dfaY4nFixfDwcGBVSAT6O2jp7k1Vkv74M+sa40iwykBbCjLxHB7KQbZ92zTM0s19dhZnm0QaW6JjQve9A2DWScF8CcCfPMTsbAgvtG2sQsaFc6X52KKq1+L9ws5rtM7hb+VA972CMXTmVeRfoeTnB7AHmVVu587zFGGCSVp2K+sbHQ9n3isybmBrebWCLRy6DShXqfXYnNuHD4uTcc8O3e87TMYtn9Toa7T6bBz5w7k5+ebHFz9/PwxderUNmtIgYGBWLhwIT755OMW7yUibNr0FcaNG9dsbG53dymWLFmKN99cY3ItVq/X45dfjuPcuXOYOHEiJk16EL17B8HVtScEAgGICDU1NcjNzcWff17A/v37kZubY/S5PM/jwoU/MG/eE1iz5g1Mnz4dlpaWbJBsB7a2tliwYAHOnTuLujpFq+6ZNGkShg+/O0FkOI5DcXERQsOCJAAAIABJREFU0tPTO+2Zjo6OXTo5YQK9GcY4eeHVqkK8W9U42loqr8enuTHYaOUAhzbEDj9dloUYXVMvYg6PuPrDojOP/OSAsc5e8ClORnITa8Dm4hSMdfKEZYvburrGrDXaUYZVymq8XpSEmjtiF3TEMG4rkuBptyCcybiIiibxEC5qVXg+9QI+8QlDSDu3F95JmUaFT7Ki8FFVPgDCF1V50GcQ3vEJ+1tq6lFRUdi1a6dJIc1xHJ555lk4O7cvpsTMmY/g4MGDyMhoeVDMz8/DgQP78eKLLxoNO8txHKZMmYITJ34xGU3sNiqVEocPH8JPPx2GSCSCubk5HB0doVSqUF1dDb1e1wrfgVtUV1dhzZrVuHEjCq+/vgY9evRgg2Q7GDhwIB599FF8++03Jvuera0dnnxy8V2bQHEch7fffqvTrDJEwLp1n2LGjBldVmZmP2oGM4EQS2T9MExkZiAMvldVYV9BEvhWiqNqnQbfl2agrkn6+Zb27QpMYwpniSWecjDUdH6vr8W1qqK7Up9iToD57kF42l6KztqRyQEY7SjFuy4BBi3BA/hNU4elaX/idFk26tsZS17D3zKxP3vzLD6qymuYgqgBbKjKx/sZV6DQ/73CjtbV1WH79kjU15uOrDd06HA88MAD7V6/lMvlmD9/fqvu3759O5KSkpr93d7eHmvWvIGgoN5tHEgJWq0WtbW1yM7ORmlpCTQadauF+Z3PsbOzYxHpOjKumplhzpy5kEpNL988/vjj6Nev310tr16vh1ar7aQ/TZeXlwn0FvCytMNb0r6wbCLSOQCfl6Yjprp1zjIXK/NxSmPo/DXXxQ82os4POCDkOEzr6Q+XJjPLKgB7SlKh4fV3pT6thCI85xGCKZ14nKqIE2CerA9W2bkZ/f26To0HMi7jzZQ/EFdT2upDYlS8HimKCnyYcRnh6Rew30gUOi2AWr0WujbsWrgX3HouXryIQ4cOtkKA8XjmmWcahXhtDw899DD69DEd3au8vAzffvst1OrmHU979eqFd95ZC0fH7tWQiQhTpz6E55573mR8ckbLBAYG4rHHHmsxjUwmw5w5c9gOg7aOh6wKWma8szeWV+bh/2qK//txA0jkdfg8NxbrrUa3uI5ap9dhb3EalE22V0VIrDDEwb3Lyi23sMXTtm5Y22TJ4KiiDE8ryhHcCTHV21UuM2v8y3MgclP/wDVd5xyrai0UY4XXQBSmXsCuJuvpt1vs05oi7FKUYpalA8LtpRhg6wJzkQSWQjEshUKoeR4qvQ4KrQpRNaU4W5mHSGUl1H8Ja2O2mNfspVjjPRj2RhwNtc0I+cNlWXCuLu6SunU2s8TEHh4tpqmsrMTWrVtb5a0+bdoMDBkypMPlcnFxwZIlS7Fq1QqTWvGxY0cxffqMZk/q4jgOYWFh+PjjT/DWW2+26iCZjsLzPGbOfARvv/02c87qDIVDKMSMGTNx/PhxJCYmGKlvPZYsWQovL29WWUygd7KJSCDEsx79cT7pDK7rG5tMtisrMKrwJhbK+jV7oEpibSl+UFU1ES/AbCdv9BB3nelOzAkw1cXXQKDnEuHHolT064R15fbS16YH3pKH4Jns6yjsJGuBm5kV1vmHQ559Ax9WGT/Jq5jXY6OiDBsVZbAVCOEjEMFdIIQ9J0Qd8SgjPa7rNKhvhcb9vpMPnvfo36yFxeiBOMTjleKULqvX52x7tijQeZ7H8ePH8ccf502uCwoEHBYsWGg0xGt7mDRpEvbsGYzLl1s+Z12pVCIychsGDBjQrCYsEAgwfvx4ODk54R//eBsxMdFd9x2JxViwYCFeeOFFODo6sgGxsyb2cjkWLVqEN95YY3BcamjoQEyd+hALItMOmMm9Ffha2uNVtyDYGjG9v1ucglSF8T20WuKxvzgNyiYCYoTIDOOcPLq83H1snLHU0nAQ+qGmECmKirvY6ThEOHniTRd/dKa7i6uZJV73GYwNroFw4wQtTlhqeD2idWr8rFFit7oWhzV1uKCtNynMewnF2O8xAC97hnbJcklHMDVBKygowMaNG0wKcyLC44/PR0hISKeVzdraGs8993yrYlqfOnUSp06dNDHhEKB///744osvMGPGzC4xzbq5ueOTTz7FqlWvMmHe2X2V4zBx4iQMHhxm0PeWLVsGV1dXVklMoHfdQPlwTz8ssnGBsImmncnr8Un2Daj0OoP7UhQV2FFraF59xEHeLZHMLIUizHU1PM0qidfjRFnmXa1TMSfAPPcgLLbr3GUHO5EET3uEYL/fCCy0doJrJwWjkHECvGjnhoO9xmBmT39YCP9exi29Xo+DB39Ebq7pkLv29g54/PHHOv0QjGHDhv11hjpMTii2bt2K4uJik0LBy8sb7733PjZt2oIRI8I75RAMe3sHLFiwELt27cb06dPZmnkX4ejoiMWLl9zhZEiYNOlBjB49hlUOE+jNDA6d9BwLgQjPe4Sgt5EtX7uUFThUlNIoxhoPwvHSTAOTcm+BCBEu3bc2NNDBDbPMbQ2u76/IRV694q62jZ1IglWe/THF3KZT20zIcRju4I5NvcZgl/cQPGZhD6927vPvLRDhFTs3HPAPx8cB4ehl5Qjub9jX09LSsGXLllvHHbfwx/N6LFiwsM2e5K3BzMwMy5cvh0QiMVmOqKhrOHLkp1adwmVtbY0JEyZg06bN+Oyz9Rg9egxsbe1aHTuciCAUChEY2AvPP/8Cdu/ejbff/gf8/Py6NDrZrfrmm/2jLoh2SIQW8+xIvPX2EB4ejgkTJt5SQCytsGDBQtjZdd/hSqbqorP/upoOqxm2IjMscZA3GkwIBEdx92/tcBSbY4W91EgZO2efsK+VPd6Th+BsVaHBbzGqakzUqhvWxau1GlQQb1AeuZk1/Cy7z7HGXmSGBa4BkFcXNhJEBCBNWQWZuXXjwVEkwQJHeaMDTngQLLtor7WnuQ0+8h6CvnesLXMAHDqh/5gJhBjv5IlwRxmSFRW4VF2IazUlSNHUIUWvRSHPAw2BeDmAE6CPQIheQglk5jYYZ++OXjbO8LO0g6CNA7uVxByrHeXoTr92XwvbZgVHcXExli9/yrTlRCzCjBkzu2z9sn//UHzwwUcoKjK9fVIgEKKurg7W1tamrWgcBzs7Ozz00EOYOHEi0tLSEBcXhytXrqCgIB95eXmoqalGfX09hEIhrKys4eTkDC8vT3h7e2PkyFHw8/ODs7Nzt4QYFYlEeOONNw3Wjxu/vwBOTk6dluft6H3Tpk1r2Rolk3VbRDwrKyssXrwEp0//hhkzZiIsLKzbvpeIiAgMGzasW2VUr169uvT5HBELes24f9AToUSrQq1WDZ1OC9xeM+cE4AQCmIskcJJYsDPV/4dQKBSorKyERqOBXq8Hx3EQiUSwtLSEk5MTc766y+h0Oqxfvx4TJ05A3779WIUwgc5gMBiMvyu1tbWwsrJisfKZQGcwGAwGg8GmQwwGg8FgMIHOYDAYDAaDCXQGg8FgMBhMoDMYDAaDwWACncFgMBgMJtAZDAaDwWAwgc5gMBgMBoMJdAaDwWAwGEygMxgMBoPBBDqDwWAwGAwm0BkMBoPBYDCBzmAwGAwGwxARqwLG/QARQaVSged5mJmZQSxmx6MyGIz2odfrUV9fDwAwNze/Z47gZRo6475AoVDgrbfewpNPLsKNGzdYhTAYjHbB8zyOH/8ZTz/9FNat+6RBsDOBzmB0EzU1Nfj552O4cSMK7u5urELu0DTy8/PATlFmMFpHQkIC/vnPf0Kr1WHRoidhZWXFBDqD0Z2kp6ehvLwUwcEhsLOzZxUCIDn5Jj788ANs3rwZPM+zCmEwTFBYWIj3338PcrkcH3zwATw8PO6p8jGBzrgvyM7OgZmZOQYOHAQLCwtWIQAOHTqEr776EsHBwRAI2FDAYLREfX09Nm3ahIqKCnz00Ufw9va+58rInOIY//Oo1WrEx8eBiODv7w+RqPXdnogamaNNCb4707dXSLYlzzvTcRzX6F6O48BxnNH0CoUCN2/ehFgshlQqa/Tb7XuM/Z+IjD73Tg3f2O/tLa+xe1tTnq56bnO09f1bWz9tKWNb3rm179KZfa8tfd1UPbVU562pY2PlNHWfXq/HrFmzsHjxYsjl8ja9W3dNmJlAZ9wHAr0eUVFRUKvr4ePj0+pBLTs7G9euXcOVK5dRUVEBBwdHjB49GsOGDYOTk5NB+sTEBJw+fQapqSkQiyUYP34cRo8eA2tr61blqdfrkZ6ejtjY2L/yrISjowPGjBmLsLAwgzwBYNu2bSgpKcaMGTNRX3/rPePj4/6yRgzE2LFj4ejo2JD+woULOH/+HOrqlLh27Rp4nsePPx7AmTOnAQDTpk1Dnz59UVZWhm+//QZCoRDLly9HYmISzpw5jczMTDz77HMICQkBEaGkpARxcXE4f/48iouLYW5uhpCQEISHj4S/v7/B4BgVdR3nzp3HgAGh8PHxxcmTJxEdHQ2VSgl/f3+MHj0GgwcPNvAa1ul02LRpE+rqFJg9+1FoNBqcPXsGsbGxePDBB/Hww9MAAEqlEjdu3MCFCxeQnp4OgYBD7959MGrUKPTp08dgMpebm4vvvtsOHx9fPPDAA7h27RpiYmKQn58HPz8/hIeHIySkv9FJIM/zKCwsxLVr1xAVdR25ubmwsrLGyJEjER4eDjc3t0bvX1paiu+++w48r8eyZcthb2+49BMfH4ejR4/C3d0dCxYsBMdxqK6uxtdfbwERYdmyZUhPT8eZM2eQlpaGxYsXY/DgMOTkZOP48V8QHx8HgEN4eDjGjx8PZ2fnVk8i8/Pzcf36NVy9eg1FRUWwtrbCyJGjEBYWBplMZjDBOH78Z0RHR2PKlCmwsbHFpUuXEB8fB41Gg5CQ/hgzZozBfXdOsuPi4nD16lUkJyejqqoK7u5ueOCBCRg8eDB++ukwSkpKMXXqVPj7+zfKNy8vr6GcxcVFsLa2xogR4Rg9ejREIhG+++47mJubY8GCBbC0tGy4T6FQIDo6GteuXUVmZiYUCgV8fHwxadIk9OnTG9u2RYKIx9y5jzX61nQ6HZKTkxEVFYUbN26gsrIC7u5SjB49GuHh4Q153Eaj0SAq6jr++OMPJCenQCp1x/jxD2DYsGFtUibaqw0wGP/TpKSkkFTqRoMHD6KSkhKT6Wtra2nz5s00YEAoeXjIKCDAn0JDQ8jDQ0YeHjKaNGkixcREN6TneZ6OHz9OgYEB5OPjTWFhgykgwI+8vDzotddeo6qqKpN5VldX0+bNm6hfv74kk7lTYGAAhYQEk6ennDw95TR16lSKj49vdE9lZSVNnTqFAgL86MknF1FAgD/5+nqTn58vSaVu5OEho2nTHqasrKyGe/bu3Uu+vt7k5eVJcrmUPD3l5OfnQ35+PhQWNohSUlKIiOj69etkZ2dBU6dG0CuvvEy+vt7k5uZKvXsHUWpqCmm1Wjp16hRNmjSRPDxk5O3tSaGh/cnHx5tkMnfq3TuIjhz5ifR6fUPeOp2O1q1bRzKZO82YMZ3CwgaRXC6lwMAAksulJJO5k5+fL3300UekUCgavWtWVhYNHTqEQkP707/+9U8KCupF7u6u5OhoS6dOnSSe5ykhIYFmz55F3t5e5OXlQSEhwdSrVwDJZO4UEOBPn376KdXX1zd67qFDh8jV1Ynmzp1LU6ZEkIeHjAID/cnDQ0YymTv5+/vS5s2bSaPRNLpPpVLRd99tp8GDB5JcLiUfHy8KDu5L3t636nX06FF06dIl4nm+4Z7Y2Fjy9/elmTNnGDzvNl999SXJZO60cePGhnsTExPJxkZCEydOoFWrVpKfnw+5ublSYKA/xcXF0qVLl2jQoIHk6Smn/v1DqG/fPuThIaO5c+dQbm6uyb6nUqlo//79NHJkOMnlUvL19aaQkGDy8vIguVxKI0YMp7Nnz9Adr0I8z9Py5cvI01NOS5YspuDgfuTl5UEBAX4kk7mTXC6l4cOH0dWrVxvlxfM8ZWdn0TPPPEN+fj4klbqRp6ec/P1v9VkvLw+aO3cOjRgxnAIDAyg7O7tROX/4YS+Fh48guVxKHh5SCgrqRV5eHiSTudPYsWNo7dq15OrqTMuXL6O6ujoiItLr9XT9+jWaNu1h8vLyJKnUjXx8vBry9/f3pWXLlpK3txfNmDGdysvLG/IsLS2ld9/9N/Xu3euv79KfgoP7NXw7Tz75ZKPvS6vV0ubNm8nPz4f8/f0oLGwweXl5kI+PF61bt460Wm2XjnVMoDP+5zlx4gS5u/ekpUuXGAiKptTV1dHatWtJLpdSRMRk+u677yguLo6Sk5PpwoULtHz5cpJK3WjatGlUXV1NREQVFRU0YEAo9evXh44fP07p6el05cqVv4TWYDp79qxJYf7GG2+QTOZO06Y9TPv376f4+Hi6efMm/f777zR//nySy6U0f/78hjyJiJKSkqhv3z4kl0tp/PhxFBkZSbGxMRQbG0s//LCXhg8fTnK5lNaseZ10Oh0RERUUFFBcXBz961//JKnUjd566y2Kjo6m2NhYSkhIaBB427dHkru7K8lk7jRp0gT6+ustdOnSJfr999+pqqqKDh48SAEB/jRo0EBat24d3bgRRSkpKXTjxg368MMPycvLgwIDAygmJqahvBqNhubMeZTkcin5+/vRq6+uovPnz1N8fBxFRUXRhx9+QIGBAeTu3pP27t3bqI4uX75M3t5eJJdLaejQIfTRRx/RH3/8QefPn6eysjKKioqioUOHkJ+fD61cuZIuXvyTbt68SfHx8bR161YaNGggeXl50KFDBxsJ2c8//4xkMnfy8JDRiy++QOfOnaP4+Di6evUqrVmzhmQyd/L0lNOVK5cb7qmvr6fPP/+M5HIpjRo1krZs2ULR0dGUlJRE165doxUrVpCnpwc98MD4RgJ1z5495O7ek955551GE507hd0rr7xM3t6edP78uYbr+/fvJze3W20xbtxY+vLLL+nixYv0+++/U35+Ps2dO4fkcint2rWTkpOTKTExkZYvX0aBgf4UGRnZYt+rr6+nDRs2kIeHjEaODKetW7dSTEw03bx5k6KiomjNmtcb8s3JyWm4r6qqiiZOnEByuZQGDhxAX3zxBV2/fp3i4uLoxIkTNHPmDJLLpTRz5kyqrKxsuC8jI4MiIiaTVOpGDz44iXbu3EFXr16luLhYOnfuHL344ovk4SEjuVxKY8eOaeiPKpWKPv10HXl4yCgoKJA++OB9unTpEiUkJFBUVBT93/99SgMG9G+YGH711ZcN7Xzp0kUKDu5HMpk7zZ8/nw4ePEg3btyg2NhYOnr0KD3++OMkl0tJLpfS6tWvNdxXUlJCS5YsJrlcSnPmPEpHjhyhuLg4unnzJp09e5Zmz55FMpk7rVy5oqGcSUlJJJO508SJE+j8+XOUnp5OJ0/+SoMHD6LRo0dRXFwcE+gMRnvR6/W0adMmkkrd6LPPPjOZft++fSSVutHcuXMazbxvU15eTk888QTJ5TLat+8H4nmeoqOjycNDRkuWLG40YUhMTKSUlBSjg/edg/jWrV+TXC6lBQsWNNJI7tQSpk6dQq6uznT48OGG67/+emuiMnPmDKMDxZEjP5G/vy8NGjSQioqK7tCUtfTPf/6DpFI3OnbsmMF9Op2O1q5dSzKZOz399NONBvJbmmYM9enTm8LCBtOZM6cbCcjbgvv9998jqdSNVq9e3TDYFRcXN2iQBw4cIJVKZZDv999/Tx4eMho/fhwVFBQ0/BYZuY1kMnd68MFJFB0d3ahOi4qKaPr0aeTj4027d+8itVpt8E6HDx8iLy8PmjDhgQaLiUKhoMWLnyR3d1fauHGjwWSvtraWli9fRjKZO23Zspl4niee5+nEiRPk4+NNERGTKS4u1iAvhUJBS5cuJXf3nrR58+aG6++88w7J5VI6evSo0b5QUlJCERGTqU+foAZLiV6vp//7v09JJnOnJUsWU0ZGRqP6rq6uosBAf+rXr2+jNs7JyaHo6OhmLQG3+95vv51qsDrduHHD6Lu89tqrJJW60fr16xuux8fHU9++fWjo0CF04cIFgz4QFxdHAwcOoMBAf4qKiiIiIqVSSS+//BLJZO60evVrlJeXZ5CfWq2mf/97LUmlbvTmm2+QVqslnufp119/JblcSmFhg+nkyZMGbazX6+nChQvUr18fcnbuQadOnSIiosLCwgYr0rp164xayyoqKmju3Lnk7t6Tdu3a1fC8zz//jKRSN3r55ZeorKzM4L60tDQaM2Y0+fh40aVLF4mI6JdffiGp1I0++OD9RmmjoqIoOzu7xbGgM2CurYz/aTQaDa5duwatVgMfn5a9UsvLyxEZuQ1isRivvLICnp6eBmkcHR0xZsxo6PU6XL58BTzPw83NDebm5rh+/ToSExMbnHWCgoLg7+/fokNMSUkJvvnmGzg5OWPVqlVGt8E4OTnh0UfnQCKRICYmuuF6TEwMBAIBHn/8CfTt29fgvgEDBkIu94BSqURZWWnD9ZqaWsTExAAgo/nV1NTg2rWr4Hkezz//fCMHII1Gg71796KiohzPP/88xowZa7BGKhaLMXPmI6isLMTJkyegVqsBAJmZGaipqUZYWBgiIiJgbm7e6D6hUIjJkyfDy8sbMTE3EBsb2/BbQkICRCIRXnzxJYSEhDSq099+O4XLly/h0UcfxSOPzIJEIjF4p2HDhsPfPxD5+fnIyclpWG+/dOkSevRwwpw5cwz2E1tbW2P06NHgeR75+QXQ6XRQq9XYuvVrCAQCrFy5En379jPIy8rKCrNnz4JIJEJ8fDx0Oh1qamqQnJwMsVjSbByEysoKZGVlQSaTQyaTNZTx4sWL0Om0ePHFl+Dt7d2ovgUCIfz8/FFSUoQLFy5Ar9cDAORyOUJCQlqMiKjRaPDNN9+A4zi88sor6N+/v9F3mThx4l/r+/FQKusAAEVFRaiursIDD9xaG27aB273faVS2RB4JS4uDt9/vwseHp545plnIZVKDfKTSCSwt3cAAISGhkIkEkGpVGLjxg3geR4vvPAixo0bZ9DGAoEAffv2hYODI4RCAXr16gUA+OOP35GUlIixY8dh2bJlsLOzM8jT1tYWYrHoL3+L3gCArKwsREZGwsnJGa+88gp69OhhcJ+vry9GjBgBrVaLxMREEBEcHBzA8zx+//13pKenN6QNDQ2Fh4dHlzvHMac4xv809fX1iI6+AUtLKwQG9moxbVpaGi5e/AO2tvbYt28fjh49YiQVh5SUZAgEAiQl3RLePXr0wBtvvIk1a1bj2WefwcqVKxERMQW2trYmy5eSkoKiokJYWVljx47vDITcbW4PDpWVldDptOB5QlpaOiQSCWQyqdF7BAIOAoEAYrEYFhaWjQR2XFwsvL19jXrr1tTUIDY2Br169WoQLLdRKGpx7NgxCAQCnDt3HqmpaQAMg9IoFHXo0UOK/Pw8qFQq2NraIj+/ABqNBoGBgc2+p6WlJYYPH4HU1BQUFRUBAKqqqpCamgorKyuDSZZWq8Xly5chEomQkZGJd9/9d7NOjqWlJairq0NtbS0AIDs7G0VFBZg5cxYsLMybqUMhAIKdnR2EQiGSk5ORmJgIgHDo0CGcPXvW6H35+fkgArKzs1BdXYW6OiVSUpLh4OBgdKJ4u40rK8sxZcqUBoGlUCgQExONwMAgowLQysoKzz33PJYvX4o1a15HYWEh5syZY9SBsinZ2dmIiooCxwlw7NjPuHDhgtF0hYVF4DgOCQnxqKtTwtLSCteuXf1r0jjAqNPb7X4nkUgaHMESExMgEokxe/ajzXqJ19fXIzk5GRzHoWdPt7/uS0RsbCyGDBmKyZMnNysUq6urkZSUgCFDhjU4qp05cwYcx2HWrNmwsbExel9tbS3S0tIgk3k0OCpevXoVhYX5cHR0wqZNmyESCY2OBdeuXWuYrBARgoODsXjxEkRGbsOzzz6Dl156GePHj4eZmRnzcmcwOkpWVhZKSorRu3cfODg4tJg2MzMTZmYW4HkeBw7sazGtWCyBg4MjOI6DUCjEo4/OgUgkxtq1/8Krr67CyZMn8eqrryEwMLDFrTfZ2dl/aXDV2L+/5TxFIjGEQhE4ToCysmKkpqbA2toGMpnxwbGmphbFxUVwcXGFi4tLo0lETU01pk59yOhAk5KSAqWyDqGhAwwEb1lZOYqKCiAWS3D69KlWlFcIiUQCnudx+fIl8DyPgQMHmahbMQBqGETLysqQnZ0NV9eeBoKgqqqqYbJz5colXL16uYUnc5BIxBAIuIb2Njc3R0hI/0YTnjs9qktKigFwkEqlEAgEqK6uhkJRC57nm5nw3fn+IlhaWkIkEiMvLxd5eTl48MEIWFsbFyxJSTchFIowaNDgBqGVnp6G6upqTJr0oIE3NXBri9WECROwefPXWLv2Hbz//rs4ffo3vPzyywgPH9li3ysqKkJ9veqOdyET/d0BQqEQGo0GyckpsLCwaHZyUlFRgaKiYtjZ2cPBwQFarRZZWdkg4iGTyZoVyrW1tUhMTICFhQV8fX0bnqXTaREQEGBUU75NamoKRCIxBg0aBFtbW9TV1aGwsBDm5uaQSt2bvS8nJwdVVZUYNGgw3NxuTSIKCvIhEolRV6fAnj27Tfbz2+UyNzfHypWrYG/vgA0b1uOpp5ZizpzHsGLFSri7uzOBzmB0hIKCAhAR+vbta3SbUFNtj+M4PPLIbKxevdqo6bbpYHp7e5WFhQUee+wx9O/fH5s3b8Lhw4eRkZGBr7/+Gn5+/kbvJyKo1bfMkXPnPo7XX3/d5PsIhUIIhUKUl5cjIyMdAwcOanZrUmZmJioqyjF27LhGwXSysrIgFkswYMAAo5pydnY2RCIxgoODDQS+Wq0Gx3Hw8/PH119/DUfHHibLbGdnB51Oh5iYGNjbO7Q4sOl0OkRH34BOp4OX160lkry8PJSWFhvdIqTT6aBQKCASibB79x7RMj2ZAAAgAElEQVQEBgaaLI+lpeVfZtIE6PV6eHp6GhV8t7bARUOjUTcsTRAReJ7HkCFD8dVXm0weysFxHGxsbJCamgqOEyAkpL/Re9RqNdLT0yEUiuDhIW8oT25uLgQCAYKCgpoNiCQSiTB58mQEBwdj584d+Prrr/HKK69g/fr1GD58RLNl43k9iAijRo3BJ5980qr+bmNjg8LCQqSnp8HOzh5ubsbbsqSkBEVFhfD29oZUKgXHcTAzk0AgEECpVDabR0VFBWJibmDSpMkN5vHb6VuqayJCTk4OOI6DTCaDUCiEWCyGWCyGVquFRqNp9t68vDzU1dXB09Ozob/rdDoAwGuvrcbcuY+Z3FMvFAobJikODg54+eWXMXJkOD799FP88MNeZGVlYdOmza2ynDCBzmAYQa/XIyYmBjyvh6+vr8kT1pycekCn0+LmzSRYWlo2a6IzZs4VCAQQCATo06cPPvjgQ7i5uePLLzdg586d+Mc//mlUI+E4Dl5et9ZECwryYWVlZXJQvU1iYiLU6noMGDCw2YEuMTEBHCdopPHV19cjISEBOp3WqHalUqkQExMNrVYDDw/D393d3WFuboEbN66CCCatHv8dNHNRUlICb29v9OzZs9lB+fz584iLi8WQIcMQEBAAAEhIiAeARu9x52TB398fmZkZqKysbHV5FAoFrl+/DqFQ2OwkQKVSITo6Cu7u0ob4BRYWFjA3t0BVVRV0Ol2rB+jy8goIhUK4uroaba+KinLEx8fBwsK8YQKo0Whw48attvD29mm2790OjiKTyfDqq6/By8sbb7zxOj7//HMMHhzWbL93dHSERCJBamoKVCoVXF1dW/ku5cjPz0NY2JBmJ5O3td6goAiYmZlBIBDAyckZt5asUqBWq41ah65fvw6JRIJRo0Y1TDatrKz++kYKoFQqjVoqNBoNrl+/jvp6JYKCggDcWo93de0JrVaLmJhYhIUNMRDMer0eFy/+CZ7nERIS0nDd1tYORDxSU9NgZWXVapO5Xq+HUCiESCRCWNgQfPnlV1izZg1OnDiOkyd/xWOPPd6lYx5zimP8Twv0qKjr0Ot5BAX1Npk+NHQA/P0DER8fh2PHjkGr1RrVAhQKRcP/4+Li8OWXG1FSUtKQzsrKChERERCLxUhISGjx4JOAgAC4urri/PlzOHHiF4OY6jzPIyMjw+BEp4yMW9pcSEiIUQGh0WiQnp4OkUgEX1+fhoFMrVYjLS0VSmV1wxplU00xKioKjo494OfnZ/C7lZUVpk+fCWtrO2zfHom6ujqjA35hYWET824xqqqqoFarUVtba1AnPM8jOTkZ//nPR9Dr9Zg79zE4OztDp9MhLS0NIpEYvr6+BgOypaUlBg0aDL1ej02bvmpYd2/6Tv/f3nlHx1Vdi/u700fSqGtmNCpWteUqG4MLmGLAlICBAAmmOLz3CxB4TkKABMgjgVASQhJqEpKQAAYC5NEMxhhsggFj44JxxUWWbFnF6l1TNeX8/pA8nhmNpJFkmZLzrWXW4mpuOefuu/c5++y9T2VlZdg929ra+OKLnUyePDWs8E4ohw5VcvhwLdOnzwgakaKiIoqKiigr28fy5cuDM7lIV3ZbW1vYseTkZAIBP9u3b+83W/T5fGzYsJEDB8qZPHlKsBCR1+tl69bPSU5OCSuuckT2amtrWbp0aVjwlVqtZt68U8jMtNHQ0NjvOUIpLh7PxImTqK2t4a233ooq7w0NDTQ3N/cbKLpcLkpLpw/oOj8S0Dhz5szgOzvttNNIT8/gueeeZfny5djtdrxeL16vF7vdzooVK3j00UdQFBVW69GiPIWFhZhMibz//mo2bdrYT3b8fj979uzhk08+ITc3n3Hj8oJ/u+SSSzAa43j66adZu3Ytbrc7OGNvbW3l6aef5rXXXiM+PoHi4vHB8+bOnYtWq2P58rf4/PPPo8prZWVl8LvsHRhs4E9/+mPQo6AoCmlpaVxxxRUEAoHgOvtYov7Vr371K6n6Jd9EmpubeeKJxxGi16DX19ezf//+fv8qKyuxWq2kpKRgMBh4//3VrF+/Ho1Gg9VqRQhBe3s7q1at4qc/vZWGhkbmzJmDoij85S9/6fuIXZSUlKAoCnZ7NytXvsvatR9z8cWXMG/evAFddiaTCY1Gy4cfrmHdunXExcWTkpIMKLS2trJixdvcdtutOBxOZs6ciUajoauri6VLn6WmppobbrghqmFubm7iqaeewu128z//syTovuzp6WHZsmW0tbWTk5NLTk4OXq8XlUqFRqOhoqKcJ554jFmzZnP55d/p55LXaDRkZGTw7rsr2bRpIx0dHVgsFnQ6HXa7nc8++4z777+fd99dybx584JejhUr3mbt2o9pbW1h48aNpKSkBF3xTU1NrFy5kjvu+BlVVYdYtOhKrr/+BvR6PS0tLfzjH3/H4XCyZMmSqFHKNpuNLVs+Z/v2bZSVlWGxWDGZTLjdbsrLy/nLX/7Cvffew+zZs4Pu/m3btvHqq69y1llnc/7534pawWvDho2sXr2KSy+9jNNOO63PbawnNTWFlStXsnHjRnw+LxaLBZVKRUdHO2vXfsIdd9zOvn1lzJ07Nziz8/v9vPnmm+zZs4eMjAxsNhter5eamhpeeulFHnjgPoQQLFp0FXPnzkWlUlFVVcUTTzzOpEmTueKKRf1mpsuXL+fuu39BRUUFU6dORa/X43K5WLNmDStXvsO0adP47nevGNCDo9FoSE1NY+XKd9i0aSM+n5+0tDQ0Gg12u51169bxs5/9lN27dzN79uzg/V9//XV27tzBdddd32+gcUTGXnzxn9TUVHPdddcHg/nS0tKIi4sLDl7Xrv2YgwcrWbv2Yx555BGeeeYfOBwOfD4fixcvJi8vL+iFAVi/fh2bNm0mOTmF1NRU/H4/9fX1LFu2jDvvvJ329nZmzpzJpZdeGpTbnJwcGhsbWL/+E95+ezmbN29m7959rF69igcffJAVK5bj9XpJTk7myiuvDK6FJycn43K52bRpAx999BFJSckkJycjhKClpYVXX32FW265GZMpkcmTJ+P1evnFL+7iX/96ORi8KYSgra2NpUuXsm/fXhYvXhw1K+KYIjOVJd9UNm3aKAoK8oPFJrKyMqP+u+qqq4I5yE6nUzzyyCMiJydLZGVlivT0ZFFcXCiyszOFzWYRJSXjxQMP3C+6u7uEEEJUVlaKSy65WNhsVlFQkCcWLrxQzJx5gsjMtIhFi64Iy6UeCLvdLv7whz8IiyVdZGVlitTURDF58kRhs1mEzWYVEyaMF48++kiwqExNTY2YMWO6OPHEE8KKdoSye/dukZeXKy66aGFYdbSenh7xxz8+IdLTk0VWVqZITo4XBQV5Yt++vUIIIVasWCFsNqt44IEHgsVoouWpr169Wpx44kyRlZUpzOY0UVRUIAoK8kRmpllYrRliyZIl4sCBA8F85yVL/kfk548T119/XV+VN6swm9PE1KmTRVaWta+dxeKhh34rWluP5vyWlZWJoqKCfu2I5LPPPhPnnLNA2GxWYbWaxbhxOWLSpBJhtWYIq9UsLrpoodi8eXMwl3zp0qV9eeJ/jXo9r9crHnjgfmGxZIg33ni939+WLl0qiot7q6KlpSWFvC+LyMnJErfffrtoa2sLy69+8MEHgzKXmWkREydOEOnpyeKUU04Wp512qsjOtonly5cHz1mz5oN+hYEi86d/8IMbhMWSIQoLC8S5554jzj77LJGdnSlOO+3UqHnl/d+lVzz//POipGSCyM7OFBkZKaKwME+MG5ctMjMtIjc3W/zv//5cNDU1BgvKLFq0SBQW5ocVDQrl8OHDYv78M8QJJ8zoJ/9ut1u8/PLL4sILLxAmk0YkJRlFamqiuOCCb4lnnnlaXHHFFcJqNQfzyENrMdx0043CbE4TWVmZwmrNEMXFhcJqzRCFhfli/vwzRHa2Tdx33339nqe9vV089tijYs6cWcJgQCQm6oXVmiEWL75GPPvss2L27FmitHSq2Lt3T7/z7rrrLmGzWfp0QUrfPc3CZrOKqVOniL///amgXO7atUvMn3+GsNksYtq0KeLiiy8SU6dOFhZLurj55h+HFYUaKxQh5EbIkm8m69Z9wqpVq4b8XXHxeL73ve+FzTB27drJO++sZNeuXTgcdtLTM5gxYwbz589n0qRJYWvdjY0NLFv2Jp99tpnGxkZyc3OZOnUql112GWZzbOuSHo+HTZs28emn69m5cyddXV1YLFamTJnCggULKCkpCc4i9+3bxz//+QJms5kbb7wp6rr7+vXreffdlRQXF3PNNYvDZmkOh4M1a9awd+9euru7MRj0/PSnP0Or1bJixdts3ryZ008/gwULFgwaN1BeXs4HH3zA1q2fU1dXh8lkYtKkyZx88snMnTs36Dru6OjgyisX0djYwEsv/Qu3283q1b3uU4/Hg9lsYcqUyZx++hmUlpaGzZY3b97E8uXLo7Yjmqv7/fffZ8uWLRw4UIFOp6O4uJhZs2Zz6qmnBiP9fT4fzz23lMrKSi666GJmzZrV71put5snn/wzbW1t/Nd//Xe/5Qe/38+uXbv6atFvo7m5GYvFQmlpKXPnnsyMGTP6zagdDgerV6/mo48+orx8P3l5+UyZMpkFC85h3brevOVrr72W4uLxCCFYvXoVn3yyjtmzZ7Nw4cKobW5vb+f999/nk0/WUlVVRWpqKtOnT2fhwoUUFhbFHAPS25bVbNu2nfb2NjIyzEyePIl5805lxowZwYC8xsZGnnqqd7vdW265NWpqZnV1Nf/4x99JTEziJz/5SVTvR0tLC21tbXR0dJCcnExqagpqtYZFi65g69YtvPvuKk488aSwczo7O1mz5gM+/PDDvowUPVOmTOGcc86ltbWVjz/+iPPOO5+zzz47anzGkaUQt9tNUlISGRkZNDc3s2jRFWg0Gv71r38FAzFD4yjWrl3L2rUfU1ZWht3eTVFRMVOmTOWMM86gqKgo2D4hBFVVVbz99tts3LiBjo4OiouL++rqnx1zfMdokAZd8o0lEPATCAwt3qHR6pFrmw6Hg0AggEajISEhYdAdnBwOB16vF71ej8FgGFERiUAggN1ux+/3o9VqgwFBkb85Egw1kIGL5TdH2njE/XrEUAkhgkF+seBwOOjp6UGtVhEXF99Pge/evZvvfvc75Obm8vrrbxAXF4cQgq6uLgKBwIDtHE47Io2xy+VCURTi4uKiDniOtFOtVg/4To/0zWC/EULQ3d2Nz+dDq9UOKiNHn8+Fy+VGr9cHjX60ew3nXbhcLjweDxqNmvj4hBHttBYICOz2bvx+PxqNhvj4+H73FUIEi9cMtNGIEAH8/gCKAmp17HHXW7du5bLLvk1qahrvvbdqwIC73ra6URQV8fG98ubz+YLPHaucALz11pvcfPOPOfHEk3jhhX8OmEng9XpxOp34/X6MRuOgWzAfkYkjvx2o5sJYIKPcJd9YVCo1oynMpNFooq7ZDjQoiHVXtcGfWTVkQZpYFHysBjlSKQ9HGR4hPj6+X5W1UOrr6+ns7OCEEy4JKkJFUWLq2+EMLI5gMBiGVKKxtDOWnbEURYmpgFD48/VGyg91r+G8i6GMTGyyN3RbFEUZsl8URYVGo+o3OFqx4m3Gjx9PScnEsHcaCATYt28fDz30W7xeL1deedWgs9lobdVoNP2ey+Vy8corr7BgwYJ+O9/19PTw2Wef8cgjvUF4V1yxaND+02q1w9IFw5UJadAlEsnXgq1bt6JSqZg9e86I9+iWfL3ZsuUz7rjjdpKTU5gzZw4nnTSLrKwsmpub2bZtG//+9/s0NjYwf/6ZLF68+JhsM/r228u59957eOGF55k9ezYzZ55IcnIyhw/XsmPHDt577z26u7u48sqruPDCC78R/Sxd7hKJZMzw+bzcdNNNfPTRh7z44stR16sl33x6C6v8lQ8++DdNTY1h6VuKopCRYWbhwoX84Ac3DlinYLhs376dJ598kg0bPqWzs6Of98NqzeTaa6/l6quvibnmhDToEonkPxaPx8POnTvx+31MnDgpZrel5JtHbx2HKqqqqqioqKCrq4vExCRycnIoLCxg3Li8ES35DIbf72ffvn3U1dVRWXkQp9NJamoa+fn55OfnYbNljfmGKdKgSyQSiUQiGRayUpxEIpFIJNKgSyQSiUQikQZdIpFIJBKJNOgSiUQikUikQZdIJBKJRBp0iUQikUgk0qBLJBKJRCKRBl0ikUgkEok06BKJRCKRSIMukUgkEolEGnSJRCKRSCTSoEskEolEIpEGXSKRSCQSadAlEolEIpFIgy6RSCQSiUQadIlEIpFIJNKgSyQSiUQiDbpEIpFIJBJp0CUSiUQikUiDLpFIJBKJRBp0iUQikUikQZdIJBKJRCINukQikUgkEmnQJRKJRCKRxILmy7hpm9dNvdtOmauT9h4XakVFgkZHviGBHEMiaTojakUZ8PweEeCgowN/wD/iZzCqteTHJxN6l2aPk6YeB4ijxyxGE+laQ/j9AwEqHO0IEeg9oECy1kiWIWHI+3oCfiocbWH3MGn15BgTw57FKwIcOAZtLIhPHvX7qnJ2Yvf1jPh8tUpFQVwKOpUKd8BPpaOdgBCjeyhFwWYwkaLVD/vUCkc7Hr8v7FiKzogthvd39N204w8Ewo5nGk2kRsjKUKzvqKelxxX8/3i1ltNSbOhU6pivYfd7aXLbEQzepwoKBkWFolKhV2tJ0RpQhrh2t6+HGlcX4sj7UsCsTyBDZxzyubp8PdQ4O8OOZejjMevjwo45/F6qnJ1H7zECUnVGMmN8f6G0e93UubpHJYpDvfcuXw/17m72Ojtp97oAhQSNFqsujmJjEsk6I4ZhvO8j19vlaKfb50EA8Wod4/Tx5Mclkaw1oB/G9SJlu9njpMzRTr3XSXePGxAYNXqy9fFMjE8lZRjPKxAccHbi8XmDxwxqDflxyagUZcjzy+xt+EJ0oFalpiA+BU3IuQI45OzEGaKjdGoNBXHJg9qRI5Q72ukJ0Qeh+ir0HrWubrq87pHPnhWF/PiUYb3rr7RB7/B5WNVUydKWA7zncfR1kxLSZTBZY+DyhHS+lVFAaaI5qmB2+Xq48cCnfOxxjPhZfmay8OuS09AqR1/axvZaLqreFva75fmzWJiRH6HkPEwuXwshAnS2IZG/j59HnsE05GBmyr4PIUR5/S51HLcWzg4TPoffy7UHN7DZbR9xG28zZfCHiWeO+r39q3YXd3YcHvH5J+niWDHpbMw6Iy1eN1eXf8K2UQwQjhj0jwtP5rTU7GGf+vChLfzV0RZ27Lvxqfx5/Kn9Bm/R+LjtMN+u3IQ9bLAlWJ0/hwUZeTE/R1OPizsqN7PeF6IkFBV7dWdSYkqLfcDl7uaEPR/QIwIx9Bug0rBAa+A0YwoXmAuZZEof0AAccnUxrewjCGnrdYkWfls4l7QhBlPVjnamln0Y8o3DmznTuThzQvjv3HbO2v8xDRGDrOHwkqWEK8eVDvu8HV3NzD+wPuwZh4fg3wUnc1Z6br+/OAM+3m06yKvNlfyfuzOqvrOptVwel8q5abmckpJNkkY34J2cAR//bj7ES80H+D9X5PV6rzlOrePbcSlclJ7HzGQbiYNcL3KStLOriTcaK1juaGG3zxOlTwRZah2XGlO41FzASck24tWDmxCfENxTtZWXupuDx2xqLf8qmMO8FNugvS6E4NaDG1npPjrgutSYxNMTzyQ5pF1+EeCx2p080VEXPJaoUvNG/mzOTMsZ/B4I7jy0hTdC9EGovgp9ltcayri1+cCIZXSqWsuKKeeSq48fMxt73FzujR4nP9u/nkWHd7IqaIiVCE2jsNvn4d6Ow8yuWMed+9dx0NkRdd6hQQFl5P9USnRdp4743UDCkBnxu7Webh6v3o7d7x2yLywRz64MMIrUjbaNI1ZS/ftlNM+hVY5tu1AUElFG3DpVlPu/4mxnWWMF/iFmiY09Lp6s34NdBPpdQxnmA33aVstWX09Q9kEBIXi7qYIAYlhvyBpr36FAwM/7Hge/7KjlhPK1/PrAJtoGmXlYIq7xj+5mXqzfizeWAUSkrEd5a4oCCaOUCWWEwqAoo5fFaPe2+708cnALl9fs4BV354D6rs7v44nuJi44tIUb9q5hU3tdVO9Vt9/LHyo/5+KabX3GnCgGV6HK7+Wx7ibOrNzMkn0fsamjnqHe0mG3nfvKNzCvfB0PdtaxOyiT/d/lYb+XP9qbmH9wE7eWfcyhCA9MVA9dxLutC/h4oGYH1e7uGM4N1z3qAZ4sUm93iQC/qt1BubNj2PfQKmOjB3WKMuZ29rgYdJffxy8PbuQfjpaQselQA1/BY92NPFi1FV8gwFedHuC5rgaer9+Hb7TuZMmXwh+bKyhztA8623i1YT/LXJ2j/yYCPl5pOYgrytfwUlcDDW7H8Wm0ENzfUcsvDmykO4bBaK9iEzzUfIBVLdXDHHj8Z+AXgmdqdvHL9uq+OWBsvOLu4mfVW2mJGFx5RYB/1OzinrYqlGHoln+6OvhV9TZaQ5Z0Itlnb+XG/Z/w687DeMRw9KzgKUcr3y/7mE/b64bdR6s93fyhajudo/XUDcK6Hie/q9pGm9fzHyN7x8XlvqWznlfsrWHHUlCYqDMyXp+ARwSodHdT5e+hPkRg81QafpQ9Fa1q6HGHCchV1DHP2kJd7ceKdiF4vLGciXEpzB+BK7j/JxNOPJD3JbcRwACMU1RoY3ySBEUdZg40itLr4Yg0mEBzhMJKRCFeiT7qP9bj3V2+Hv5yeDe/LZob1ZW4x97Kn1oOHpN77ehsYrW7K+rftvs8rG2rYZFt4oivn4FCiqKEyVAAcCFoFYJIFfdidxPnNFVySeb4mOSyLuDn14d3UhSXREl8yjGXsQwULMOQX/UxnP3kKwrxMc51vIh+krjP2c4TrZURcgwTtQZKDIkIBBVuOzU+D/UiwJGFhnhF4ee2yf3iE6qdXfym9SBKiE6IB6ZqjRTpE9ApCgc8Diq9LupFAG/Qi6hiibVkwHiHSlcXPzqwkX/3OPrNRFMVhSxFTZrWQADo9Lmp8vtpj9BKa3xueg59xpOaU5hqSh9WP7/Q3cDEhjJuzJoS03r6SPg/ewsT6vbwk9zSMdGH4xVVr8cxBhIVFWM9Rx9zg+4XgnfbaukKEwSF32dO5LuZEzD2rd35RIBd3a280VjOsq4GykSAe6wTmJyQHlWh+CIE6xR9PC9MPCvmDtMoqjF5wfsDPu6v2UGWIYHxcaMISBP92zhdH8/rJfPRxPjcY2XQC1VqXi6Zj00XF5sbSFFIUveueaVp9fyx6JSobsVDjjYur90e5lT7QUo2V5iLovq/hopXGAl/627grNZqLjbnhynqbr+XPx/eTVmMs9ihvom3WippHWS29XxLJReYCzHFuAYayZJEK/89bkaYBPlEgE6fh71dTfypqYKNvqNmvQt4sbmC8y2FMQdUbfS6ub9qK48Wn4xZaxy5qAuIDP1cnJTJXfknxTy/PZaBRo/ZpnLKMGIh4tXaMN30RWcjNRHBrHek5bEkd3pwoOgXgjJnOx+2VLO0rYrtfi+3JGczPy23nw7b2FFHS4iX0gjck57PDTmlwesFhKDc2cGqlkO83FbNLr+X7yfbOHeAdjj8Xu6t3MxHEcY8BYXz4pK5MXMi0xIzetsmeoN5N3fW81JjBW84Wgj1Y63zebiv8jP+OnE+acMICu0Ugocb91NkTGbBEGvdI8WO4I/NByiJS+bCjLwRTwNEn4cu8ujL409jnDExRs+WMmiMxNfCoAsEO93dYZ9lqdbAxZZCTCEfgkZRcVKShZmJZq60t7C6tZpLLcVRR94KfWvoocYLhTSNfsD16OPJhz0OnqjezgNFc0nW6Ed2EYV+M2ANKlI1hpg8FmOJGkjR6If18QaVkUrNjAFG8vFRXH65ujhmJmYct7Z5heCR+j2ckGgmty9qOoDgvZZDPNXdeEzucdDZwf91Hb2WHphvMPFeyJriux4727qaRhT0B5Cq1gafP5ITTBlMNZm5qvwTdgeOBqJt97qpdHUNa8b9qqOVaYf38JPc6SOOrFaUvnXMCDlJ1eq/FPlOVGtGJNtHNN4OVydhjmRFxTXWCWHKXKPAtIR0piakc7G5kFcaK7g6c0LUgUlnRPBvhqJwiWV8uHFQYHJCGpMS0rjUXMRz9Xu5LnvKgIP6T9pqecfZHvQOKIAJhfstxfx31hTiQj1UCmhVKs5My2VOso0ZNTu5r/kATSFa/TVPN5c1Vw7bq1QZ8PNg7Q4KjYkUxiWNyfusFQF+fXgXecZEpiakjVQdh0XWHyFZoxuFrBx7jotlcIvw0epOfw9Ob8+As7lppgx+mjcz5gjNryJ/7m5mad1eer4G6/+SCGXX4+Sl+n3Bd1fl6ubh+n3HaIALq1qrqAwxpFPVWn6bdxKlEYO/V5sq6BlF2uJgTEvM4Fvx4cqtQvhxD3O90Qv8vqWSt5sr5Xp63/v1RL4zEaC5xzmgociLS+L2/JlR014DCJyB8Oh/p4A6j33Q691TOIcsffQBnSvg5+mGMlpCZpwCuCU1h+9nTw035hHEqTVcl1vKjSlZ/b1KzQdpHWZalwA+6nHy2+qtwz53OPfY5HXzUPU2mgaJJ/gmMOYGXUEhN8IdJwJ+flm5mY3th49ZwEIwAvFLwhYxslYQPNRUwcdtNcdUzX0FHBDfAK0bPsgap6goVevCPoY/tVWxpasBrwjwUv0+NnnDFcG5UZSlLoYljpYeF8vba8N8L+clWpmSkMb1qeGpTy/am6mIIUp3pMRFzixE8D/hRiXC1Rgp660iwG/qvuCL7tYIY8SIs8G+XDFXRnGmQl7EUpQK+MWhLaxoPECjx8lwhvgqFBIjBnotCH5VtZW3Gito8jiHzMyIZL+9jU0hAwIFOFGj579sk2JautCr1NyUU8psdfiE690ee7++FXMAABcqSURBVL+6A9HIUFSoI9aTX+hu4YX6fUNmTgQzVIbAgEJ8xPf4oqONpw5/gfsYDpIVvloKecxd7ipF4VyThRe7m8LcUM8723m+Yh3n6U3Mj09joimdUlMGmYaEIdd+BaLf+nKd38urDeUxGbx5yZlk6hOOaTtPiUtmoi6Bh9qq8PSpxSYR4N6a7RTEJVE4zPV0IXrdv6E0+3t4rbEipgCgk5OsZI3BGjP0rn2903KI1Bg8KHnGJE5KNH8F51FHmajRc7V1Aotrd6CI3lXbwwE/T9TuYrHXw8NtVWG/X2IyMy/RwqrDu8KOxxKuuK6tlvdDZms2ReHijALUisK56XkkNR+gs0+ptQvBe82VlMSnHvOgIXfAT21EtH6WSoUmZBmsVw4FkdnhVyRn4exx8jd7S/DYNl8Pv636nCcmnB7M5ffFGDUdbQ19j7ub1xrLhxwMKyicl5ZLwjH05n3U1UhzDGbXqNJwdlpumBFUgBmmDNKaKmjte/oAsNrr4r3qLZxYb2RhfDpTEjM4wZRBuj6BhCFyuScmWqC5PGyg8ZHXxUfVW5lRt5sL4lIoTbIyM9GMWZ8wZG74ju5makLejQDON2XEvBYMYNHHc1myjU2th8LexsbOeqYnWQY9N1tr4O7UXO5tPOol6EHw+6YKJselcFZ67oAptxoltlmoW6XiWWsJd9Xvpa6vrQrwcOshpsSlstBcMKxASkF/fQwKK1urMHcPHT+SYzAxO8n69Q+KU4CzMvKY21zOWq874gNVeM9j5z2PHdoOUajWcaExiTNTx3Fyio30AaIzFZR+a+hbfB6uqNka05v5XHfqMTfoCYqaH2RPocxj5xVH76ccANb7PNx/6HP+UHxKTEVLQmfi2giB2+PzcGXNtph6/bPCk/sZ9EpnJ+4hineoVArF8SmD5rBXiQA31u0mlgTE35uLx8SgCwQVjo4hUxo1KhXFkWvCEQNGBYX5qdnc5Wjj123VweNvujpZW72V9r4PWQEma3T8IGsKdVFmIkOl/dj9Xt5sDR8cnGJIYkpfTEGOMZEbTGZ+39UQ/Ptz7TVcYZtI1jEsRuEK+Hm5bm9fsZOjTNboyYyQGeVI/mzIqx6nNbLAOp5dFZ/yacjg5E1XJ0XV2/l5/okYVZpej4UYesIbbQ39DVcHb1RvjUnWG5Osx9Sg39tRCx01Q/7uQl08J6dk9ZvVnpBk5bL4VJ5ytIZ7K1DY4nWzpe/66SoNF+hNLEjJZn5aDrYBBuAzksxcH5/B3x0t/f62zedhW1cDdNVjVmk4X2/irJQszk7PI3MAmamM4q4vTcgYlrFRgJMSzRhbDxHqu7LH4NJ2iwCXmwtp8br5feshnH2apE4EuLt2J1lGE5PiU6Oe2yNEzFUmv5WeR4ffyz2N5XT1hVd2CMG9dV+QazRxwjD0kkJ/fawCftiwLyY9eG9qHrMSLWMe43Vc0tbSdEYeK5jDzQc3stHrpmeALjvg9/K4vYXH7S2c27CX/82eximpOcMYScXwO2Xs1vnMOiN3586govwTPg+JIH7O0cr4w7u5LXf6l+oO/F3NDv7Z3TTob0rUOj4tvQBVTAFOSgy/GBsB9gvBbVWf8+EQLulzdXG8PPW8CK9P/2cyqNRcZ5vMp/ZWPuyL/PUA9SHuOYHCTy0TmGxK43AUgz7UGvLu7haedx6tSBUPLM4oCAaT6VVqFmbk8/uu+uAzlvu9rG+t5ju2icPqyR3uLpY17A8tSEgAQXWPk+32Ft52deAM+aMBuCY9f8jqb0d6YlJ8KndnTeX6qi3BiG4X8Nf2Worjkrk6ohrc8Zb143Fv1QDSHafWcE/BbFwVn/KqqwP3ANdvCfh5ztXBc64OTm7az83WiXzbWtzPQ5mg1nJvwSxcBzbwqrMdzwDXawq53pnNB/ihtYTzzYX9Bhz9iwgJsnTDz1LQa/TEKwquEDmq83rwBALohwjcNag03Jg9hT2uTl53tgf9IRu9Lh6s2sYfik7GrBtdsJlWUfE920R2OTt4vrsp6Gna7vPwYPU2Hiueh00fN+LrB4ajB4/TWqnqeH0apYlmXpl4No9bJjBLoyNpiE5Y1ePkvys383bTga9VsE1JQgr3ZE/rl0P7p5ZKVrYc+lJbEhAB7EP88wv/16avY2mPCMQuPXlGE7fZJmEeYMnnelMGl5gLR1SBr0cEeKc5PDd5gkbPnJSssKtNMZm5RH/U9ekCnms+iDswvLKoT7s6uLRmO5fVHv33ndod3NZUzgvOdjqECCokDfAtQyLnZRQM6x7z03K4NaMQU0gLmkWA39fvZUtnI//J2AwJPFlyBi9nl3K21kjGEAp9g6+Hmw7v5M9V26KuI2caEnhywum8kDWN07RG0pXBh8prvG6ur93BU9Xb+5UE1kYZrI9kb4WAEP2WSowqVdQqnNEGS1ZdHHePO4ETImIE/ulo5ZnDu/Ecg4DiVI2eu8bNYJ4uLqy/XnN18mTtLlx+/zdK7o5bLXcFsOjjuCF3GlfaStjZ2ci6zkbWdNVT7uuhKooQHxQB7qrdycxECzlGU8h4sn+OtkVRmKkb2i3pR2BQj12zVSiclz6O/3V2cHNTefB4gwhwX+1OHlJrY6yU11u0Ikw4FRWzdcaYZr1a1di10URvvWNDDEFgpq9ZpsKZaTl8v7OBB9vDXa6lah03ZU0ecR5pjauLv3XVhx0r1sWxvbspYu1dIVWrI3Qa9pnXyca2OuZHqRc+WuJR+JYxkYeLTo5agESIAO4BlL1OUfH9rMmUOzv4a3dTcICwy+/loaptXG0uimmSHW0NvUilYYLWMOS3ElAUVMe43sIcjY5U9dDvOVNrHHRGlKDRcXHmBBaYC/iiq5l1HfWs625kn9fFvojALAG0CcEDzQc5JdnGScmZUb+ly20lLDDns7e7hU2djazpaqDM62J/lECvViH4bUslc5JtzAq5Xv90QIUdznZOTssZVj81ubtxRMhGukY/ZAxUvKIKxjpNSkjl/uxpXFu1haaQa/2+pZKpcSlEVn0wKKphv+98YxK/yT2BKw98GmZn/txWw7S4FDwxDGai56HDfK0xWEtlMNI0huPicDruu62pUEjS6Dk1LZd5abn8yO/loLOTnZ0NvNFWwzJPd1hFpP0BHxvbasjOmhTsj2h56LN08Sybel5Mxm6s3R9aRcX3siaxzdnBUntzUCB2+r38rHobnbGY9Ch56FN1cSybcm5MBWOitTGWgUQs49V8lZpnJ5xBdgzrumPZ1bFMKsQwfSJGlYbrsiazwdHKR33rwyYUlliKKR1mJayjrjnBhy3VNEUo3WXOdt45uKnf7yOVWLMQvN58gNPSco5pRbQZah0/sYznIkvhgPUSFEVBO9iATa3lttwZ7C1fF1yqAHjD082eut0xuiP7r6F/N8nKA0Unx7QD27EOGPx15hTOsBTGps+GuLdCb+GZ2Sk2ZqXYuKWvAMzuriaWt1XztquDdnFUStsQvNtSxQlJ1gFrcCRr9MxNyWJOShZLRIAKZyf7u5t5o7WKFRHXqxcBVjQfDDPo04z9A3R3dTXhCfhjriXgF4J3O+v7LZ+ajUPnkvcQCH67KhTOSh/HrY527myuCP6mHcGPD++gK+L9+xDD/qYVYFZyJndbJ/Lj+j04+s7vRHBL3Rcxedyi56EL/l58Kvkx5M8ryvGJiP9Stk8N7aQEtZZppnSmmdK52Dqe02p2cktIeU0fsNnVwWVCDGmIVShficIy9H10d46bQVX5Oj7sMwwBYKfPM6oeU6GMWIHdapvMEn/xEH3Ym1ISiyJTfYl9rVYUfpcznd8EBq/cplHUUQtCDEaBMZGfZE6irHor9SLApQlpfMdSPOLNbjq8Hp5uOxRFsUGslaxXOtu40d7CFFNsRXZOV+uYbjAFVV+dz8Nyjz3sfvlaAwvNBUMUP1J6DcsgOrQgLom7c0qprdxMecjSwD5/zygkvVf9fRnfs0phTGT7SHsmxKcwIT6FCyxFnFVfxvfqvggbdK9zdxAQYsjBm9I3eZgYn8LE+BTOMRfwSl0ZP2vYE1ZCudrdjSvgD84kJyVmMF2lYXvIu/qLs43vdTQwJzUrprbs7G7mY0d4mqJeUTE3yTrkuZHR4lpFxfezJlHm6uDZkMyJyigBvD4hRrTNrlpRuMxazA5nO090Hq09Xz/KFDZljGTlK2/Qyx3t7LW3cn5GwYCVzkwaHd+1TuCW1sqw6Zcj8PVc5xgfl8wd2dM4dGgLhwK+Lz0SoOQ4Vlwb+8GgwuSksUuHOyd9HNd0NfBeVyM/zpoStl3jsLwIwKftdewcZdGMyoCfVS1VTDbFFo18eaKVHxbNCf5/a48L774PWe6xB+XwDXcXC+rLuD5n2qhn/vNSsrjVOZ5bG/aGBUn9p9LocbKmrZqFGYUkaKL7OPQqNQvNhcxqOcjmkGwBh9/Xz59W53Gwoe0wZ2fkDbjsY1Rp+HbmeN5oqWS59+j13EL0pRD2GvQ8QyJnJqSxPaRaoRq4r2YbfzOayBkifa25x8XD1dv7uflvSEgn0ziy7KF0rYE7cmewv2I96wcowjNakjQ6fppbSkV5d9iWrN8kjktQXIPHwc8PbuLi6m08cHATe+2tAxZDqPc4+vlSC7VxQ6e+AF+xHP/elL3UbG43F4cFDo12RCgZJSKWHGM1P8yexq9sUyiNcVasV/q7Kz0BP882VRCqovJUar5lSOTCIf7lhcRBKMCyjsPUu+0xDyRCSdMZuSOnFBEhQHc3lbOls2H0MwNF4ZrMCSxJsn0jhoujocvXw32HPuOq2p3cXPYx2zsbBtyBscvXQ0dEwKNZYwj70O1+L48c+pzLa7dxS9ladnQ2DZjj3+BxcDjCa2XWaEkIqS+gUhSuzZxEQYgnzg+82+PkR+Xr+LyjIWogsl8I9jnauLtiAy+6wrNL0hQVi60lGEcRuzM+PplfZk0lS6UeM1WebTBxZ04p49XaY3KP/7jCMh0+D7879Dmvu7tQgPvaa/hnZz2Xm8ycnZJFtjERrUqDXwQ45Ojg4fo9YecnANNMaWHuzmiFZer9XpY1VMTcvXEaHaenZh/TTR2iKzoVV2ZOYJernb91NhCrryFaYZkWfw9vNlTEPJuKU2s5PS3nmLexSwjeaz5EWoyzVpWiYn5azlenlG+M/ZdrSCDLUhRzf0eLTt7Z3cyGiHzvW9LyuTZ7ypDXe7OhnP+q3x000Ot9Hta0VnNN1qQRNXtWso0n0gv5cchaZbMIcF/VVp6PO5M0nXFU3Zqg1vLDnGl84e7mvQF2khtI1iO/izJ3N282VsTsXjXr45mbnHlM3J+fdDXSPoytRPPjU5jeF1/hFQGW1n7BM32z32ecbbxdvo6r49M4IyWb8fEpaNVaBIImt52n6vexP2QLUTVwnik9KHM9gQDP1H7Bw10NgMKzzjbeKV/L1aYMTk3KpCQ+DZ1GQ0AI6l3d/KOhjM8jNhDKi0/rpxenmNJZkp7PL5oPhOWRv+Wxs6liHf8v0co5qTmk64woikJHj5t32mp4q7uR3VE2KLorLY/SUXrMFBTOTMvhTlcnP2oogzHwaSrAKUmZ/DJzIj86vIuOGOVroMIyq1qrsHQZY1Q7CnNSbFh1cWOm2sbUoAtgeeMB/tZXKONIdxwM+PhdZx2/66wjRaXGrKhwiAC1UVzrM3RxTDWZ+734yKC4z3weLoup6Eov5+lNzEq2jrlBP+LquT13BhX71/JvjyOm0o/RCsvs9nn4Tu32mO97oT6Bk1Iyj3kbD4kA1/cZmtgkWUV1YsZXqDZ/7Ep/OK7oyFlNAMHbzZU0hCiCOJWac9LzYoqWPyt9HLOb9rMpRIG+21bDRZaiEfWlWlG42jaJT7oaedVz1OX4YY+DZ2p3cVv+SaM2iLkGE/flTufggQ3sj3V/9ShBca+5OngtpsIyvdyWZGNWspVjsUHl3R210FEbs5Z70jopaNA/ba/jty0Hw3LPm0WAx+zNPGZvJk5Rka1S40Nw0O/vZ7RsKjXzUnOC7djQWc89zRVhgcJNIsCjXY082tVIsqLCqlLjRXAgyprzVJWGC9JyowyyFW7MmUa9x8HjXQ1hgZgNIsBvOuv4TWdd3+BXGdCrpaV3Z7/rckpjKn08FFpFxTWZE9jrbOfJrrFJfVQpCpeYC/nC0c5D7TUxa4xohWVuaoh9j4ckRcUHRtOYGnTVWKvNS63FPGQuIneAl90e8FPm90Y15jmKivuyp2HRH/sOON6u63EGE3flzqBAffziEL/cPdnCO/s/caXgoLOLtzrrw1T2TQkZFMRYBjjLkMD8xPBykS+5O9nR1TTiZ0rVGbgtp5SkkO/RBTzeVs2HrdXH5Js/IdHCLzInkXAcP7IvT77C7zw3OZMnsqYxWaWJ+v05RYD9fi8Ho6yTp6LwG+tESkKqpM1OsvBM1jQKB3Bld4gA+/zeqMY8TVH4iWU84wfYYSxOreWXhXO4O3UcSYO5TwYw5umKwi9Tc7mrYBYmjfaY9WiyRs9tuTNYcIyreUZ6k27OKeUyY/KI9eRws+T1x0FOx1znJ6i13JQ7gzeK5rEk0UJhDKM4LTBHo+eZcSdyamrON8YYnJKUyS+tE0mRC+H/Efy75RD7ItZHz08bhy7G7W8V4NvpeVgj5GVZU8WoNpg4MTmT+9PDi8gcFgF+V7uTeo991O1WKwqXWwq5LSXnP+6d61RqLsssZlnJfH6eks2kGL1jpWotj9om893MkjCvkEGl4ZLMCbxTcga/SslhYoxr1NPVWh61TeGakHTfaCRqdPw0/0ReGXciF+kTiKU2mwE4RxfHs7kzuT3/pGGVtI6VfGMiP8+eRskY1tPI1Mdxe+50pn2Nd/WM5LhMF9WKwsxkK9OSzNxkb2NzZz2fdjVR4bHTIgJ0iAAGFHJUGnJ1Rs5LyeHUtJwB61erUJiki8M2CsOYpo3r556LU2u5KmRUKISIupWgWlE4RxdPIKT4RE4MQq1WFC61FFHl7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+INFO:root:2025-10-18 20:26:41.498935 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:26:41.498935 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:26:41.499980 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:26:41.869521 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_031.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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+INFO:root:2025-10-18 20:27:46.272157 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-18 20:27:46.272157 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:27:46.272157 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:27:46.350389 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

\n

 

' + dest = <_io.BufferedRandom name='./temp_direct/Bulletin_Note_part nom4_client_part 4_607.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject 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+INFO:root:2025-10-18 20:28:14.583363 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-18 20:28:14.583363 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-18 20:28:23.005615 : Security check : IP adresse '127.0.0.1' connected +DEBUG:matplotlib.pyplot:Loaded backend tkagg version 8.6. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) 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+DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUB.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Nirmala.ttc', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SimsunExtG.ttf', name='SimSun-ExtG', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWAD.TTF', name='Leelawadee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LEELAWDB.TTF', name='Leelawadee', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MSUIGHUR.TTF', name='Microsoft Uighur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='C:\\Windows\\Fonts\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05 +DEBUG:matplotlib.font_manager:findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Continue\\Ela_back\\Back_Office\\venv\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\DejaVuSans.ttf') with score of 0.050000. +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 1189 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 2994 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 696 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 526 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 499 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 673 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 922 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 568 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sBIT' 41 4 +DEBUG:PIL.PngImagePlugin:b'sBIT' 41 4 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 57 9 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 626 +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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BULLETIN DE NOTE
Entrée Scolaire: 2024
Promotion : Licence info Semestre 1
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xddd   qsdqsd
Né(e) le 12/10/2025
 
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Note par matière / UE  

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Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

\n

Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

\n

Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

\n

Note par matière / UE  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Matière / UERangMoy. ElMoy. Ens.Observation
INITIATION A LA PROGRAMMATION26.927.36 ssqdsqqsd
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 110.00.0  
ALGORITHMIQUE ET PROGRAMMATION IMPERATIVE 210.00.0  
\n

 

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\n

Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

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RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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Note générale  

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
RangMoy. ElValidéObservation
22.311bravoo 17/10
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Imprimé le : 18/10/2025  

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C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:36:42.603514 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:36:42.603514 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:36:42.603514 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:36:42.603514 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:36:42.604522 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:37:42.169366 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:37:42.169366 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:37:42.169366 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:37:42.169366 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:37:42.170358 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 09:39:58.680572 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:58] "POST /myclass/api/partner_login/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:39:59.484986 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:39:59.485986 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:39:59.491998 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:39:59.497034 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:39:59.500038 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:39:59.543332 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:39:59.555362 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:39:59.559355 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:39:59.565937 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/Get_List_Class_domaine/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:39:59.574948 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/Get_List_class_metier/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/Get_Ref_Pedagogique_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:39:59.582923 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:39:59.586914 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/Get_List_Class_Categorie/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/find_partner_class_like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:39:59] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:40:04.035270 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:04.038220 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:04.041218 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:04.043316 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:04.048847 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:04.054490 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:04] "POST /myclass/api/Get_List_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:40:04.063467 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:04] "POST /myclass/api/Get_Partner_All_Class_Few_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:40:04.074204 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:04] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:04] "POST /myclass/api/Get_Partner_Session_Ftion_Reduice_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:04] "POST /myclass/api/Get_List_Partner_Document_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:04] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:04] "POST /myclass/api/Get_List_Evaluation_Planification_No_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:04] "POST /myclass/api/Get_List_Unite_Enseignement_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:40:08.719829 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:08.727861 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:08.743041 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:08] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:40:08.754054 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:08] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:08] "POST /myclass/api/Get_List_Jury/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:08] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:40:10.077878 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:10.080893 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:10.089448 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:10] "POST /myclass/api/Get_Given_Jury_With_Members/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:10] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class_From_Session_Id/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:10] "POST /myclass/api/Get_List_Jury_Soutenenace/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:40:11.900287 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:11] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:40:16.038262 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:16] "POST /myclass/api/Get_Given_SessionFormation_From_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:40:16.169018 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:16.172029 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:16.175013 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:16.176029 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:16.181016 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:40:16.185023 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:16] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:16] "POST /myclass/api/Get_List_Evaluation_Inscriptio_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:16] "POST /myclass/api/Get_List_Evaluation_Final_Note_Classement_Detail_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:16] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:16] "POST /myclass/api/Get_List_Evaluation_UE_Note_Classement_With_Filter HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:40:16] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:21.740674 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:21.742673 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:21.746672 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:21] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:21.753673 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:21] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:21] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:21.765674 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:21] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:21.789675 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:21.792675 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:21.795674 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:21.799673 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:21] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:21] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:21] "POST /myclass/api/Get_List_Jury/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:21] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:30.072932 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:30.074930 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:30.076954 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:30.080917 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:30.082918 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:30.087918 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:30.103965 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:30.110918 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:30.114918 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:30.117918 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:30.121918 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:30.129952 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:30] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:31] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:32.771594 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:32.775598 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:32.780593 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:32] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:32.794602 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:32] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:32] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:32.803116 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:32.809116 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:32] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:32.812117 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:32] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:32] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:32] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:42:35.138761 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:42:35.141758 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:35] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:42:36] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:44:37.471016 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:44:37.473016 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:44:37.477016 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:44:37.479014 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:44:37] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:44:37] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:44:37] "POST /myclass/api/Get_List_Jury/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:44:37] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:51:10.049124 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:51:10.052127 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:51:10.054122 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:51:10.056126 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:51:10.059122 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:51:10.062123 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:51:10.064122 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:51:10.065121 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:51:10.072124 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 09:51:10.076136 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:51:10.079129 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 09:51:10.080128 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 09:51:10] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:52:37.232206 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:52:37.232206 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:52:37.232206 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:52:37.232206 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:52:37.232206 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:52:57.274465 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:52:57.275466 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:52:57.275466 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:52:57.275466 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:52:57.276468 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:55:08.472301 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:55:08.472301 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:55:08.473301 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:55:08.473301 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:55:08.473301 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:55:33.830298 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:55:33.830298 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:55:33.830298 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:55:33.830298 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:55:33.830298 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:55:50.987581 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:55:50.988581 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:55:50.988581 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:55:50.988581 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:55:50.988581 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:56:14.834861 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:56:14.834861 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:56:14.834861 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:56:14.834861 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:56:14.834861 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:56:51.549100 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:56:51.550096 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:56:51.550096 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:56:51.550096 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:56:51.550096 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:57:23.668625 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:57:23.668625 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:57:23.668625 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:57:23.668625 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:57:23.668625 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:57:42.325213 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:57:42.325213 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:57:42.325213 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:57:42.325213 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:57:42.325213 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:58:12.343890 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:58:12.343890 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:58:12.344893 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:58:12.344893 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:58:12.344893 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:58:41.365746 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:58:41.365746 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:58:41.365746 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:58:41.365746 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:58:41.365746 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 09:59:49.976276 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 09:59:49.976276 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 09:59:49.976276 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 09:59:49.976276 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 09:59:49.976276 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 10:00:15.379985 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 10:00:15.380998 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 10:00:15.380998 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 10:00:15.380998 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 10:00:15.380998 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 10:00:47.130656 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 10:00:47.130656 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 10:00:47.130656 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 10:00:47.130656 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 10:00:47.130656 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 10:01:28.574153 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 10:01:28.574153 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 10:01:28.574153 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 10:01:28.574153 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 10:01:28.574153 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 10:02:06.328895 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 10:02:06.329897 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 10:02:06.329897 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 10:02:06.329897 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 10:02:06.329897 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 10:03:08.816333 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 10:03:08.816333 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 10:03:08.816333 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 10:03:08.816333 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 10:03:08.816333 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 10:03:43.391038 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 10:03:43.391038 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 10:03:43.392040 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 10:03:43.392040 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 10:03:43.392040 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 10:04:21.184784 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 10:04:21.184784 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 10:04:21.184784 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 10:04:21.184784 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 10:04:21.185782 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 10:04:51.141020 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 10:04:51.141020 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 10:04:51.142019 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 10:04:51.142019 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 10:04:51.142019 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 10:06:05.123633 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 10:06:05.123633 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 10:06:05.124639 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 10:06:05.124639 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 10:06:05.124639 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 10:06:39.385911 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 10:06:39.385911 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 10:06:39.385911 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 10:06:39.385911 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 10:06:39.385911 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 10:09:13.931057 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:09:13.937055 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:09:13.942578 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:09:13.946578 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:09:13.958588 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:09:13.969107 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:13] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:13] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:09:14.001671 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:14] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:09:14.013680 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:14] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:14] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:14] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:14] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:09:14.031220 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:09:14.048262 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:09:14.059282 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:09:14.063264 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:14] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:14] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:14] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:14] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:09:15] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:12:49.701191 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:12:49.703191 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:12:49.707190 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:12:49.711191 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:49] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:12:49.724191 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:49] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:12:49.729193 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:49] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:49] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:12:49.747199 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:12:49.757200 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:49] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:12:49.767712 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:49] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:49] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:12:49.784220 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:12:49.799220 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:12:49.803226 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:49] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:49] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:49] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:50] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:12:51] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:17:56.974509 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:17:56.977509 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:17:56.983508 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:17:56.986509 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:57] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:17:57.002509 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:17:57.004508 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:57] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:17:57.013510 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:17:57.016509 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:17:57.023516 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:57] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:57] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:17:57.033515 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:57] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:17:57.046025 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:17:57.050026 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:57] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:57] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:57] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:57] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:17:58] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:20:16.873728 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:20:16.877682 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:20:16.879680 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:20:16.882683 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:20:16.886786 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:20:16.893683 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:16] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:16] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:16] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:20:16.904687 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:20:16.910195 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:16] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:20:16.914203 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:16] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:16] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:20:16.927716 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:16] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:20:16.935716 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:20:16.938715 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:16] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:16] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:16] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:17] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:20:18] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:28.659964 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:28.662975 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:28.665974 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:28.672976 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:28.678484 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:28.714486 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:28.717527 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:28.720509 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:28.724485 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:28.728485 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:28.732484 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:28.739483 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:28.744485 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:28.751484 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:28.757484 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:28.765493 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:28.769491 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:28] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:29] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:29] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:31.489441 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:31.493467 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:31.496442 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:31.501463 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:31] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:31.506475 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:31.513476 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:31] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:31] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:31.523095 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:31] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:31] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:31] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:31] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:34.432793 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:34.435815 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:34] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:35] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:29:51.531998 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:29:54.340880 : UpdateStagiairetoClass - : La valeur 'email' n'est pas presente dans la liste des arguments +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:29:54] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:21.315684 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:32:21.320686 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:32:21.323686 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:32:21.326687 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:21.336686 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:21.339693 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:21.347693 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:32:21.357204 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:21.365205 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:21.378205 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:32:21.383205 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:21.387203 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:21] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:22] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:51.837707 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:32:51.840706 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:32:51.843706 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:32:51.846707 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:51] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:51.852706 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:51] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:51.863711 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:51] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:51] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:51.871713 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:51] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:51.877759 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:51] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:51.882271 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:51] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:32:51.889356 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:32:51.895867 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:32:51.899870 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:51] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:51] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:51] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:52] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:32:52] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:33:05.882455 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:33:05.885483 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:33:05.889456 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:33:05.893461 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:33:05.898456 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:05] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:05] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:33:05.904456 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:33:05.905457 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:05] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:05] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:05] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:05] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:05] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:33:08.706133 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:33:08.710137 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:08] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:09] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:33:10.339779 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:33:10.343782 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:10] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:11] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:33:22.579232 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:33:22.602232 : UpdateStagiairetoClass -'status' - ERRORRRR AT Line : 1368 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:33:22] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:17.561821 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:17.578829 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:17.594344 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:17.606346 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:17] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:17.621343 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:17] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:17.637345 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:17] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:17.647490 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:17.658496 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:17] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:17] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:17.671492 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:17.680498 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:17.697390 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:17] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:17.708391 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:17] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:17] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:18] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:20] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:39.066868 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:39.070871 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:39.073881 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:39.078868 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:39] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:39.087895 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:39] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:39.093882 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:39.095869 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:39] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:39] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:39] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:39] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:39] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:41.546272 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:41.550245 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:41] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:42] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:50.381451 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:50.396957 : SendPre_InscriptionEmail - La valeur 'token' n'est pas presente dans la liste des arguments +INFO:root:2025-10-19 10:40:50.399471 : WARNING : Impossible d'envoyer le mail de preinscriton ŕ UpdateStagiairetoClass - l'adresse email mysytraining+apprenant1@gmail.com pour la session 68e419c2e5fea6f5328c2007 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:50] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:40:50.573233 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:50.579253 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:40:50.583255 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:50] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:50] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:40:50] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:42:01.072669 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:42:01.087631 : SendPre_InscriptionEmail - La valeur 'token' n'est pas presente dans la liste des arguments +INFO:root:2025-10-19 10:42:01.089631 : WARNING : Impossible d'envoyer le mail de preinscriton ŕ UpdateStagiairetoClass - l'adresse email SDSQ@sddf.fr pour la session 68e419c2e5fea6f5328c2007 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:42:01] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:42:01.262404 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:42:01.264403 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:42:01.267403 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:42:01] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:42:01] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:42:01] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:43:25.324650 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:43:25.328650 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:25] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:26] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:43:30.977915 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:35] "POST /myclass/api/Accept_List_AttendeeInscription/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:43:35.496869 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:43:35.499434 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:43:35.502443 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:35] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:43:35.507442 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:35] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:35] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:37] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:43:39.156065 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:43:39.161595 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:43:39.168594 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:43:39.174586 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:43:39.178588 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:43:39.185584 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:43:39.196580 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:43:39.197578 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:43:39.201588 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:43:39.206588 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:43:39.211588 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:43:39.214587 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:43:39] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:44:32.984941 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:32.988459 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:32.991457 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:32.994459 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:32.996460 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:32] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:33] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:44:33.003973 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:33] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:44:33.009975 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:33.011484 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:33] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:33] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:44:33.018493 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:33] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:33] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:44:33.024023 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:33.027021 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:33.032020 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:33] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:33] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:33] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:33] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:34] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:44:36.623637 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:36.629639 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:36.632637 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:36.635637 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:36.641637 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:36.647635 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:36] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:36] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:44:36.655634 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:36] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:36] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:36] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:36] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:36] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:44:39.400421 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:44:39.403439 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:39] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:44:40] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:51:49.342248 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:51:49] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:51:49.480422 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:51:49.482931 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:51:49.485960 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:51:49] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:51:49] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:51:49] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:52:03.516050 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:52:03] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:52:03.658340 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:52:03.659340 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:52:03.661340 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:52:03] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:52:03] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:52:03] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:53:34.381373 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:53:34.383391 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:53:34] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:53:34] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:53:35.594777 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:53:35.598782 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:53:35] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:53:36] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:55:29.471483 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:55:29.475481 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:55:29.482480 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:55:29.487482 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:55:29.490483 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:55:29.497483 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:55:29.513490 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:55:29.521004 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:55:29.532001 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:55:29.538002 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:55:29.543003 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:55:29.546003 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:29] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:55:30] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:56:00.372025 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:56:00.376541 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:56:00.379540 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:56:00.384545 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:00] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:56:00.391542 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:00] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:56:00.399545 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:00] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:00] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:56:00.407541 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:00] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:00] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:00] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:56:02.210829 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:56:02.213834 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:02] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:03] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:56:17.116509 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:18] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:56:18.404845 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:56:18.406811 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:56:18.412865 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:18] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:18] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:18] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:56:28.262522 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:56:28.266526 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:28] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:56:29] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:06.900872 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:06.903876 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:06] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:08] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:17.466428 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:17.481956 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:17.492956 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:17.508031 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:17] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:17.520026 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:17] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:17.532137 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:17] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:17] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:17.554555 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:17.567071 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:17.578078 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:17] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:17.593087 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:17] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:17.620648 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:17.629643 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:17] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:17] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:18.498815 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:18.510802 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:18] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:18.522797 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:18.533904 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:18.546010 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:18.555647 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:18.567575 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:18] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:18] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:18] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:18] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:18] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:18] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:18] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:20.664872 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:20.673923 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:20] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:22] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:24] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:42.608175 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:42.609175 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:42.613686 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:42.617788 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:42.632787 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:42.665792 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:42.667793 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:42.669793 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:42.672985 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:42.679983 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:42.688495 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:42.690543 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:42.696544 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:42.701543 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:42.708570 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:42.710569 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:42.716609 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:42] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:43] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:45.381711 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:45.386711 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:45.389710 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:45.394708 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:45.397716 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:45] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:45.402714 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:45] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:45.407715 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:45] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:45] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:45] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:45] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:45] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:57:47.130422 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:57:47.134436 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:47] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:57:47] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:44.066743 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:44.069704 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:44.074704 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:44.082710 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:44.088708 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:44.097223 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:44.105224 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:44.109224 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:44.122756 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:44.127752 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:44.136754 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:44.141754 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:44] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:45] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:49.022877 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:49.027878 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:49.035909 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:49.042907 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:49.047910 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:49.088911 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:49.096423 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:49.104423 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:49.110427 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:49.121429 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:49.130977 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:49.138491 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:49.147491 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:49.152493 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:49.157493 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:49.163491 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:49.168497 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:49] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:50] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:53.770673 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:53.775674 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:53.779682 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:53.781683 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:53] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:53.787143 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:53] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:53] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:53] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:53] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:53.803668 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:53.806667 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:53] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:53] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:58:55.521499 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:58:55.540518 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:55] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:58:56] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:59:04.411375 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:59:04] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:59:04.563560 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:59:04.564561 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:59:04.568562 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:59:04] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:59:04] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:59:04] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:59:14.100511 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:59:14.103521 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:59:14] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:59:14] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 10:59:23.043939 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 10:59:23.046937 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:59:23] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 10:59:23] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:00:35.186333 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:35] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:00:35.213334 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:00:35.228334 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:35] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:00:35.245339 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:35] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:35] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:00:35.267850 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:00:35.283851 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:35] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:00:35.297850 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:35] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:00:35.410367 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:35] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:00:35.427368 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:00:35.439368 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:00:35.456890 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:35] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:35] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:35] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:00:35.472405 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:36] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:00:43] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:01:48.059513 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:48.063532 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:48.065534 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:48.070533 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:48.072533 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:01:48.083537 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:01:48.096049 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:01:48.107049 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:01:48.113048 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:48.120049 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:01:48.128051 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:48.137049 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:48] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:01:56.749761 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:56.753759 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:56.756760 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:56.759763 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:56.765771 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:56] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:56] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:01:56.771770 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:01:56.773770 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:56] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:56] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:56] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:56] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:01:56] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:02:02.118738 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:02:02.121739 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:02:02] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:02:02] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:02:10.308699 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:02:10.312712 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:02:10] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:02:11] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:10.446482 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:10.450059 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:10.455058 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:10.464056 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:10.468059 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:10.472059 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:10.482058 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:10.489071 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:10.491091 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:10.499064 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:10.510587 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:10.512581 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:10] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:11] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:52.081777 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:52.086778 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:52.090778 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:52.093777 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:52.098778 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:52.106780 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:52.120778 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:52.134785 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:52.141295 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:03:52.146295 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:52.154295 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:03:52.168296 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:52] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:03:53] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:02.006220 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:02.007270 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:02.009783 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:02.014783 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:02.030812 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:02.066329 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:02.069329 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:02.072330 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:02.076330 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:02.086331 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:02.089329 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:02.092329 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:02.102361 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:02.107331 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:02.116331 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:02.118331 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:02.122331 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:02] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:04.519528 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:04.522528 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:04.525529 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:04.528036 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:04] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:04.532551 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:04.536556 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:04] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:04.542562 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:04] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:04] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:04] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:04] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:04] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:04:06.604791 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:04:06.607787 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:06] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:04:07] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:00.528568 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:00.535572 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:00] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:01] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:05.861146 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:05.865143 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:05] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:06] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:11.538981 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:12] "POST /myclass/api/Accept_List_AttendeeInscription/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:12.793990 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:12.794971 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:12.797957 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:12] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:12] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:12.804406 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:12] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:13] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:27.774234 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:27] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:27.972641 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:27.976658 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:27.979641 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:27] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:27] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:27] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:42.814586 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:42.816611 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:42.820639 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:42.826612 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:42.832633 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:42.866612 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:42.868640 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:42.872652 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:42.878628 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:42.881632 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:05:42.888614 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:42.902646 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:42.907619 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:42.924087 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:05:42.934102 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:42] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:43] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:44] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:05:44] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:07:17.831736 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:17.832842 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:17.836468 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:17.840457 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:17.844078 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:17.850734 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:17] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:07:17.869613 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:17.875947 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:17] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:17] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:07:17.895196 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:17.903672 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:17] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:17] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:17] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:18] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:19] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:19] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:07:41.429605 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:41.434606 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:41.439606 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:41.446116 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:41.465117 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:41.466116 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:41] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:41] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:07:41.496117 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:41.503144 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:41] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:41] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:07:41.530123 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:07:41.535122 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:41] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:41] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:41] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:41] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:42] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:07:42] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:08:10.573823 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:08:10.577839 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:08:10.579836 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:08:10.584839 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:08:10.589840 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:08:10.592839 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:08:10] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:08:10] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:08:10.618356 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:08:10.622356 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:08:10] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:08:10] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:08:10.652356 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:08:10.663361 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:08:10] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:08:10] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:08:10] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:08:10] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:08:11] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:08:12] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:11:51.169386 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:11:51.176388 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:11:51.187392 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:11:51.191947 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:11:51.200838 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:11:51.213838 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:11:51] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:11:51] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:11:51.266229 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:11:51.275858 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:11:51] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:11:51] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:11:51.315758 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:11:51.333757 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:11:51] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:11:51] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:11:51] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:11:51] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:11:53] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:11:53] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:13:59.146819 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:13:59.150826 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:13:59.156820 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:13:59.163244 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:13:59.172201 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:13:59.180750 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:13:59] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:13:59.210743 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:13:59] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:13:59.228753 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:13:59] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:13:59] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:13:59.251218 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:13:59.263376 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:13:59] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:13:59] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:13:59] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:13:59] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:14:00] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:14:00] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:15:20.586455 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:20.591494 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:20.595402 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:20.601475 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:20.611095 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:20.623110 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:20] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:20] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:15:20.644645 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:20.654817 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:20] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:20] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:15:20.686607 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:20.693493 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:20] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:20] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:20] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:21] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:22] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:22] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:15:29.650142 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:29.653139 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:15:29.662653 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:29.666156 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:29.672676 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:15:29.703704 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:29.705705 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:29.708704 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:29.714704 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:29.718705 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:29.725704 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:15:29.739707 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:29.742705 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:15:29.763706 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:15:29.770712 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:29] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:30] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:31] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:15:31] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:16:02.283598 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:16:02] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 11:17:10.545716 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 11:17:10.545716 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 11:17:10.545716 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 11:17:10.545716 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 11:17:10.546715 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 11:17:42.252928 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 11:17:42.252928 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 11:17:42.252928 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 11:17:42.252928 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 11:17:42.252928 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 11:17:45.889077 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:17:46] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:09.362604 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:09] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:10.730212 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:10.733212 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:10.737212 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:10.741210 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:10] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:10] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:10] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:10] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:11.612191 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:11.617188 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:11.621191 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:11.627189 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:11] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:11.631193 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:11] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:11.638188 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:11] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:11.645188 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:11] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:11] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:11] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:11] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:21.307179 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:21.311191 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:21.315191 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:21.319230 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:21] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:21] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:21] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:21] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:23.308843 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:23.312843 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:23.316879 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:23.321843 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:23.324900 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:23] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:23.330901 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:23] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:23.335903 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:23] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:23] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:23] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:23] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:23] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:31.689283 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:31.693288 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:31.698313 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:31.703292 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:31] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:31] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:31] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:31] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:35.508287 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:35.512283 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:35.516288 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:35.519286 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:18:35.525284 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:35] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:35.527284 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:35] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:35] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:18:35.538282 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:35] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:35] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:35] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:18:35] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\prj_common.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 11:20:10.356677 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 11:20:10.356677 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 11:20:10.356677 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 11:20:10.356677 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 11:20:10.356677 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 11:21:35.207394 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 11:21:35.207394 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 11:21:35.207394 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 11:21:35.207394 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 11:21:35.207394 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Inscription_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 11:22:06.169118 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 11:22:06.169118 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 11:22:06.169118 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 11:22:06.169118 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 11:22:06.169118 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 11:22:14.061498 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:22:14.066498 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:22:14.070497 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:22:14.075501 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:22:14.931148 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:22:14.936231 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:22:14.937214 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:22:14.949214 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:22:14.950229 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:22:14.953213 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:22:14.960214 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:22:14] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:23:08.819793 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:23:08.820794 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:23:08.823798 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:23:08.831794 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:08] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:08] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:08] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:08] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:23:11.940632 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:23:11.944638 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:23:11.949633 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:23:11.953632 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:11] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:23:11.958639 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:23:11.963637 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:11] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:23:11.970142 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:11] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:11] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:11] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:11] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:23:11] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\prj_common.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 11:24:41.833467 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 11:24:41.833467 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 11:24:41.834469 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 11:24:41.834469 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 11:24:41.834469 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 11:24:41.899763 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:41.901762 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:41.903763 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:41.905762 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:41.906762 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:41.908762 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:41] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:41] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:24:41.924762 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:41.925763 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:41] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:24:41.932762 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:41] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:24:41.941764 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:41] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:41] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:41] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:41] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:42] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:42] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:24:45.573804 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:45] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:24:46.570193 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:46.573191 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:46.580192 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:46.591743 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:46] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:46] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:46] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:46] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:24:47.706863 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:47.710372 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:47.713411 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:47.717379 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:47.721914 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:47] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:47] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:24:47.731436 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:24:47.736437 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:47] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:47] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:47] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:47] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:24:47] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:25:30.733303 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:25:30.738544 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:25:30.739556 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:25:30.744556 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:30] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:30] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:30] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:30] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:25:31.542156 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:25:31.546159 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:25:31.549154 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:25:31.553184 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:25:31.558187 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:31] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:25:31.564153 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:31] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:25:31.568154 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:31] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:31] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:31] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:31] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:25:31] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\prj_common.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 11:26:42.133051 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 11:26:42.133051 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 11:26:42.133051 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 11:26:42.134058 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 11:26:42.134058 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 11:26:56.854523 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:26:56.857524 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:26:56.858550 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:26:56.863525 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:26:56.869545 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:26:56.870541 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:26:56] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:26:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:26:56] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:26:56.905544 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:26:56.909544 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:26:56.913539 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:26:56] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:26:56.931526 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:26:56] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:26:56] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:26:56] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:26:56] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:26:57] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:26:58] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:25.818550 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:25.821549 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:25.823549 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:25.827554 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:25.829550 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:25.832554 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:25] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:25.848552 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:25] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:25] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:25.868072 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:25.876074 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:25] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:25] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:25.895599 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:25] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:25] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:25] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:26] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:27] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:37.213067 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:37.226074 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:37.235241 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:37.242246 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:37.247240 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:37.277245 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:37.287276 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:37.293798 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:37.304797 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:37.318803 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:27:37.338318 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:37.357318 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:37.371316 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:37.384324 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:27:37.427852 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:37] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:38] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:27:38] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:28:13.808609 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:13.811692 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:13.817691 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:13.823697 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:13.829697 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:13.832699 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:13] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:13] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:28:13.857213 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:13.868210 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:13] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:28:13.882215 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:13] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:13] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:28:13.915704 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:13] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:13] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:14] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:15] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:15] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:28:23.733604 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:23.737603 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:23.740604 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:23.744604 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:23.746113 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:23.754114 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:23] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:28:23.775119 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:23] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:23] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:28:23.795633 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:23.801632 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:23] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:28:23.823631 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:23] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:23] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:23] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:24] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:25] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:25] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:28:43.970450 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:43] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:28:44.090363 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:28:44.092258 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:44] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:28:45] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:29:07.521459 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:07.524459 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:07.527458 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:07.532497 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:07.534459 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:07.540458 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:07] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:07] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:29:07.560463 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:07.565464 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:07] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:07] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:29:07.593995 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:07.596994 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:07] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:07] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:07] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:07] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:08] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:08] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:29:17.864624 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:17.868024 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:17.871027 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:17.876025 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:17.882025 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:17.881025 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:17] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:29:17.910032 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:17.915030 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:17] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:17] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:29:17.938542 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:29:17.946544 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:17] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:17] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:17] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:18] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:19] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:29:19] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:05.507688 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:05.510691 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:05.513689 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:05.516689 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:05.519692 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:05.523689 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:05] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:05] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:05.538690 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:05.541697 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:05] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:05] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:05.555697 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:05.559526 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:05] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:05] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:05] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:05] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:08] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:09] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:23.253017 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:23] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:33.193253 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:33] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:34.586310 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:34.589312 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:34.594311 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:34.598308 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:34] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:34] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:34] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:34] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:35.259478 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:35.262487 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:35.266484 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:35.271484 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:35] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:35.278989 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:35] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:35.285993 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:35] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:35.291989 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:35] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:35] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:35] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:35] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:55.331464 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:55] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:30:55.412078 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:30:55.415079 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:55] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:30:55] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:31:07.444820 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:31:07.449337 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:07] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:08] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:31:19.453446 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:19] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:31:19.650506 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:31:19.652905 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:19] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:31:19.657905 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:19] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:19] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:31:25.328638 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:31:25.331641 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:31:25.334639 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:31:25.335641 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:25] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:25] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:25] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:25] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:31:26.316152 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:31:26.321152 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:31:26.325150 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:26] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:31:26.330152 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:26] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:31:26.335168 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:31:26.338179 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:26] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:31:26.343181 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:26] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:26] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:26] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:31:26] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:18.524277 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:18.528283 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:18.533283 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:18.538282 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:18.548795 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:18.553795 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:18] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:18.569799 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:18] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:18] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:18.590308 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:18.597308 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:18] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:18.610310 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:18] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:18] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:18] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:18] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:18] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:19] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:35.169867 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:35.173867 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:35.179867 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:35.185867 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:35.191872 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:35.198992 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:35] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:35.215383 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:35] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:35] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:35.229382 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:35.239384 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:35] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:35.249387 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:35] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:35] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:35] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:35] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:35] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:36] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:38.858028 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:38.862031 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:38.866030 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:38.870026 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:38] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:38] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:38] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:38] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:40.131966 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:40.136961 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:40.139988 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:40.147991 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:40] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:40.155964 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:40] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:32:40.164964 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:32:40.166979 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:40] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:40] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:40] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:40] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:32:40] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:34:59.758071 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:34:59.760184 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:34:59.764185 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:34:59.766218 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:34:59.771185 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:34:59.778185 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:34:59] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:34:59] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:34:59.802185 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:34:59] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:34:59.811185 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:34:59.823190 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:34:59] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:34:59] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:34:59] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:34:59.847203 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:34:59] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:34:59] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:00] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:00] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:03.849707 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:03.853236 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:03.856235 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:03.860233 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:03] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:03] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:03] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:03] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:04.891145 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:04.895163 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:04.898153 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:04.905165 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:04.909163 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:04] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:04] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:04] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:04.919145 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:04] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:04.928307 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:04] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:04] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:04] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:54.032715 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:54.033713 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:54.036712 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:54.038713 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:54.043713 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:54.050714 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:54] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:54] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:54] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:54.072235 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:54.076232 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:54.080232 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:54] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:54] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:54.101232 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:54] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:54] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:54] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:54] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:55] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:56.049433 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:56.053440 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:56.057450 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:56.059476 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:56.929550 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:56.932552 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:56.936558 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:56.941567 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:35:56.944554 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:56.951557 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:35:56.961573 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:35:56] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:36:45.083243 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:45.086746 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:45.091750 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:45.093753 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:45.099752 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:45.107752 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:45] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:45] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:45] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:36:45.119752 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:45.127760 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:45.136262 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:45] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:45] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:36:45.155267 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:45] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:45] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:45] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:45] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:46] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:36:59.174073 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:59.177088 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:59.180089 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:59.184091 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:59] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:59] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:59] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:36:59] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:36:59.994510 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:36:59.998512 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:37:00.004521 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:37:00.010524 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:37:00] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:37:00] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:37:00.020519 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:37:00.026619 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:37:00] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:37:00.030603 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:37:00] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:37:00] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:37:00] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:37:00] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:17.466904 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:17.469904 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:17.472905 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:17.477904 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:17.481909 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:17] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:17.500422 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:17] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:17.505936 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:17.521934 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:17] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:17.530934 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:17.538960 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:17] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:17] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:17] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:17] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:17] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:18] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:22.145294 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:22.148309 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:22.151861 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:22.157417 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:22] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:22] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:22] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:22] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:23.059747 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:23.064748 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:23.068750 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:23] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:23.074748 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:23.077746 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:23] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:23.086746 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:23] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:23.091746 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:23] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:23] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:23] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:23] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:38:30.820639 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:38:30.823669 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:30] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:38:30] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:39:06.149047 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:06.151052 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:06.154054 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:06.157051 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:06.162094 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:06.170271 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:06] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:39:06.184272 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:06] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:06] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:39:06.197273 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:06.200277 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:06] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:39:06.217272 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:06] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:06] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:06] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:06] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:06] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:07] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:39:37.164805 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:37.170173 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:37.175139 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:37.177140 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:37.184139 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:37.187139 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:37] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:37] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:37] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:39:37.221686 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:37.225688 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:37.243655 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:37] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:39:37.261656 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:37] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:37] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:37] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:37] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:37] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:38] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:39:58.197519 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:58.199520 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:58.203521 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:58.208521 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:58.215521 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:58.221520 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:58] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:58] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:58] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:39:58.245519 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:58.248552 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:39:58.254522 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:58] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:39:58.266520 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:58] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:58] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:58] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:58] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:58] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:39:59] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:40:05.341749 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:40:05.344748 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:40:05.349746 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:40:05.352773 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:05] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:05] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:05] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:05] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:40:06.220329 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:40:06.223324 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:40:06.227308 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:40:06.233309 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:40:06.238307 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:06] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:40:06.244309 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:06] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:06] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:40:06.253308 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:06] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:06] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:06] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:40:06] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:17.986924 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:17.995440 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:18.005442 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:18.013450 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:18.030961 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:18] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:18.057002 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:18] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:18.064971 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:18.074014 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:18] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:18.100026 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:18] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:18] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:18.120815 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:18] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:18] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:18] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:18] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:19] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:22.259285 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:22.262291 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:22.266296 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:22.270296 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:22] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:22] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:22] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:22] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:23.157151 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:23.160152 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:23.164162 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:23.169161 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:23.173176 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:23] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:23.177295 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:23.182296 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:23] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:23] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:23] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:23] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:23] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:23] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:33.632298 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:33.636293 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:33] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:33] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:39.959348 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:39] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:42:40.034695 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:42:40.037714 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:40] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:42:40] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:04.841963 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:04.843964 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:04.849964 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:04.854963 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:04.859964 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:04.864966 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:04] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:04] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:04.886968 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:04.892977 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:04] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:04.908494 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:04] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:04] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:04.928500 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:04] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:04] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:04] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:05] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:06] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:11.590720 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:12] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:15.728653 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:15.729685 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:15.734668 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:15.738655 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:15] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:15] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:15] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:15] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:16.660397 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:16.665403 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:16.670412 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:16.675427 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:16] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:16.684426 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:16] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:16] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:16.690930 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:16.693944 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:16] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:16] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:16] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:16] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:32.078678 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:32] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:32.255227 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:32.256261 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:32] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:33] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:35.435259 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:35.439282 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:35.440273 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:35.444275 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:35] "POST /myclass/api/Get_List_Unite_Enseignement_Of_Given_Class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:35] "POST /myclass/api/Get_List_Class_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:35] "POST /myclass/api/Get_Inscrit_List_EU_Type_Evaluation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:35] "POST /myclass/api/Get_Inscrit_List_EU/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:36.555771 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:36.559769 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:36.562272 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:36.566276 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:36.569282 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:36] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:36.575280 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:36] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:36.581788 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:36] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:36] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:36] "POST /myclass/api/Get_List_Participant_Notes/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:36] "POST /myclass/api/Get_List_Jury_Soutenenace_With_Filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:36] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.123361 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.132829 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.134837 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.144842 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.148850 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.179364 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.181363 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.184368 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.189368 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.195411 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.201928 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.203956 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.209926 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.216926 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.219925 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.223925 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.229927 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.240938 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.246441 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:48.251448 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.270467 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.276468 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:48.284486 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:48] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:49] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:43:50.381345 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:43:50.384346 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:50] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:43:51] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:28.527202 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:28.529204 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:28.533205 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:28.539204 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:28.548205 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:28.583208 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:28.586207 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:28.588209 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:28.590208 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:28.596244 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:28.600244 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:28.614246 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:28.622245 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:28.636265 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:28.644250 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:28] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.114550 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:29.117551 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:29.123549 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:29.126549 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.138555 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.148555 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:29.152557 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.164931 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:29.172932 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.182940 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:29.192444 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.215451 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.226451 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.261450 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.280457 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:29.281456 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.306971 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.324986 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.341970 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:47:29.347969 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.371969 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.386972 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.431528 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.444525 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.460196 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.481243 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.506014 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.608014 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.654017 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:47:29.672554 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:29] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:30] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:30] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:30] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:31] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:47:31] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:48:15.904931 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:48:15.907931 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:48:15.911929 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:48:15.917932 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:48:15.919931 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:48:15.926933 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:48:15] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:48:15] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:48:15.944931 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:48:15.949934 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:48:15] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:48:15.970971 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:48:15] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:48:15.982468 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:48:15] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:48:16] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:48:16] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:48:16] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:48:17] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:48:17] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\prj_common.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 11:49:37.618615 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 11:49:37.619599 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 11:49:37.619599 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 11:49:37.619599 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 11:49:37.619599 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 11:49:59.012829 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:49:59.016829 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:49:59.020828 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:49:59.024865 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:49:59.028832 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:49:59.033833 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:49:59.034834 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:49:59] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:49:59] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:49:59] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:49:59] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:49:59] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:49:59] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:49:59] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:01.963800 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:50:01.966795 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:01] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:02] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:06.528823 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:50:06.541820 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:06] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:06.567331 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:06] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:06] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:06.586331 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:06] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:06.607330 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:06] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:06.639313 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:06] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:06.654344 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:06] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:08.112449 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 11:50:08.133438 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:08] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:20.836305 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:20] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:21.003664 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:21] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:21.026765 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:21] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 11:50:21.052683 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:21] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 11:50:21] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:00:50.823443 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:00:50.823443 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:00:50.823443 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:00:50.823443 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:00:50.823443 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:02:05.063633 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:02:05.063633 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:02:05.063633 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:02:05.063633 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:02:05.063633 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 12:02:24.397786 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:24.400786 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:24.402787 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:24.405787 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:24.409789 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:24.415299 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:24] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:24] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:02:24.427315 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:24] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:24] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:24] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:24] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:24] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:02:27.868117 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:27.872111 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:27] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:28] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:02:50.466003 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:50] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:02:50.491003 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:50.511020 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:50] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:02:50.520019 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:50] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:02:50.552521 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:50] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:02:50.570521 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:50.651030 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:50.691030 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:50.714031 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:50.729050 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:50.752588 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:50] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:50] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:50] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:02:50.799548 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:50.861300 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:02:50.902285 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:53] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:02:53.849427 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:55] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:55] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:55] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:02:55] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:03:03] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:03:07] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:03:09.109806 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

 

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_74 

Destinataire : part nom5_client

adresse04
code_postal04  - adresse04
pays04

\n
\n

Client : part nom5_client part 5 mysy1000formation+01@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

 

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_74 

Destinataire : part nom5_client

adresse04
code_postal04  - adresse04
pays04

\n
\n

Client : part nom5_client part 5 mysy1000formation+01@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'PLTE' 41 6 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 59 825 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:03:13] "POST /myclass/api/Invoice_Inscrption_With_Split_Session_By_Inscription_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:03:13.984403 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:03:15] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:03:23.206559 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:03:23.231632 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:03:23] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:03:23.720055 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:03:23.752630 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:03:23] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:03:41] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:03:41] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:10:18.194268 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:10:18.194268 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:10:18.195268 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:10:18.195268 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:10:18.195268 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:10:39.769258 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:10:39.769258 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:10:39.769258 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:10:39.769258 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:10:39.769258 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:11:42.937348 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:11:42.937348 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:11:42.937348 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:11:42.937348 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:11:42.937348 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:12:31.348026 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:12:31.348026 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:12:31.348026 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:12:31.348026 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:12:31.348026 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:14:00.778620 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:14:00.779631 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:14:00.779631 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:14:00.779631 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:14:00.779631 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 12:15:27.168093 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.173091 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.178090 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.183113 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.188094 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.193092 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.200092 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.225092 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.233100 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.239099 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.247622 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.258610 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.265644 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.282644 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.287649 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.307161 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.314160 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.328161 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.473438 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.475953 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.483954 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.488955 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.500953 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.508953 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.526952 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.533960 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.534958 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.542959 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:15:27.569988 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:15:27.579990 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:27] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:29] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:15:29] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:36.987574 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:36.990576 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:36.992574 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:36.994573 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:36.999577 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.007576 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.016575 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.024581 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:37.026580 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.034682 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.042689 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:37.046687 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:37.054688 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.073880 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.086881 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.109887 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:37.112912 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.140715 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:37.152701 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:37.153700 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.173701 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.181705 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.198702 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:37.205708 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.231226 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.243744 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.252741 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.268741 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:37.276740 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:37.305772 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:37] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:39] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:39] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:41.468427 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:41.471428 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:41.475442 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:41] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:41.483427 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:41.487434 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:41] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:41.492425 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:41.498432 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:41] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:41] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:41] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:41] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:41] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:43.843655 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:43.847656 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:43] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:44] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:18:50.725552 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:18:50.729548 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:50] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:18:51] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:17.281022 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:17.287037 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:17.291038 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:17.292038 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:17.299556 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:17.318108 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:17.339119 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:17.344638 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:17.355634 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:17.359656 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:17.363642 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:17.368641 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:17.934381 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:17.942409 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:17.963388 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:17.973384 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:17.977383 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:17] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:17.991398 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:18.010387 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:18.026920 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:18.031902 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:18.068423 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:18.083429 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:18.151954 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:18.163952 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:18.171961 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:18.195497 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:18.207034 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:18.228466 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:18.252467 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:18] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:20] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:20] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:20.390822 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:20.392822 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:20.396831 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:20] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:20.401848 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:20.404829 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:20.405830 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:20] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:20.412357 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:20] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:20] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:20] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:20] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:20] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:21.854189 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:21.858205 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:21] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:23] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:26.948646 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:26] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:27.095361 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:27.098340 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:27.102341 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:27] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:27] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:27] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:28.998252 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:29.000249 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:29] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:30] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.427839 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.433841 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.438351 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.444373 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.454372 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.463397 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.470400 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.478909 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.483912 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.489921 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.501909 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.509909 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.525915 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.546926 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.555454 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.590451 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.600451 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.610468 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.663107 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.668078 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.686086 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.695093 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.713084 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.731118 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.739078 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.745102 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.752122 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.776166 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:20:56.782166 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:20:56.822167 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:56] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:57] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:57] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:59] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:20:59] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:00.066758 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:00.070754 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:00.074756 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:00.079756 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:00] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:00] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:00.089777 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:00] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:00.094756 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:00.100761 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:00] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:00] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:00] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:00] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:01.939185 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:01.942197 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:01] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:03] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:30.726416 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:30.733416 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:30.736416 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:30.752414 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:30] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:30.761413 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:30] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:30.767413 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:30.773417 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:30] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:30] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:30.787414 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:30.790424 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:30] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:30.797425 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:30] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:30] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:30.810559 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:30.817081 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:30] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:30] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:30] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:31.703158 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:31.710377 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:31.720380 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:31.728379 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:31.741377 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:31.757377 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:31.765377 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:31.779378 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:31.786377 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:31.796389 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:31.812906 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:31.834944 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:31.858948 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:31.873944 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:31.923492 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:31.933546 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:31] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:31.970547 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:32] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:32] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:32] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:32.012603 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:32] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:32] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:32] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:33] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:42.632514 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:42.636515 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:42.642521 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:42.646520 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:42] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:42] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:42.654514 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:42.661511 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:42.665519 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:42] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:42] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:42] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:42] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:42] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:45.628149 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:45.631138 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:45] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:46] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.159442 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.161425 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.166422 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.173424 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.176424 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.191423 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.198424 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.201424 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.215681 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.219710 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.226713 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.234283 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.237871 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.254933 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.265951 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.274951 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.283952 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.319060 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.364989 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.378916 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.391886 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.414871 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.417871 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.448992 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.473554 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.482600 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.492163 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.499169 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:21:56.516172 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:21:56.545252 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:56] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:57] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:57] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:58] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:21:58] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:10.437938 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:10.442938 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:10.450035 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:10.454951 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:10.468490 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:10] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:10] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:10.488703 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:10] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:10] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:10.505852 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:10] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:10.525000 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:10.531902 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:10] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:10] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:10.550507 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:10.556585 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:10.570047 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:10] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:10] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:10] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:11.345053 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:11.349051 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:11.354064 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:11.372061 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:11.385077 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:11.399147 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:11.400193 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:11.421161 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:11.432159 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:11.448153 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:11.459168 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:11.474157 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:11.502737 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:11.540845 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:11.562881 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:11.594980 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:11.643583 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:11.656583 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:11] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:12] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:13.463580 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:13.477577 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:13] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:13.488589 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:13.502144 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:13] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:13.514159 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:13] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:13] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:13.530695 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:13] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:13] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:13.550719 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:13] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:14] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:15] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:22:16.824395 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:22:16.839948 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:16] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:22:20] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:09.591067 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:09.596070 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:09.598624 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:09.609641 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:09.614626 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:09] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:09] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:09.622176 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:09.628178 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:09] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:09] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:09] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:09] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:09] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:11.679626 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:11.683636 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:11] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:12] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:24.657557 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:24.659560 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:24.660558 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:24.662557 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:24.668557 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:24.672074 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:24.690081 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:24.694081 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:24.701081 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:24.702596 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:24.711112 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:24.725208 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:24.732209 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:24.745210 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:24.749208 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:24.772214 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:24.778731 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:24.784732 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:24] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:25.150432 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:25.154432 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:25.162442 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:25.163442 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:25.168440 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:25.176965 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:25.188972 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:25.218269 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:25.227270 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:23:25.233269 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:25.237270 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:23:25.242270 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:25] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:26] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:28] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:23:28] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:00.020242 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.023208 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:00.031219 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.035220 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.040225 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:00.048281 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.052805 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:00.475527 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.479526 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.481525 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.485529 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.488526 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.494526 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:00.503529 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:00.507527 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:00.520527 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.528526 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.530530 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:00.534534 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:00] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.487559 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:01.489560 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:01.493560 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:01.494561 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.502560 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.509561 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.520559 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.528581 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.546566 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:01.548564 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.555082 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:01.578081 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.590085 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.617080 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.658986 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.674106 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.715092 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:01.747643 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:01] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:02] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:02] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:03.283398 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:03.286398 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:03.289399 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:03.294915 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:03.297915 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:03] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:03.308915 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:03] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:03] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:03.325208 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:03] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:03] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:03] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:03] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:03] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:24:05.265301 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:24:05.268326 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:05] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:24:06] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:38.571275 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:38.573258 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:38.579262 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:38.583267 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:38.588271 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:38] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:38] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:38.597284 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:38.602467 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:38.608459 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:38] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:38] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:38] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:38] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:38] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:38.625443 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:38.628445 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:38.633449 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:38.637444 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:38] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:38] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:38] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:39.276396 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:39.281398 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:39.284397 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:39.295416 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:39.303917 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:39.315478 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:39.333477 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:39.345494 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:39.352481 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:39.360519 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:39.371520 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:39.374083 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:39.401582 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:39.410616 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:39.438667 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:39.456693 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:39.481669 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:39.492717 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:39] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:40] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:41] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:42.706850 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:42.709829 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:42.713832 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:42.716848 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:42.719857 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:42] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:42.727899 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:42] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:42.735937 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:42] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:42] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:42] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:42] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:42] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:25:47.848015 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:25:47.863020 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:47] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:25:48] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:00.927631 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:00.930628 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:00] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:01] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:30.407959 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:30.410960 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:30.413970 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:30.417970 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:30.418969 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:30.424972 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:30.438517 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:30.448520 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:30.450025 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:30.457033 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:30.462034 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:30.469032 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:30] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:31.134339 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:31.141854 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:31.144855 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:31.153855 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:31.163857 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:31.184853 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:31.186855 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:31.198854 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:31.213877 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:31.221889 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:31.231407 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:31.243409 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:31.267955 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:31.296120 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:31.338687 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:31.354683 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:31.394446 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:31.417450 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:31] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:32] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:33] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:36.631929 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:36.635926 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:36.641328 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:36.644326 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:36] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:36.653334 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:36] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:36.655366 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:36] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:36.661335 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:36] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:36] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:36] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:36] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:40.336222 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:40.340221 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:40] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:41] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:26:48.688265 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:26:48.691291 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:48] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:26:49] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:40.186906 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:40.196914 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:40.200913 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:40.206920 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:40.211921 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:40.223435 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:40.236434 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:40.243434 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:40.251434 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:40.255434 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:40.263434 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:40.268434 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:40.274444 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:40.301961 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:40.318472 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:40.334474 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:40.339475 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:40.365468 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:40] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:41] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:46.902432 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:46.911432 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:46.933448 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:46] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:46.949990 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:46.962993 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:46] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:46] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:46] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:47.071121 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:47.078154 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:47.084143 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:47.093336 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:47.101347 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:47.126880 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:47.128889 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/Get_List_Ressource_Materielle_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:47.142900 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:47.172506 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/Get_List_Partner_Or_Default_session_step/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/GetPartnerAttestation_Certificat/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:47.193047 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:47.207049 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:47.224045 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/Get_List_Site_Formation_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:47] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:49] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:55.406069 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:55.410103 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:55.413069 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:55.417067 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:55] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:55.421066 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:55.427068 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:55] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:55] "POST /myclass/api/Get_Session_Sequence_List/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:55.437068 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:55] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:55] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:55] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:55] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:27:57.134106 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:27:57.137450 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:57] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:27:59] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:03.402416 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:03.410939 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:03] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:09.394363 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:09] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:09.567228 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:09.589244 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:09] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:09.615238 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:09] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:09] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:11.764095 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:11.782096 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:11] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:13] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:14] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:20.895237 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:20.898223 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:20] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:22] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:26.148872 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:27] "POST /myclass/api/Accept_List_AttendeeInscription/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:27.857373 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:27.859393 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:27.864390 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:27.866383 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:27] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:27] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:27] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:29] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:43.582952 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:43] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:43.785548 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:43.788555 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:43.790057 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:43] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:43] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:43] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:52.978393 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:52.981393 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:52.983396 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:52] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:52.988397 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:52.999398 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:52] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:53] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:53] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:53.042399 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:53.044399 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:53.047908 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:53.050910 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:53.056910 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:28:53.065917 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:53] "POST /myclass/api/Get_Partner_List_Partner_Client_with_filter_Like/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:53.074422 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:53] "POST /myclass/api/Get_Partner_Object_Specific_Valide_Displayed_Fields/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:53.082428 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:53] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:53.098737 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:53] "POST /myclass/api/getRecodedStagiaireImage_from_front/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:28:53.107763 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:53] "POST /myclass/api/GetSessionFormation/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:53] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:53] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:53] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:54] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:28:54] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:29:02.957361 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:29:02] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:29:03.078430 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:29:03.080430 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:29:03] "POST /myclass/api/GetAttendeeDetail_perSession/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:29:04] "POST /myclass/api/Get_Statgaire_List_Partner_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:29:06.914157 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:29:06.916157 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:29:06] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:29:07.451791 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:29:07.454790 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:29:07] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:29:07] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:29:08] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:29:26.574627 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

 

\n\n\n\n\n\n\n\n
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Facture n° NEW_Invoice_75 

Destinataire : qsdqs


  -

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Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

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Intitulé de la formation : CONDUIRE UN PROJET  

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\n

 

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
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Condition de règlement :    

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Date échéance : 19/10/2025 

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

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\n

 

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JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

 

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_75 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'PLTE' 41 6 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 59 816 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:29:27] "POST /myclass/api/Invoice_Inscrption_With_Split_Session_By_Inscription_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:29:27.705912 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:29:28] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:34:59.760979 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:34:59.760979 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:34:59.760979 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:34:59.761980 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:34:59.761980 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 12:34:59.889512 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:34:59.892512 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:34:59.894514 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:34:59.898031 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:34:59.900030 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:34:59.904031 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:34:59.908030 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:34:59.915043 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:34:59.923043 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:34:59] "POST /myclass/api/Get_List_Ressource_Humaine_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:34:59.944557 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:34:59] "POST /myclass/api/Get_Given_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:34:59] "POST /myclass/api/Get_List_Paiement_Condition/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:34:59.965558 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:34:59.973559 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:34:59] "POST /myclass/api/Get_List_Partner_Basic_Setup/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:34:59] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:34:59.996562 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:35:00.013559 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:35:00] "POST /myclass/api/Get_List_Partner_Produit_Service/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:35:00] "POST /myclass/api/get_partner_class/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:35:00] "POST /myclass/api/Get_List_Partner_Order_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:35:00] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:35:00] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:35:00] "POST /myclass/api/GetAllValideSessionPartner_List/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:35:01] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:35:02] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:35:02] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:41:29.764521 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:41:29.769519 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:41:33.459391 : check_source_ipv4 -[Errno 22] Invalid argument - ERRORRRR AT Line : 1057 +INFO:root:2025-10-19 12:41:33.462495 : check_source_ipv4 -[Errno 22] Invalid argument - ERRORRRR AT Line : 1057 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:41:33] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 500 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:41:33] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 500 - +ERROR:werkzeug:Error on request: +Traceback (most recent call last): + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Initiale\Ela_back\Back_Office_FI\prj_common.py", line 1057, in check_source_ipv4 + myprint(" Security check : IP adresse '"+str(source_ip)+"' connected") + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Initiale\Ela_back\Back_Office_FI\prj_common.py", line 74, in myprint + print(Fore.RED+str(datetime.now()) + " : " + str(message)+Style.RESET_ALL) +OSError: [Errno 22] Invalid argument + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\werkzeug\debug\__init__.py", line 329, in debug_application + app_iter = self.app(environ, start_response) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 2552, in __call__ + return self.wsgi_app(environ, start_response) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 2532, in wsgi_app + response = self.handle_exception(e) + ^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask_cors\extension.py", line 176, in wrapped_function + return cors_after_request(app.make_response(f(*args, **kwargs))) + ^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 2529, in wsgi_app + response = self.full_dispatch_request() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 1825, in full_dispatch_request + rv = self.handle_user_exception(e) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask_cors\extension.py", line 176, in wrapped_function + return cors_after_request(app.make_response(f(*args, **kwargs))) + ^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 1821, in full_dispatch_request + rv = self.preprocess_request() + ^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 2313, in preprocess_request + rv = self.ensure_sync(before_func)() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Initiale\Ela_back\Back_Office_FI\main.py", line 131, in before_request + if mycommon.check_source_ipv4(str(request.remote_addr)) is False: + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Initiale\Ela_back\Back_Office_FI\prj_common.py", line 1065, in check_source_ipv4 + myprint(str(inspect.stack()[0][3]) + " -" + str(e) + " - ERRORRRR AT Line : " + str(exc_tb.tb_lineno)) + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Initiale\Ela_back\Back_Office_FI\prj_common.py", line 74, in myprint + print(Fore.RED+str(datetime.now()) + " : " + str(message)+Style.RESET_ALL) +OSError: [Errno 22] Invalid argument + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\werkzeug\serving.py", line 333, in run_wsgi + execute(self.server.app) + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\werkzeug\serving.py", line 322, in execute + for data in application_iter: + ^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\werkzeug\debug\__init__.py", line 364, in debug_application + environ["wsgi.errors"].write("".join(tb.render_traceback_text())) +OSError: [Errno 22] Invalid argument +ERROR:werkzeug:Error on request: +Traceback (most recent call last): + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Initiale\Ela_back\Back_Office_FI\prj_common.py", line 1057, in check_source_ipv4 + myprint(" Security check : IP adresse '"+str(source_ip)+"' connected") + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Initiale\Ela_back\Back_Office_FI\prj_common.py", line 74, in myprint + print(Fore.RED+str(datetime.now()) + " : " + str(message)+Style.RESET_ALL) +OSError: [Errno 22] Invalid argument + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\werkzeug\debug\__init__.py", line 329, in debug_application + app_iter = self.app(environ, start_response) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 2552, in __call__ + return self.wsgi_app(environ, start_response) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 2532, in wsgi_app + response = self.handle_exception(e) + ^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask_cors\extension.py", line 176, in wrapped_function + return cors_after_request(app.make_response(f(*args, **kwargs))) + ^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 2529, in wsgi_app + response = self.full_dispatch_request() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 1825, in full_dispatch_request + rv = self.handle_user_exception(e) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask_cors\extension.py", line 176, in wrapped_function + return cors_after_request(app.make_response(f(*args, **kwargs))) + ^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 1821, in full_dispatch_request + rv = self.preprocess_request() + ^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\flask\app.py", line 2313, in preprocess_request + rv = self.ensure_sync(before_func)() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Initiale\Ela_back\Back_Office_FI\main.py", line 131, in before_request + if mycommon.check_source_ipv4(str(request.remote_addr)) is False: + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Initiale\Ela_back\Back_Office_FI\prj_common.py", line 1065, in check_source_ipv4 + myprint(str(inspect.stack()[0][3]) + " -" + str(e) + " - ERRORRRR AT Line : " + str(exc_tb.tb_lineno)) + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Initiale\Ela_back\Back_Office_FI\prj_common.py", line 74, in myprint + print(Fore.RED+str(datetime.now()) + " : " + str(message)+Style.RESET_ALL) +OSError: [Errno 22] Invalid argument + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\werkzeug\serving.py", line 333, in run_wsgi + execute(self.server.app) + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\werkzeug\serving.py", line 322, in execute + for data in application_iter: + ^^^^^^^^^^^^^^^^^ + File "C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\werkzeug\debug\__init__.py", line 364, in debug_application + environ["wsgi.errors"].write("".join(tb.render_traceback_text())) +OSError: [Errno 22] Invalid argument +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:42:26.573546 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:42:26.573546 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:42:26.574547 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:42:26.574547 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:42:26.574547 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:42:52.458142 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:42:52.458142 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:42:52.458142 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:42:52.458142 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:42:52.458142 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\email_mgt.py', reloading +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\email_mgt.py', reloading +INFO:werkzeug: * Restarting with stat +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 12:44:20.745932 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 12:44:20.745932 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 12:44:20.745932 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 12:44:20.745932 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 12:44:20.746933 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 12:46:39.069403 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:46:39] "GET /myclass/api/ManualSendInvoiceEmailRIB_CIC_JMJFormation HTTP/1.1" 308 - +INFO:root:2025-10-19 12:46:39.077966 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '\n\n\n\n\t
\n\t\tMysy Training Logo \n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t\t\n\t\t\t\t
\n\t\t\t\t\n\t\t\t\t\tJMJ FORMATION
\n\t\t\t\t\t388 Chemin Long Pré
\n\t\t\t\t\t97232 Le Lamentin
\n\t\t\t\t\tMartinique
\n\n\t\t\t\t
\n\n\n\t\t\t\t\n\n\t\t\t\t\tFacture n° FACT_20251012\n\n\n\t\t\t\t
\n\n\n\n\t\t\t\t\n\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t
Date de facture: 19/10/2025 Date échéance : 19/10/2025
\n\n\t\t\t\t\n\t\t\t\t\tOrigine : Contrat MTT/2025_N°0012/JMJ FORMATION
\n\t\t\t\t\tPériode : 14/10/2025 au 13/11/2025 \n\t\t\t\t\n\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\n\n\t\t\t\t\t
DescriptionQuantitéPrix unitaire (HT)Montant (HT)
ELYOS - Pack BASIQUE
\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t
1130 €130 €
 
\n\t\t\t\t\n\n\n\t\t\t\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t
    Montant HT: 130   €
    TVA : 11.05   €
    Montant TTC : 141,05   €
\n\n\t\t\t
\n\t\t\t
\n\t\t\t
\n\n\t\t\t\n\t\t\t\tRčglement : Virement bancaire

\n\n\n\n\n\t\t\t

\n\t\t\t\t> Relevé d\'identité bancaire\n\t\t\t

\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\n\n\n\t\t\t\t\n\n\t\t\t
BanqueCode agenceNuméro de compteClé RIB
30087338560002128550340
\n\n\t\t\t

\n\t\t\t\t> Identification internationale\n\t\t\t

\n\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\t\t\t\t\t\n\n\n\n\t\t\t\t\n\n\t\t\t
IBANCode BIC
FR76 3008 7338 5600 0212 8550 340CMCIFRPP
\n\n\t\t\n\n\t\n\n\t
\n\t
\n\t\n\n\t\tTermes et conditions
\n\n\t\tPas d\'escompte accordé pour paiement anticipé.
\n\t\tEn cas de non-paiement ŕ la date d\'échéance, des pénalités calculées ŕ trois fois le taux d\'intéręt légal\n\t\tseront appliquées.
\n\t\tTout retard de paiement entraînera une indemnité forfaitaire pour frais de recouvrement de 40€.
\n\n\t\n\t
\n\t
\n\t\t\n\n\t\t\t

MySy Training
\n\n\t\t\t\tMySy Training Technology (MTT), société par actions simplifiée au capital de 10 000 euros, dont le sičge\n\t\t\t\tsocial est\n\t\t\t\tsitué 2, place des magnolias, 77680, Roissy en Brie, immatriculée au Registre du Commerce et des\n\t\t\t\tSociétés sous le numéro 917 500 860 R.C.S. Melun\n\t\t\t

\n\t\t\n\t
\n\n\n' + dest = <_io.BufferedRandom name='./Invoices/invoice_FACT_20251012.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject './../img/MYSY-LOGO-BLUE.png', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.files:URLParts: ParseResult(scheme='c', netloc='', path='\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__', params='', query='', fragment=''), 'c' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': 75.0, 'height': None, 'align': None, 'id': None} +DEBUG:xhtml2pdf.files:Unrecognized scheme, assuming local file path +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 25 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 6645 +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 25 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 6645 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:46:39] "GET /myclass/api/ManualSendInvoiceEmailRIB_CIC_JMJFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:50:05.888531 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:50:05] "GET /myclass/api/ManualSendInvoiceEmailRIB_CIC_JMJFormation HTTP/1.1" 308 - +INFO:root:2025-10-19 12:50:05.928323 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '\n\n\n\n\t
\n\t\tMysy Training Logo \n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t\t\n\t\t\t\t
\n\t\t\t\t\n\t\t\t\t\tJMJ FORMATION
\n\t\t\t\t\t388 Chemin Long Pré
\n\t\t\t\t\t97232 Le Lamentin
\n\t\t\t\t\tMartinique\n\n\t\t\t\t
\n\n\n\t\t\t\t\n\n\t\t\t\t\tFacture n° FACT_20251012\n\n\n\t\t\t\t
\n\n\n\n\t\t\t\t\n\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t
Date de facture: 19/10/2025 Date échéance : 19/10/2025
\n\n\t\t\t\t\n\t\t\t\t\tOrigine : Contrat MTT/2025_N°0012/JMJ FORMATION
\n\t\t\t\t\tPériode : 14/10/2025 au 13/11/2025 \n\t\t\t\t\n\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\n\n\t\t\t\t\t
DescriptionQuantitéPrix unitaire (HT)Montant (HT)
ELYOS - Pack BASIQUE
\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t
1130 €130 €
 
\n\t\t\t\t\n\n\n\t\t\t\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t
    Montant HT: 130   €
    TVA : 11.05   €
    Montant TTC : 141,05   €
\n\n\t\t\n\t\t\t
\n\n\t\t\t\n\t\t\t\tRčglement : Virement bancaire

\n\n\n\n\n\t\t\t

\n\t\t\t\t> Relevé d\'identité bancaire\n\t\t\t

\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\n\n\n\t\t\t\t\n\n\t\t\t
BanqueCode agenceNuméro de compteClé RIB
30087338560002128550340
\n\n\t\t\t

\n\t\t\t\t> Identification internationale\n\t\t\t

\n\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\t\t\t\t\t\n\n\n\n\t\t\t\t\n\n\t\t\t
IBANCode BIC
FR76 3008 7338 5600 0212 8550 340CMCIFRPP
\n\n\t\t\n\n\t\n\n\t
\n\n\t\n\n\t\tTermes et conditions
\n\n\t\tPas d\'escompte accordé pour paiement anticipé.
\n\t\tEn cas de non-paiement ŕ la date d\'échéance, des pénalités calculées ŕ trois fois le taux d\'intéręt légal\n\t\tseront appliquées.
\n\t\tTout retard de paiement entraînera une indemnité forfaitaire pour frais de recouvrement de 40€.
\n\n\t\n\n\t
\n\t\t\n\n\t\t\t

MySy Training
\n\n\t\t\t\tMySy Training Technology (MTT), société par actions simplifiée au capital de 10 000 euros, dont le sičge\n\t\t\t\tsocial est\n\t\t\t\tsitué 2, place des magnolias, 77680, Roissy en Brie, immatriculée au Registre du Commerce et des\n\t\t\t\tSociétés sous le numéro 917 500 860 R.C.S. Melun\n\t\t\t

\n\t\t\n\t
\n\n\n' + dest = <_io.BufferedRandom name='./Invoices/invoice_FACT_20251012.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject './../img/MYSY-LOGO-BLUE.png', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.files:URLParts: ParseResult(scheme='c', netloc='', path='\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__', params='', query='', fragment=''), 'c' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': 75.0, 'height': None, 'align': None, 'id': None} +DEBUG:xhtml2pdf.files:Unrecognized scheme, assuming local file path +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 25 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 6645 +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 15% +DEBUG:xhtml2pdf.tables:Col 3 has width 15% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 15% +DEBUG:xhtml2pdf.tables:Col 3 has width 15% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '15%', '15%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 25 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 6645 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:50:06] "GET /myclass/api/ManualSendInvoiceEmailRIB_CIC_JMJFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:50:56.738024 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:50:56] "GET /myclass/api/ManualSendInvoiceEmailRIB_CIC_JMJFormation HTTP/1.1" 308 - +INFO:root:2025-10-19 12:50:56.760418 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '\n\n\n\n\t
\n\t\tMysy Training Logo \n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t\t\n\t\t\t\t
\n\t\t\t\t\n\t\t\t\t\tJMJ FORMATION
\n\t\t\t\t\t388 Chemin Long Pré
\n\t\t\t\t\t97232 Le Lamentin
\n\t\t\t\t\tMartinique\n\n\t\t\t\t
\n\n\n\t\t\t\t\n\n\t\t\t\t\tFacture n° FACT_20251012\n\n\n\t\t\t\t
\n\n\n\n\t\t\t\t\n\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t
Date de facture: 19/10/2025 Date échéance : 19/10/2025
\n\n\t\t\t\t\n\t\t\t\t\tOrigine : Contrat MTT/2025_N°0012/JMJ FORMATION
\n\t\t\t\t\tPériode : 14/10/2025 au 13/11/2025 \n\t\t\t\t\n\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\n\n\t\t\t\t\t
DescriptionQuantitéPrix unitaire (HT)Montant (HT)
ELYOS - Pack BASIQUE
\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t
1130 €130 €
 
\n\t\t\t\t\n\n\n\t\t\t\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t
    Montant HT: 130   €
    TVA : 11.05   €
    Montant TTC : 141,05   €
\n\n\t\t\n\t\t\t
\n\n\t\t\t\n\t\t\t\tRčglement : Virement bancaire

\n\n\n\n\n\t\t\t

\n\t\t\t\t> Relevé d\'identité bancaire\n\t\t\t

\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\n\n\n\t\t\t\t\n\n\t\t\t
BanqueCode agenceNuméro de compteClé RIB
30087338560002128550340
\n\n\t\t\t

\n\t\t\t\t> Identification internationale\n\t\t\t

\n\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\t\t\t\t\t\n\n\n\n\t\t\t\t\n\n\t\t\t
IBANCode BIC
FR76 3008 7338 5600 0212 8550 340CMCIFRPP
\n\n\t\t\n\n\t\n\n\t
\n\n\t\n\n\t\tTermes et conditions
\n\n\t\tPas d\'escompte accordé pour paiement anticipé.
\n\t\tEn cas de non-paiement ŕ la date d\'échéance, des pénalités calculées ŕ trois fois le taux d\'intéręt légal\n\t\tseront appliquées.
\n\t\tTout retard de paiement entraînera une indemnité forfaitaire pour frais de recouvrement de 40€.
\n\n\t\n\n\t
\n\t\t\n\n\t\t\t

MySy Training
\n\n\t\t\t\tMySy Training Technology (MTT), société par actions simplifiée au capital de 10 000 euros, dont le sičge\n\t\t\t\tsocial est\n\t\t\t\tsitué 2, place des magnolias, 77680, Roissy en Brie, immatriculée au Registre du Commerce et des\n\t\t\t\tSociétés sous le numéro 917 500 860 R.C.S. Melun\n\t\t\t

\n\t\t\n\t
\n\n\n' + dest = <_io.BufferedRandom name='./Invoices/invoice_FACT_20251012.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject './../img/MYSY-LOGO-BLUE.png', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.files:URLParts: ParseResult(scheme='c', netloc='', path='\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__', params='', query='', fragment=''), 'c' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': 75.0, 'height': None, 'align': None, 'id': None} +DEBUG:xhtml2pdf.files:Unrecognized scheme, assuming local file path +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 25 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 6645 +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 25 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 6645 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:50:57] "GET /myclass/api/ManualSendInvoiceEmailRIB_CIC_JMJFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:51:54.187397 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:51:54] "GET /myclass/api/ManualSendInvoiceEmailRIB_CIC_JMJFormation HTTP/1.1" 308 - +INFO:root:2025-10-19 12:51:54.190396 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '\n\n\n\n\t
\n\t\tMysy Training Logo \n\t
\n\t\n\t\t
\n\t\t\t
\n\t\t\t\t\n\t\t\t\t
\n\t\t\t\t\n\t\t\t\t\tJMJ FORMATION
\n\t\t\t\t\t388 Chemin Long Pré
\n\t\t\t\t\t97232 Le Lamentin
\n\t\t\t\t\tMartinique\n\n\t\t\t\t
\n\n\n\t\t\t\t\n\n\t\t\t\t\tFacture n° FACT_20251012\n\n\n\t\t\t\t
\n\n\n\n\t\t\t\t\n\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t
Date de facture: 19/10/2025 Date échéance : 19/10/2025
\n\n\t\t\t\t\n\t\t\t\t\tOrigine : Contrat MTT/2025_N°0012/JMJ FORMATION
\n\t\t\t\t\tPériode : 14/10/2025 au 13/11/2025 \n\t\t\t\t\n\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\n\n\t\t\t\t\t
DescriptionQuantitéPrix unitaire (HT)Montant (HT)
ELYOS - Pack BASIQUE
\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t
1130 €130 €
 
\n\t\t\t\t\n\n\n\t\t\t\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t
    Montant HT: 130   €
    TVA : 11.05   €
    Montant TTC : 141,05   €
\n\n\t\t\n\t\t\t
\n\n\t\t\t\n\t\t\t\tRčglement : Virement bancaire

\n\n\t\t\t

\n\t\t\t\t> Relevé d\'identité bancaire\n\t\t\t

\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\n\n\n\n\t\t\t\t\n\n\t\t\t
BanqueCode agenceNuméro de compteClé RIB
30087338560002128550340
\n\n\t\t\t

\n\t\t\t\t> Identification internationale\n\t\t\t

\n\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\t\t\t\t\t\n\n\n\n\t\t\t\t\n\n\t\t\t
IBANCode BIC
FR76 3008 7338 5600 0212 8550 340CMCIFRPP
\n\n\t\t\n\n\t\n\n\t
\n\t
\n\n\t\n\n\t\tTermes et conditions
\n\n\t\tPas d\'escompte accordé pour paiement anticipé.
\n\t\tEn cas de non-paiement ŕ la date d\'échéance, des pénalités calculées ŕ trois fois le taux d\'intéręt légal\n\t\tseront appliquées.
\n\t\tTout retard de paiement entraînera une indemnité forfaitaire pour frais de recouvrement de 40€.
\n\n\t\n\n\t
\n\t\t\n\n\t\t\t

MySy Training
\n\n\t\t\t\tMySy Training Technology (MTT), société par actions simplifiée au capital de 10 000 euros, dont le sičge\n\t\t\t\tsocial est\n\t\t\t\tsitué 2, place des magnolias, 77680, Roissy en Brie, immatriculée au Registre du Commerce et des\n\t\t\t\tSociétés sous le numéro 917 500 860 R.C.S. Melun\n\t\t\t

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\n\n\n' + dest = <_io.BufferedRandom name='./Invoices/invoice_FACT_20251012.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.files:FileObject './../img/MYSY-LOGO-BLUE.png', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.files:URLParts: ParseResult(scheme='c', netloc='', path='\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__', params='', query='', fragment=''), 'c' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': 75.0, 'height': None, 'align': None, 'id': None} +DEBUG:xhtml2pdf.files:Unrecognized scheme, assuming local file path +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 25 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 6645 +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 25 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 78 6645 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:51:54] "GET /myclass/api/ManualSendInvoiceEmailRIB_CIC_JMJFormation/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:54:49.704823 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:54:49.710824 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:54:49.716824 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:54:49.727341 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:54:49] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:54:49] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:54:49.764343 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:54:49] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:54:49] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:54:49.775338 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:54:49.784344 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:54:50] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:54:51] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:54:53.591514 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:54:53.594531 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:54:53] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:54:53] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:54:58.567556 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:54:58] "POST /myclass/api/Get_Given_Line_Of_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:55:14.534373 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:55:14.537370 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:14] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:55:15.129895 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:55:15.132933 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:15] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:15] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:16] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:55:30.298993 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:55:30.301997 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:30] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:30] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:55:36.298753 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:36] "POST /myclass/api/Create_Invoice_Avoir_Total/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:55:36.371077 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:55:36.374079 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:55:36.379120 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:55:36.385110 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:36] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:36] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:36] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:37] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:55:39.295182 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:55:39.298179 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:39] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:55:39.719350 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:55:39.720343 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:39] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:40] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:55:41] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:56:06.272048 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:56:06.274027 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:56:06] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:56:06] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:56:19.739642 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:56:19] "POST /myclass/api/UpdateStagiairetoClass/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:56:19.957531 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:56:19.960531 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:56:19.963548 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:56:19] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:56:19] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:56:19] "POST /myclass/api/GetTableauEmargement/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:56:37.170916 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
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  Date Facture   19/10/2025 
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Facture n° NEW_Invoice_76 

Destinataire : qsdqs


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Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

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Intitulé de la formation : CONDUIRE UN PROJET  

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DésignationQuantitéPrix unitaire HTTotal HT
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CONDUIRE UN PROJET
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11500.0 €1500.0 €
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 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
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Condition de règlement :    

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Date échéance : 19/10/2025 

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 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
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BIC
QNTOFRP1XXX

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JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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0RhuKMU/Zqs5woAbCtKhprveBAHIQQQGjEdhNo44S15MLyMfCwfVuRhT0EydN0YcEVHPLYVJKLESJ5v9wzEYLueXZKvhVCEXveRMAeA/Px8bN68qU1BVgDgt99O4bffTjcKI9we9Ho9fvzxx1Z5tIvFYjz99LN47733MHLkyGaF+Z1apkwmw7Jly7Fly1b07t2n4TeJxAzLlz+NTz/9PwwdOqxTNd/W1gnRvR3ISSAQYOLESQgPH2ky7ZUrl3DixIk29aOioiJ8++23JpdGxGIxFi5cBDc39/taPjGB3laTBsehl42hx7yCCIVqZbufq+J12F6cgoomBu/nrJ0RZPNfr1pbkRmed/ZtZFrhAeytK8dNRXmXdpRJPTzxumsgHJvMxOtBWFt0E6cr8rrNSe5mXSUO1hSj6RRmtJkVJrn4QMgO2+g0wbNnzx5kZma0fXIoFGLjxg0oLi7uUBkKCgpw8OCPrcrv6aefxcqVK9u8vUwkEmHkyJF4//0P4OPjCx8fH3z++edYvXp1qxy/7mfs7e2xbNkyk+vDHCfA9u2RyM3NbXXf++mnn5CUlGgy7ahRYzB27Nj7vi2YQG8HjhJzND2NRglCja793q+xNSU4r6oxuD7LxbeRQxcHYJyzF3ybrJcrQDhQnNqlWrKQ4/CYey8ssHOHWWMFHtm8DmtzopFW1/UBOAjAn5X5KDTyrk+5+MNFYsE6aSeRkpKCPXu+b7emePNmEg4fPtyhEKDR0dFITU0xmW7WrNl49tlnTWrlzQscDqGhofjiiw2IjPwOERFTWhURjQEMGTIU06fPMNlPUlKSceTIEeh0OpPPzMrKwu7du0xq9NbWNli0aCHs7Ozu+3ZgAr0daHjeqJBpr26qIx67i1INDkSZa2GHUCOmY0exBZ519DTIf0d1IdK7OHqZrVCM1Z4DMNnC1qDzXNCq8K+s6yju4m10PBHO1hSj6fTJSyjBKAcpBGDaeWeg1WoRGbkNJSUd07A3bfoKOTk57fs2dDqcP3/epLm7Rw8nLF68uMN7jzmOQ79+/eDl5cWOVG0DVlZWmDdvvkmnL4FAgG3bvjVp8eF5Hvv27WuVZSgiIgJDhgxljcAEevuo1KjQ1JvVHByshe3bKpGsqMBxheG+2kecvI2e7iXkODzo4gP7JuvZubweJ0symvFT7zxczSzxludABIvMDETnobpybM6L7ZT1/ObIV9chpd7wCMiJFrZwkJizDtpJXL58Gfv37+vwc8rLy7B3795WaWVNKSsra5Uz3oIFCxEQEMga7S4SHByMJ56Yb1KjLikpxt69P7QYEjYxMQH79v1gMs8ePZywcOGidltlWp7cARUV5Q2n8rX3Lz8/v83+J+2Febm3VTsnHtdrDDUWO04At3Zs3eJB+LEkDalNBOAEiSVG9fBo9j5vSzu8aNsTa+/w8tYD2FKehWluAW0Kj9vmjg4g1NYZa2XBWJ59HcX035VzFYDPy7PhZW6LJ9x6dcladrlGiXwjE4Yhtq7d7mnf9rr7e2h9tbW1iIzc1qqDSYjIpDa7Y8d2TJ48GSEhIW0uhylzOxFhxIgRXb7Hl9EyYrEYjzzyCH7++RiysjJbtILs3r0LERERGDBggMHvarUae/bsQVFRocl+9cQTT6BPnz5d861yAqxY8Qr0el2HnuPr64fffjvTJZOObhPoZdp6FKg7LwCDABxczCzuujk1X1WLPYoSg+v9Le1bfVb2nWQpa7C/qgAcGpvspzvIWlwLFnMCTHf1x9rqgkbRJG7yOpwty8Z8Wd8uNu1wmOTkhdWqGrxSnNKo9BVE+KgwCd4Wdhjp0PlepyX1ChQZrJ8T3Mys7+0Rjwjf5NyAo7DzYuALOQGe9Q2DuJPPqb969QqOHv3J4Azxpnh5eWPgwIE4cGAfWtqDXVtbi507d6J3795tOme8oCAfdXUKWFg0P1kOCuqNgIAAJlHvAXx9fTFv3jy8++6/W0ynUNRix47vEBQUBAuLxuNcVFQUDh48aFKYe3v7YPbsR7t0371QKOzwRFEg6L6JZhcJdA4v5dyAGdd5Fd1LZIadfSbAWnj3NLA6vQ4bc2OR3WTGZgbgIXt3CNvxvqfLshHbZLvbEJEEU1x8TdeJTQ88Y9UDXynKGq5pAewsy0SEqx96iLt2RigRCLBQGoQMVQ2+qCls9FuiXot3c6KwwcwS/padG0zB6Bo9ocsOPelEiY73aks69Ym9OQGepsGdOxkvK8WGDRtMCnOe57F8+XIMGzYcly5dQn5+y+cPHD16BFOnTsXo0aNbXZbS0lKIRC1/84GBgd2i/TBaI7wEeOihh/Dzzz8jKup6i2mPHTuGGTNmYNSo//aHuro67N69GzU11Sb3tS9duhRyuZxVeiNFq4vI0muRrFN32l+9TtPla8MtoeJ12J2fgA3VBYZailCM0T082mw7KFTXYUd5lsF9D9r2hIeRuO1NsRCIMMvFz3CSoFHiXHlOt9SLg8gMKzxDEWFuWN5f1XX4JCcaFdrOjX1dayTMKzgOTmLmkdxReJ7HkSNHceWK6UAeffr0xcSJk+Dj49OqEKB1dQps2vQVampa77hZV6cEZ2Ki7OHh2Satn9G1uLm5Y9GiRRCLW7ZE1dersG3bNigU/z2O+dKlizh27IjJvhQWNgSTJ0d0W1S8+16g/69AADJU1ViXcRUri5JhTDStdPGDczuOiv29PBfntapG05QAgRCzXP1bPTkYaN8Ts80ar5frAPxYkoG6Dq79tBZPCxu86dEffY2Ent1eU4xv8+I71UnO+EE4BP09HoTj70BOTg42bfrKpJlQr9fjqaeegouLMziOw4wZM+DhYTqi1p9/XsCxY8da//21ok27y+GI0XrGjh2HkSNNB5v5/fffce7cWQBAVdWtMwBMOU9yHLB8+VPsKNv7UaDzAIo0KmTXK1r9l1VfizhFOc5X5OHTzGuYm3QGaytyUGvEQjDD3AaP9AyAoI3OX1VaNXaUZRg0xmRrZwRZt/54RjuRGR4zYp7fVV+Nq910aAsHYJi9G/4h7Qf3JtqUGsDHZRk4UprZaSfS2TSz7FKkVbEvugPodDrs378fhYUFJtOOGzce48aNx+11czc3NyxfvtykZkVE+Pbbb02a529jbW1lMkpYfn4etFota8B7CDs7Oyxa9KTJbYQajRqRkZEoLS3F6dOncfbsGZP9Z8qUhzBixAhWyUbosgXpXgIRLDrRw9lGKGqXh3AdgBezrqKtxth6AHVEBpHbGmnHIjO84z0Yju1Yu71YVYCjTbZeuXIcHnX1b7Nn+IgeHphQlIyTTQTajyXpGNFDDjHX9fM2DsBUFx8kq6rwUWkmFHfUWwnxeCcvDr4Wtgi1delwXs05lZVq6u/tr43jsKdnENwknXeQjYDjIOoks2NycjK2bv26VWkXL17SaM8xx3GYPDkC33+/2+TRqTdvJuHAgR/xwgsvmJwAODk5Q6fTtWi+TU6+CY1Gw9bR7zHCw8MxderD2Lv3+xbTXblyGXv2fI9ffvnFZH+QSCRYsmQpbGxsWAV3hUAnMrayTXjPMxQDjIRIbb8pgYNlO7wFedw66rOzCROZ4VOvQejbjndU6nU4VGoYMGGUhR0G2bm2+XkuEgtMdZDiZElaYy29rhxP1pQitB3PbA8WAiGelwcjW1WL7YpS3Kkzxes1eCv7Or70D4dnB7fUuZlbw47jUN1I4+eQV19zj39uHAY7yuBzDx65Wl9fj61bv4ZSaXpnyvTpMzB0qGEgjx49euDZZ5/Ds88+Y2Jew+Hbb7di8uTJ8Pf3bzGtVCqFlZUVeL75iXViYgJSUlIwaNAgNqLfS8JFJMKCBQtw8uSvqKgob1GGfPLJxyaXV4h4LFiwEH379u22dxCLxR32cpdIJN0WpKjDAp3jjOvNPcUW8DL/35xFPW5hj1Weoehv69KuTXQxNSXYcodneoNA5ATYXZDUrmdW67V/6cl3bh/j8WNJGvrZukDUTR3KXmSGVV4DkJb6B8422bb4s6oGn+VE49++Q2HdgaNebSSWcOMEqCZ9IwvB77UlWKjXwkbIHKTayqVLl3DkyE8m092KCDbPYKvR7bFg7NhxGDduPE6f/q3F55SXl+O777bj7bf/0eLZ1dbW1vD19W9xLzrHCXDu3DmEhoayvej3GL1798YTTzyBL75Yb1IxNIVM5oE5c+Z0WzheIsLbb/8Dw4cP75gyKhB2m9MmCyzTBgaIzPCUkzcecQtAD3H74oVriceh0nSjv0XWVSCyrqJTy7y9uhCLlFXw7UatMNDSHv/2GIDF6ReR2sQj/bOqAnjlJ+IZWT9I2mkqlptbw0tsgZtqxR02IeB4fS1K1UrYWLKYzm1BoVDgq6++bDFy153aef/+oS0K/KVLl+G33061qJVwHIcdO77DlClTjWr7t3F2dkb//v1NBpfZtWsnHnroIbYf/R5DKBRi9uxHceTIT8jKyurQs5544gn4+3df+xIRpFLZ3yoCIfNybyUPmtvgcNB4LPUIbrcwB26Fed1eXdRt5c7l9ThSkt5pDmmtZZi9G95072PUj2JdSSpOdWBbnaVQhCFGHAdreT1Ol2d3+7v+3fn1119x5cplk+lcXFzxxBPzWtSoAWDgwIGYOXOWyefxPI9vv/3G4AzyRhqHSISRI0eZPNylrKwU33yzFXV1HQ9mFR8fh+zsbNYxOgkPDw8sWbKsQ7sR+vULxrRp05kF5n7X0C0BvO3iB3+L1mlt5eo6PFV00+B6oroOefW1kFm0fxlBT4QjJekope7dZrOlIhdz3YPQ08yq2/IUchxmu/ohS1WNf5VnGUwy3smNgZu5Nbza4SDGAZjg6IGvKnJQcofw5gB8VZqBSc7eXRr69n+JwsJCbN68uRWnoXGYO/cxBAUFmXymhYUF5s+fj6NHfzLpfX78+M945JFZmDRpUosTBF9fvxbDiQLA3r170LNnTzz33PMmJx3NER0djVdfXQWxWIznn38BEydONBnYhmFCaxQIEBERgUOHDuL69WttF1KiW2edy2QyVpn3u4YuBjDWQYZHXP1a9bdI3g9rHQ1jqOcQj49ybqCyA0FS8uoV2FmVj+7eNZuk1+BUaVarwvLwnRi+x1IownMewVhiZahNX9fV44PsKOS3MzxwiK0zhjbZf08AonRqHCpOY3vSW8mBAweQlJTQKi1rzpw5rdaQgoODMW/eApNroxzHYePGDSgvb95pSiaT4eGHp5l0LOJ5Hhs3bsTGjRtRU9M2B0mdToerV6/i7bffQnLyTcTHx2HlyhVYv/5zlJSUsI7SQZydnbFs2fJ23Tt06FBMmDCBnX7HNPS2I+EEWCbrhzO1pTjTZBvYr2oFduTH4wWvgW12XCMQfi5JQ3qTMK/2nADP2Uth2UkhbXkiHKkpwhVt4y1cm8rSMa2nH2xELTuUEBE685w0J7EFVnsNRGrKeZy/o0x6AD8qKyHKiQLa4QZoLRTjmZ6BOJd1DdVNpiAflaShv7UTwnvIOz3yv5Z4XKjIx0A7V9iIJH/rvp6eno6dO78zbRHhOCxevKRNGpJEIsHjjz+OI0d+QllZaYtpY2KicfToUcyfP99o5C+O4/DYY3Px88/HkJaW2uKzNBo11q//DElJiVi+/CmEhIS06JDE8zyqqqqwb98+fPnlRlRW/teHpa5OgfXrP8fVq1fx+utr0K9fPxaZrAOEh4cjImJqqyLB/dfaY4nFixfDwcGBVSAT6O2jp7k1Vkv74M+sa40iwykBbCjLxHB7KQbZ92zTM0s19dhZnm0QaW6JjQve9A2DWScF8CcCfPMTsbAgvtG2sQsaFc6X52KKq1+L9ws5rtM7hb+VA972CMXTmVeRfoeTnB7AHmVVu587zFGGCSVp2K+sbHQ9n3isybmBrebWCLRy6DShXqfXYnNuHD4uTcc8O3e87TMYtn9Toa7T6bBz5w7k5+ebHFz9/PwxderUNmtIgYGBWLhwIT755OMW7yUibNr0FcaNG9dsbG53dymWLFmKN99cY3ItVq/X45dfjuPcuXOYOHEiJk16EL17B8HVtScEAgGICDU1NcjNzcWff17A/v37kZubY/S5PM/jwoU/MG/eE1iz5g1Mnz4dlpaWbJBsB7a2tliwYAHOnTuLujpFq+6ZNGkShg+/O0FkOI5DcXERQsOCJAAAIABJREFU0tPTO+2Zjo6OXTo5YQK9GcY4eeHVqkK8W9U42loqr8enuTHYaOUAhzbEDj9dloUYXVMvYg6PuPrDojOP/OSAsc5e8ClORnITa8Dm4hSMdfKEZYvburrGrDXaUYZVymq8XpSEmjtiF3TEMG4rkuBptyCcybiIiibxEC5qVXg+9QI+8QlDSDu3F95JmUaFT7Ki8FFVPgDCF1V50GcQ3vEJ+1tq6lFRUdi1a6dJIc1xHJ555lk4O7cvpsTMmY/g4MGDyMhoeVDMz8/DgQP78eKLLxoNO8txHKZMmYITJ34xGU3sNiqVEocPH8JPPx2GSCSCubk5HB0doVSqUF1dDb1e1wrfgVtUV1dhzZrVuHEjCq+/vgY9evRgg2Q7GDhwIB599FF8++03Jvuera0dnnxy8V2bQHEch7fffqvTrDJEwLp1n2LGjBldVmZmP2oGM4EQS2T9MExkZiAMvldVYV9BEvhWiqNqnQbfl2agrkn6+Zb27QpMYwpniSWecjDUdH6vr8W1qqK7Up9iToD57kF42l6KztqRyQEY7SjFuy4BBi3BA/hNU4elaX/idFk26tsZS17D3zKxP3vzLD6qymuYgqgBbKjKx/sZV6DQ/73CjtbV1WH79kjU15uOrDd06HA88MAD7V6/lMvlmD9/fqvu3759O5KSkpr93d7eHmvWvIGgoN5tHEgJWq0WtbW1yM7ORmlpCTQadauF+Z3PsbOzYxHpOjKumplhzpy5kEpNL988/vjj6Nev310tr16vh1ar7aQ/TZeXlwn0FvCytMNb0r6wbCLSOQCfl6Yjprp1zjIXK/NxSmPo/DXXxQ82os4POCDkOEzr6Q+XJjPLKgB7SlKh4fV3pT6thCI85xGCKZ14nKqIE2CerA9W2bkZ/f26To0HMi7jzZQ/EFdT2upDYlS8HimKCnyYcRnh6Rew30gUOi2AWr0WujbsWrgX3HouXryIQ4cOtkKA8XjmmWcahXhtDw899DD69DEd3au8vAzffvst1OrmHU979eqFd95ZC0fH7tWQiQhTpz6E55573mR8ckbLBAYG4rHHHmsxjUwmw5w5c9gOg7aOh6wKWma8szeWV+bh/2qK//txA0jkdfg8NxbrrUa3uI5ap9dhb3EalE22V0VIrDDEwb3Lyi23sMXTtm5Y22TJ4KiiDE8ryhHcCTHV21UuM2v8y3MgclP/wDVd5xyrai0UY4XXQBSmXsCuJuvpt1vs05oi7FKUYpalA8LtpRhg6wJzkQSWQjEshUKoeR4qvQ4KrQpRNaU4W5mHSGUl1H8Ja2O2mNfspVjjPRj2RhwNtc0I+cNlWXCuLu6SunU2s8TEHh4tpqmsrMTWrVtb5a0+bdoMDBkypMPlcnFxwZIlS7Fq1QqTWvGxY0cxffqMZk/q4jgOYWFh+PjjT/DWW2+26iCZjsLzPGbOfARvv/02c87qDIVDKMSMGTNx/PhxJCYmGKlvPZYsWQovL29WWUygd7KJSCDEsx79cT7pDK7rG5tMtisrMKrwJhbK+jV7oEpibSl+UFU1ES/AbCdv9BB3nelOzAkw1cXXQKDnEuHHolT064R15fbS16YH3pKH4Jns6yjsJGuBm5kV1vmHQ559Ax9WGT/Jq5jXY6OiDBsVZbAVCOEjEMFdIIQ9J0Qd8SgjPa7rNKhvhcb9vpMPnvfo36yFxeiBOMTjleKULqvX52x7tijQeZ7H8ePH8ccf502uCwoEHBYsWGg0xGt7mDRpEvbsGYzLl1s+Z12pVCIychsGDBjQrCYsEAgwfvx4ODk54R//eBsxMdFd9x2JxViwYCFeeOFFODo6sgGxsyb2cjkWLVqEN95YY3BcamjoQEyd+hALItMOmMm9Ffha2uNVtyDYGjG9v1ucglSF8T20WuKxvzgNyiYCYoTIDOOcPLq83H1snLHU0nAQ+qGmECmKirvY6ThEOHniTRd/dKa7i6uZJV73GYwNroFw4wQtTlhqeD2idWr8rFFit7oWhzV1uKCtNynMewnF2O8xAC97hnbJcklHMDVBKygowMaNG0wKcyLC44/PR0hISKeVzdraGs8993yrYlqfOnUSp06dNDHhEKB///744osvMGPGzC4xzbq5ueOTTz7FqlWvMmHe2X2V4zBx4iQMHhxm0PeWLVsGV1dXVklMoHfdQPlwTz8ssnGBsImmncnr8Un2Daj0OoP7UhQV2FFraF59xEHeLZHMLIUizHU1PM0qidfjRFnmXa1TMSfAPPcgLLbr3GUHO5EET3uEYL/fCCy0doJrJwWjkHECvGjnhoO9xmBmT39YCP9exi29Xo+DB39Ebq7pkLv29g54/PHHOv0QjGHDhv11hjpMTii2bt2K4uJik0LBy8sb7733PjZt2oIRI8I75RAMe3sHLFiwELt27cb06dPZmnkX4ejoiMWLl9zhZEiYNOlBjB49hlUOE+jNDA6d9BwLgQjPe4Sgt5EtX7uUFThUlNIoxhoPwvHSTAOTcm+BCBEu3bc2NNDBDbPMbQ2u76/IRV694q62jZ1IglWe/THF3KZT20zIcRju4I5NvcZgl/cQPGZhD6927vPvLRDhFTs3HPAPx8cB4ehl5Qjub9jX09LSsGXLllvHHbfwx/N6LFiwsM2e5K3BzMwMy5cvh0QiMVmOqKhrOHLkp1adwmVtbY0JEyZg06bN+Oyz9Rg9egxsbe1aHTuciCAUChEY2AvPP/8Cdu/ejbff/gf8/Py6NDrZrfrmm/2jLoh2SIQW8+xIvPX2EB4ejgkTJt5SQCytsGDBQtjZdd/hSqbqorP/upoOqxm2IjMscZA3GkwIBEdx92/tcBSbY4W91EgZO2efsK+VPd6Th+BsVaHBbzGqakzUqhvWxau1GlQQb1AeuZk1/Cy7z7HGXmSGBa4BkFcXNhJEBCBNWQWZuXXjwVEkwQJHeaMDTngQLLtor7WnuQ0+8h6CvnesLXMAHDqh/5gJhBjv5IlwRxmSFRW4VF2IazUlSNHUIUWvRSHPAw2BeDmAE6CPQIheQglk5jYYZ++OXjbO8LO0g6CNA7uVxByrHeXoTr92XwvbZgVHcXExli9/yrTlRCzCjBkzu2z9sn//UHzwwUcoKjK9fVIgEKKurg7W1tamrWgcBzs7Ozz00EOYOHEi0tLSEBcXhytXrqCgIB95eXmoqalGfX09hEIhrKys4eTkDC8vT3h7e2PkyFHw8/ODs7Nzt4QYFYlEeOONNw3Wjxu/vwBOTk6dluft6H3Tpk1r2Rolk3VbRDwrKyssXrwEp0//hhkzZiIsLKzbvpeIiAgMGzasW2VUr169uvT5HBELes24f9AToUSrQq1WDZ1OC9xeM+cE4AQCmIskcJJYsDPV/4dQKBSorKyERqOBXq8Hx3EQiUSwtLSEk5MTc766y+h0Oqxfvx4TJ05A3779WIUwgc5gMBiMvyu1tbWwsrJisfKZQGcwGAwGg8GmQwwGg8FgMIHOYDAYDAaDCXQGg8FgMBhMoDMYDAaDwWACncFgMBgMJtAZDAaDwWAwgc5gMBgMBoMJdAaDwWAwGEygMxgMBoPBBDqDwWAwGAwm0BkMBoPBYDCBzmAwGAwGwxARqwLG/QARQaVSged5mJmZQSxmx6MyGIz2odfrUV9fDwAwNze/Z47gZRo6475AoVDgrbfewpNPLsKNGzdYhTAYjHbB8zyOH/8ZTz/9FNat+6RBsDOBzmB0EzU1Nfj552O4cSMK7u5urELu0DTy8/PATlFmMFpHQkIC/vnPf0Kr1WHRoidhZWXFBDqD0Z2kp6ehvLwUwcEhsLOzZxUCIDn5Jj788ANs3rwZPM+zCmEwTFBYWIj3338PcrkcH3zwATw8PO6p8jGBzrgvyM7OgZmZOQYOHAQLCwtWIQAOHTqEr776EsHBwRAI2FDAYLREfX09Nm3ahIqKCnz00Ufw9va+58rInOIY//Oo1WrEx8eBiODv7w+RqPXdnogamaNNCb4707dXSLYlzzvTcRzX6F6O48BxnNH0CoUCN2/ehFgshlQqa/Tb7XuM/Z+IjD73Tg3f2O/tLa+xe1tTnq56bnO09f1bWz9tKWNb3rm179KZfa8tfd1UPbVU562pY2PlNHWfXq/HrFmzsHjxYsjl8ja9W3dNmJlAZ9wHAr0eUVFRUKvr4ePj0+pBLTs7G9euXcOVK5dRUVEBBwdHjB49GsOGDYOTk5NB+sTEBJw+fQapqSkQiyUYP34cRo8eA2tr61blqdfrkZ6ejtjY2L/yrISjowPGjBmLsLAwgzwBYNu2bSgpKcaMGTNRX3/rPePj4/6yRgzE2LFj4ejo2JD+woULOH/+HOrqlLh27Rp4nsePPx7AmTOnAQDTpk1Dnz59UVZWhm+//QZCoRDLly9HYmISzpw5jczMTDz77HMICQkBEaGkpARxcXE4f/48iouLYW5uhpCQEISHj4S/v7/B4BgVdR3nzp3HgAGh8PHxxcmTJxEdHQ2VSgl/f3+MHj0GgwcPNvAa1ul02LRpE+rqFJg9+1FoNBqcPXsGsbGxePDBB/Hww9MAAEqlEjdu3MCFCxeQnp4OgYBD7959MGrUKPTp08dgMpebm4vvvtsOHx9fPPDAA7h27RpiYmKQn58HPz8/hIeHIySkv9FJIM/zKCwsxLVr1xAVdR25ubmwsrLGyJEjER4eDjc3t0bvX1paiu+++w48r8eyZcthb2+49BMfH4ejR4/C3d0dCxYsBMdxqK6uxtdfbwERYdmyZUhPT8eZM2eQlpaGxYsXY/DgMOTkZOP48V8QHx8HgEN4eDjGjx8PZ2fnVk8i8/Pzcf36NVy9eg1FRUWwtrbCyJGjEBYWBplMZjDBOH78Z0RHR2PKlCmwsbHFpUuXEB8fB41Gg5CQ/hgzZozBfXdOsuPi4nD16lUkJyejqqoK7u5ueOCBCRg8eDB++ukwSkpKMXXqVPj7+zfKNy8vr6GcxcVFsLa2xogR4Rg9ejREIhG+++47mJubY8GCBbC0tGy4T6FQIDo6GteuXUVmZiYUCgV8fHwxadIk9OnTG9u2RYKIx9y5jzX61nQ6HZKTkxEVFYUbN26gsrIC7u5SjB49GuHh4Q153Eaj0SAq6jr++OMPJCenQCp1x/jxD2DYsGFtUibaqw0wGP/TpKSkkFTqRoMHD6KSkhKT6Wtra2nz5s00YEAoeXjIKCDAn0JDQ8jDQ0YeHjKaNGkixcREN6TneZ6OHz9OgYEB5OPjTWFhgykgwI+8vDzotddeo6qqKpN5VldX0+bNm6hfv74kk7lTYGAAhYQEk6ennDw95TR16lSKj49vdE9lZSVNnTqFAgL86MknF1FAgD/5+nqTn58vSaVu5OEho2nTHqasrKyGe/bu3Uu+vt7k5eVJcrmUPD3l5OfnQ35+PhQWNohSUlKIiOj69etkZ2dBU6dG0CuvvEy+vt7k5uZKvXsHUWpqCmm1Wjp16hRNmjSRPDxk5O3tSaGh/cnHx5tkMnfq3TuIjhz5ifR6fUPeOp2O1q1bRzKZO82YMZ3CwgaRXC6lwMAAksulJJO5k5+fL3300UekUCgavWtWVhYNHTqEQkP707/+9U8KCupF7u6u5OhoS6dOnSSe5ykhIYFmz55F3t5e5OXlQSEhwdSrVwDJZO4UEOBPn376KdXX1zd67qFDh8jV1Ynmzp1LU6ZEkIeHjAID/cnDQ0YymTv5+/vS5s2bSaPRNLpPpVLRd99tp8GDB5JcLiUfHy8KDu5L3t636nX06FF06dIl4nm+4Z7Y2Fjy9/elmTNnGDzvNl999SXJZO60cePGhnsTExPJxkZCEydOoFWrVpKfnw+5ublSYKA/xcXF0qVLl2jQoIHk6Smn/v1DqG/fPuThIaO5c+dQbm6uyb6nUqlo//79NHJkOMnlUvL19aaQkGDy8vIguVxKI0YMp7Nnz9Adr0I8z9Py5cvI01NOS5YspuDgfuTl5UEBAX4kk7mTXC6l4cOH0dWrVxvlxfM8ZWdn0TPPPEN+fj4klbqRp6ec/P1v9VkvLw+aO3cOjRgxnAIDAyg7O7tROX/4YS+Fh48guVxKHh5SCgrqRV5eHiSTudPYsWNo7dq15OrqTMuXL6O6ujoiItLr9XT9+jWaNu1h8vLyJKnUjXx8vBry9/f3pWXLlpK3txfNmDGdysvLG/IsLS2ld9/9N/Xu3euv79KfgoP7NXw7Tz75ZKPvS6vV0ubNm8nPz4f8/f0oLGwweXl5kI+PF61bt460Wm2XjnVMoDP+5zlx4gS5u/ekpUuXGAiKptTV1dHatWtJLpdSRMRk+u677yguLo6Sk5PpwoULtHz5cpJK3WjatGlUXV1NREQVFRU0YEAo9evXh44fP07p6el05cqVv4TWYDp79qxJYf7GG2+QTOZO06Y9TPv376f4+Hi6efMm/f777zR//nySy6U0f/78hjyJiJKSkqhv3z4kl0tp/PhxFBkZSbGxMRQbG0s//LCXhg8fTnK5lNaseZ10Oh0RERUUFFBcXBz961//JKnUjd566y2Kjo6m2NhYSkhIaBB427dHkru7K8lk7jRp0gT6+ustdOnSJfr999+pqqqKDh48SAEB/jRo0EBat24d3bgRRSkpKXTjxg368MMPycvLgwIDAygmJqahvBqNhubMeZTkcin5+/vRq6+uovPnz1N8fBxFRUXRhx9+QIGBAeTu3pP27t3bqI4uX75M3t5eJJdLaejQIfTRRx/RH3/8QefPn6eysjKKioqioUOHkJ+fD61cuZIuXvyTbt68SfHx8bR161YaNGggeXl50KFDBxsJ2c8//4xkMnfy8JDRiy++QOfOnaP4+Di6evUqrVmzhmQyd/L0lNOVK5cb7qmvr6fPP/+M5HIpjRo1krZs2ULR0dGUlJRE165doxUrVpCnpwc98MD4RgJ1z5495O7ek955551GE507hd0rr7xM3t6edP78uYbr+/fvJze3W20xbtxY+vLLL+nixYv0+++/U35+Ps2dO4fkcint2rWTkpOTKTExkZYvX0aBgf4UGRnZYt+rr6+nDRs2kIeHjEaODKetW7dSTEw03bx5k6KiomjNmtcb8s3JyWm4r6qqiiZOnEByuZQGDhxAX3zxBV2/fp3i4uLoxIkTNHPmDJLLpTRz5kyqrKxsuC8jI4MiIiaTVOpGDz44iXbu3EFXr16luLhYOnfuHL344ovk4SEjuVxKY8eOaeiPKpWKPv10HXl4yCgoKJA++OB9unTpEiUkJFBUVBT93/99SgMG9G+YGH711ZcN7Xzp0kUKDu5HMpk7zZ8/nw4ePEg3btyg2NhYOnr0KD3++OMkl0tJLpfS6tWvNdxXUlJCS5YsJrlcSnPmPEpHjhyhuLg4unnzJp09e5Zmz55FMpk7rVy5oqGcSUlJJJO508SJE+j8+XOUnp5OJ0/+SoMHD6LRo0dRXFwcE+gMRnvR6/W0adMmkkrd6LPPPjOZft++fSSVutHcuXMazbxvU15eTk888QTJ5TLat+8H4nmeoqOjycNDRkuWLG40YUhMTKSUlBSjg/edg/jWrV+TXC6lBQsWNNJI7tQSpk6dQq6uznT48OGG67/+emuiMnPmDKMDxZEjP5G/vy8NGjSQioqK7tCUtfTPf/6DpFI3OnbsmMF9Op2O1q5dSzKZOz399NONBvJbmmYM9enTm8LCBtOZM6cbCcjbgvv9998jqdSNVq9e3TDYFRcXN2iQBw4cIJVKZZDv999/Tx4eMho/fhwVFBQ0/BYZuY1kMnd68MFJFB0d3ahOi4qKaPr0aeTj4027d+8itVpt8E6HDx8iLy8PmjDhgQaLiUKhoMWLnyR3d1fauHGjwWSvtraWli9fRjKZO23Zspl4niee5+nEiRPk4+NNERGTKS4u1iAvhUJBS5cuJXf3nrR58+aG6++88w7J5VI6evSo0b5QUlJCERGTqU+foAZLiV6vp//7v09JJnOnJUsWU0ZGRqP6rq6uosBAf+rXr2+jNs7JyaHo6OhmLQG3+95vv51qsDrduHHD6Lu89tqrJJW60fr16xuux8fHU9++fWjo0CF04cIFgz4QFxdHAwcOoMBAf4qKiiIiIqVSSS+//BLJZO60evVrlJeXZ5CfWq2mf/97LUmlbvTmm2+QVqslnufp119/JblcSmFhg+nkyZMGbazX6+nChQvUr18fcnbuQadOnSIiosLCwgYr0rp164xayyoqKmju3Lnk7t6Tdu3a1fC8zz//jKRSN3r55ZeorKzM4L60tDQaM2Y0+fh40aVLF4mI6JdffiGp1I0++OD9RmmjoqIoOzu7xbGgM2CurYz/aTQaDa5duwatVgMfn5a9UsvLyxEZuQ1isRivvLICnp6eBmkcHR0xZsxo6PU6XL58BTzPw83NDebm5rh+/ToSExMbnHWCgoLg7+/fokNMSUkJvvnmGzg5OWPVqlVGt8E4OTnh0UfnQCKRICYmuuF6TEwMBAIBHn/8CfTt29fgvgEDBkIu94BSqURZWWnD9ZqaWsTExAAgo/nV1NTg2rWr4Hkezz//fCMHII1Gg71796KiohzPP/88xowZa7BGKhaLMXPmI6isLMTJkyegVqsBAJmZGaipqUZYWBgiIiJgbm7e6D6hUIjJkyfDy8sbMTE3EBsb2/BbQkICRCIRXnzxJYSEhDSq099+O4XLly/h0UcfxSOPzIJEIjF4p2HDhsPfPxD5+fnIyclpWG+/dOkSevRwwpw5cwz2E1tbW2P06NHgeR75+QXQ6XRQq9XYuvVrCAQCrFy5En379jPIy8rKCrNnz4JIJEJ8fDx0Oh1qamqQnJwMsVjSbByEysoKZGVlQSaTQyaTNZTx4sWL0Om0ePHFl+Dt7d2ovgUCIfz8/FFSUoQLFy5Ar9cDAORyOUJCQlqMiKjRaPDNN9+A4zi88sor6N+/v9F3mThx4l/r+/FQKusAAEVFRaiursIDD9xaG27aB273faVS2RB4JS4uDt9/vwseHp545plnIZVKDfKTSCSwt3cAAISGhkIkEkGpVGLjxg3geR4vvPAixo0bZ9DGAoEAffv2hYODI4RCAXr16gUA+OOP35GUlIixY8dh2bJlsLOzM8jT1tYWYrHoL3+L3gCArKwsREZGwsnJGa+88gp69OhhcJ+vry9GjBgBrVaLxMREEBEcHBzA8zx+//13pKenN6QNDQ2Fh4dHlzvHMac4xv809fX1iI6+AUtLKwQG9moxbVpaGi5e/AO2tvbYt28fjh49YiQVh5SUZAgEAiQl3RLePXr0wBtvvIk1a1bj2WefwcqVKxERMQW2trYmy5eSkoKiokJYWVljx47vDITcbW4PDpWVldDptOB5QlpaOiQSCWQyqdF7BAIOAoEAYrEYFhaWjQR2XFwsvL19jXrr1tTUIDY2Br169WoQLLdRKGpx7NgxCAQCnDt3HqmpaQAMg9IoFHXo0UOK/Pw8qFQq2NraIj+/ABqNBoGBgc2+p6WlJYYPH4HU1BQUFRUBAKqqqpCamgorKyuDSZZWq8Xly5chEomQkZGJd9/9d7NOjqWlJairq0NtbS0AIDs7G0VFBZg5cxYsLMybqUMhAIKdnR2EQiGSk5ORmJgIgHDo0CGcPXvW6H35+fkgArKzs1BdXYW6OiVSUpLh4OBgdKJ4u40rK8sxZcqUBoGlUCgQExONwMAgowLQysoKzz33PJYvX4o1a15HYWEh5syZY9SBsinZ2dmIiooCxwlw7NjPuHDhgtF0hYVF4DgOCQnxqKtTwtLSCteuXf1r0jjAqNPb7X4nkUgaHMESExMgEokxe/ajzXqJ19fXIzk5GRzHoWdPt7/uS0RsbCyGDBmKyZMnNysUq6urkZSUgCFDhjU4qp05cwYcx2HWrNmwsbExel9tbS3S0tIgk3k0OCpevXoVhYX5cHR0wqZNmyESCY2OBdeuXWuYrBARgoODsXjxEkRGbsOzzz6Dl156GePHj4eZmRnzcmcwOkpWVhZKSorRu3cfODg4tJg2MzMTZmYW4HkeBw7sazGtWCyBg4MjOI6DUCjEo4/OgUgkxtq1/8Krr67CyZMn8eqrryEwMLDFrTfZ2dl/aXDV2L+/5TxFIjGEQhE4ToCysmKkpqbA2toGMpnxwbGmphbFxUVwcXGFi4tLo0lETU01pk59yOhAk5KSAqWyDqGhAwwEb1lZOYqKCiAWS3D69KlWlFcIiUQCnudx+fIl8DyPgQMHmahbMQBqGETLysqQnZ0NV9eeBoKgqqqqYbJz5colXL16uYUnc5BIxBAIuIb2Njc3R0hI/0YTnjs9qktKigFwkEqlEAgEqK6uhkJRC57nm5nw3fn+IlhaWkIkEiMvLxd5eTl48MEIWFsbFyxJSTchFIowaNDgBqGVnp6G6upqTJr0oIE3NXBri9WECROwefPXWLv2Hbz//rs4ffo3vPzyywgPH9li3ysqKkJ9veqOdyET/d0BQqEQGo0GyckpsLCwaHZyUlFRgaKiYtjZ2cPBwQFarRZZWdkg4iGTyZoVyrW1tUhMTICFhQV8fX0bnqXTaREQEGBUU75NamoKRCIxBg0aBFtbW9TV1aGwsBDm5uaQSt2bvS8nJwdVVZUYNGgw3NxuTSIKCvIhEolRV6fAnj27Tfbz2+UyNzfHypWrYG/vgA0b1uOpp5ZizpzHsGLFSri7uzOBzmB0hIKCAhAR+vbta3SbUFNtj+M4PPLIbKxevdqo6bbpYHp7e5WFhQUee+wx9O/fH5s3b8Lhw4eRkZGBr7/+Gn5+/kbvJyKo1bfMkXPnPo7XX3/d5PsIhUIIhUKUl5cjIyMdAwcOanZrUmZmJioqyjF27LhGwXSysrIgFkswYMAAo5pydnY2RCIxgoODDQS+Wq0Gx3Hw8/PH119/DUfHHibLbGdnB51Oh5iYGNjbO7Q4sOl0OkRH34BOp4OX160lkry8PJSWFhvdIqTT6aBQKCASibB79x7RMj2ZAAAgAElEQVQEBgaaLI+lpeVfZtIE6PV6eHp6GhV8t7bARUOjUTcsTRAReJ7HkCFD8dVXm0weysFxHGxsbJCamgqOEyAkpL/Re9RqNdLT0yEUiuDhIW8oT25uLgQCAYKCgpoNiCQSiTB58mQEBwdj584d+Prrr/HKK69g/fr1GD58RLNl43k9iAijRo3BJ5980qr+bmNjg8LCQqSnp8HOzh5ubsbbsqSkBEVFhfD29oZUKgXHcTAzk0AgEECpVDabR0VFBWJibmDSpMkN5vHb6VuqayJCTk4OOI6DTCaDUCiEWCyGWCyGVquFRqNp9t68vDzU1dXB09Ozob/rdDoAwGuvrcbcuY+Z3FMvFAobJikODg54+eWXMXJkOD799FP88MNeZGVlYdOmza2ynDCBzmAYQa/XIyYmBjyvh6+vr8kT1pycekCn0+LmzSRYWlo2a6IzZs4VCAQQCATo06cPPvjgQ7i5uePLLzdg586d+Mc//mlUI+E4Dl5et9ZECwryYWVlZXJQvU1iYiLU6noMGDCw2YEuMTEBHCdopPHV19cjISEBOp3WqHalUqkQExMNrVYDDw/D393d3WFuboEbN66CCCatHv8dNHNRUlICb29v9OzZs9lB+fz584iLi8WQIcMQEBAAAEhIiAeARu9x52TB398fmZkZqKysbHV5FAoFrl+/DqFQ2OwkQKVSITo6Cu7u0ob4BRYWFjA3t0BVVRV0Ol2rB+jy8goIhUK4uroaba+KinLEx8fBwsK8YQKo0Whw48attvD29mm2790OjiKTyfDqq6/By8sbb7zxOj7//HMMHhzWbL93dHSERCJBamoKVCoVXF1dW/ku5cjPz0NY2JBmJ5O3td6goAiYmZlBIBDAyckZt5asUqBWq41ah65fvw6JRIJRo0Y1TDatrKz++kYKoFQqjVoqNBoNrl+/jvp6JYKCggDcWo93de0JrVaLmJhYhIUNMRDMer0eFy/+CZ7nERIS0nDd1tYORDxSU9NgZWXVapO5Xq+HUCiESCRCWNgQfPnlV1izZg1OnDiOkyd/xWOPPd6lYx5zimP8Twv0qKjr0Ot5BAX1Npk+NHQA/P0DER8fh2PHjkGr1RrVAhQKRcP/4+Li8OWXG1FSUtKQzsrKChERERCLxUhISGjx4JOAgAC4urri/PlzOHHiF4OY6jzPIyMjw+BEp4yMW9pcSEiIUQGh0WiQnp4OkUgEX1+fhoFMrVYjLS0VSmV1wxplU00xKioKjo494OfnZ/C7lZUVpk+fCWtrO2zfHom6ujqjA35hYWET824xqqqqoFarUVtba1AnPM8jOTkZ//nPR9Dr9Zg79zE4OztDp9MhLS0NIpEYvr6+BgOypaUlBg0aDL1ej02bvmpYd2/6Tv/f3nlHx1Vdi/u700fSqGtmNCpWteUqG4MLmGLAlICBAAmmOLz3CxB4TkKABMgjgVASQhJqEpKQAAYC5NEMxhhsggFj44JxxUWWbFnF6l1TNeX8/pA8nhmNpJFkmZLzrWXW4mpuOefuu/c5++y9T2VlZdg929ra+OKLnUyePDWs8E4ohw5VcvhwLdOnzwgakaKiIoqKiigr28fy5cuDM7lIV3ZbW1vYseTkZAIBP9u3b+83W/T5fGzYsJEDB8qZPHlKsBCR1+tl69bPSU5OCSuuckT2amtrWbp0aVjwlVqtZt68U8jMtNHQ0NjvOUIpLh7PxImTqK2t4a233ooq7w0NDTQ3N/cbKLpcLkpLpw/oOj8S0Dhz5szgOzvttNNIT8/gueeeZfny5djtdrxeL16vF7vdzooVK3j00UdQFBVW69GiPIWFhZhMibz//mo2bdrYT3b8fj979uzhk08+ITc3n3Hj8oJ/u+SSSzAa43j66adZu3Ytbrc7OGNvbW3l6aef5rXXXiM+PoHi4vHB8+bOnYtWq2P58rf4/PPPo8prZWVl8LvsHRhs4E9/+mPQo6AoCmlpaVxxxRUEAoHgOvtYov7Vr371K6n6Jd9EmpubeeKJxxGi16DX19ezf//+fv8qKyuxWq2kpKRgMBh4//3VrF+/Ho1Gg9VqRQhBe3s7q1at4qc/vZWGhkbmzJmDoij85S9/6fuIXZSUlKAoCnZ7NytXvsvatR9z8cWXMG/evAFddiaTCY1Gy4cfrmHdunXExcWTkpIMKLS2trJixdvcdtutOBxOZs6ciUajoauri6VLn6WmppobbrghqmFubm7iqaeewu128z//syTovuzp6WHZsmW0tbWTk5NLTk4OXq8XlUqFRqOhoqKcJ554jFmzZnP55d/p55LXaDRkZGTw7rsr2bRpIx0dHVgsFnQ6HXa7nc8++4z777+fd99dybx584JejhUr3mbt2o9pbW1h48aNpKSkBF3xTU1NrFy5kjvu+BlVVYdYtOhKrr/+BvR6PS0tLfzjH3/H4XCyZMmSqFHKNpuNLVs+Z/v2bZSVlWGxWDGZTLjdbsrLy/nLX/7Cvffew+zZs4Pu/m3btvHqq69y1llnc/7534pawWvDho2sXr2KSy+9jNNOO63PbawnNTWFlStXsnHjRnw+LxaLBZVKRUdHO2vXfsIdd9zOvn1lzJ07Nziz8/v9vPnmm+zZs4eMjAxsNhter5eamhpeeulFHnjgPoQQLFp0FXPnzkWlUlFVVcUTTzzOpEmTueKKRf1mpsuXL+fuu39BRUUFU6dORa/X43K5WLNmDStXvsO0adP47nevGNCDo9FoSE1NY+XKd9i0aSM+n5+0tDQ0Gg12u51169bxs5/9lN27dzN79uzg/V9//XV27tzBdddd32+gcUTGXnzxn9TUVHPdddcHg/nS0tKIi4sLDl7Xrv2YgwcrWbv2Yx555BGeeeYfOBwOfD4fixcvJi8vL+iFAVi/fh2bNm0mOTmF1NRU/H4/9fX1LFu2jDvvvJ329nZmzpzJpZdeGpTbnJwcGhsbWL/+E95+ezmbN29m7959rF69igcffJAVK5bj9XpJTk7myiuvDK6FJycn43K52bRpAx999BFJSckkJycjhKClpYVXX32FW265GZMpkcmTJ+P1evnFL+7iX/96ORi8KYSgra2NpUuXsm/fXhYvXhw1K+KYIjOVJd9UNm3aKAoK8oPFJrKyMqP+u+qqq4I5yE6nUzzyyCMiJydLZGVlivT0ZFFcXCiyszOFzWYRJSXjxQMP3C+6u7uEEEJUVlaKSy65WNhsVlFQkCcWLrxQzJx5gsjMtIhFi64Iy6UeCLvdLv7whz8IiyVdZGVlitTURDF58kRhs1mEzWYVEyaMF48++kiwqExNTY2YMWO6OPHEE8KKdoSye/dukZeXKy66aGFYdbSenh7xxz8+IdLTk0VWVqZITo4XBQV5Yt++vUIIIVasWCFsNqt44IEHgsVoouWpr169Wpx44kyRlZUpzOY0UVRUIAoK8kRmpllYrRliyZIl4sCBA8F85yVL/kfk548T119/XV+VN6swm9PE1KmTRVaWta+dxeKhh34rWluP5vyWlZWJoqKCfu2I5LPPPhPnnLNA2GxWYbWaxbhxOWLSpBJhtWYIq9UsLrpoodi8eXMwl3zp0qV9eeJ/jXo9r9crHnjgfmGxZIg33ni939+WLl0qiot7q6KlpSWFvC+LyMnJErfffrtoa2sLy69+8MEHgzKXmWkREydOEOnpyeKUU04Wp512qsjOtonly5cHz1mz5oN+hYEi86d/8IMbhMWSIQoLC8S5554jzj77LJGdnSlOO+3UqHnl/d+lVzz//POipGSCyM7OFBkZKaKwME+MG5ctMjMtIjc3W/zv//5cNDU1BgvKLFq0SBQW5ocVDQrl8OHDYv78M8QJJ8zoJ/9ut1u8/PLL4sILLxAmk0YkJRlFamqiuOCCb4lnnnlaXHHFFcJqNQfzyENrMdx0043CbE4TWVmZwmrNEMXFhcJqzRCFhfli/vwzRHa2Tdx33339nqe9vV089tijYs6cWcJgQCQm6oXVmiEWL75GPPvss2L27FmitHSq2Lt3T7/z7rrrLmGzWfp0QUrfPc3CZrOKqVOniL///amgXO7atUvMn3+GsNksYtq0KeLiiy8SU6dOFhZLurj55h+HFYUaKxQh5EbIkm8m69Z9wqpVq4b8XXHxeL73ve+FzTB27drJO++sZNeuXTgcdtLTM5gxYwbz589n0qRJYWvdjY0NLFv2Jp99tpnGxkZyc3OZOnUql112GWZzbOuSHo+HTZs28emn69m5cyddXV1YLFamTJnCggULKCkpCc4i9+3bxz//+QJms5kbb7wp6rr7+vXreffdlRQXF3PNNYvDZmkOh4M1a9awd+9euru7MRj0/PSnP0Or1bJixdts3ryZ008/gwULFgwaN1BeXs4HH3zA1q2fU1dXh8lkYtKkyZx88snMnTs36Dru6OjgyisX0djYwEsv/Qu3283q1b3uU4/Hg9lsYcqUyZx++hmUlpaGzZY3b97E8uXLo7Yjmqv7/fffZ8uWLRw4UIFOp6O4uJhZs2Zz6qmnBiP9fT4fzz23lMrKSi666GJmzZrV71put5snn/wzbW1t/Nd//Xe/5Qe/38+uXbv6atFvo7m5GYvFQmlpKXPnnsyMGTP6zagdDgerV6/mo48+orx8P3l5+UyZMpkFC85h3brevOVrr72W4uLxCCFYvXoVn3yyjtmzZ7Nw4cKobW5vb+f999/nk0/WUlVVRWpqKtOnT2fhwoUUFhbFHAPS25bVbNu2nfb2NjIyzEyePIl5805lxowZwYC8xsZGnnqqd7vdW265NWpqZnV1Nf/4x99JTEziJz/5SVTvR0tLC21tbXR0dJCcnExqagpqtYZFi65g69YtvPvuKk488aSwczo7O1mz5gM+/PDDvowUPVOmTOGcc86ltbWVjz/+iPPOO5+zzz47anzGkaUQt9tNUlISGRkZNDc3s2jRFWg0Gv71r38FAzFD4yjWrl3L2rUfU1ZWht3eTVFRMVOmTOWMM86gqKgo2D4hBFVVVbz99tts3LiBjo4OiouL++rqnx1zfMdokAZd8o0lEPATCAwt3qHR6pFrmw6Hg0AggEajISEhYdAdnBwOB16vF71ej8FgGFERiUAggN1ux+/3o9VqgwFBkb85Egw1kIGL5TdH2njE/XrEUAkhgkF+seBwOOjp6UGtVhEXF99Pge/evZvvfvc75Obm8vrrbxAXF4cQgq6uLgKBwIDtHE47Io2xy+VCURTi4uKiDniOtFOtVg/4To/0zWC/EULQ3d2Nz+dDq9UOKiNHn8+Fy+VGr9cHjX60ew3nXbhcLjweDxqNmvj4hBHttBYICOz2bvx+PxqNhvj4+H73FUIEi9cMtNGIEAH8/gCKAmp17HHXW7du5bLLvk1qahrvvbdqwIC73ra6URQV8fG98ubz+YLPHaucALz11pvcfPOPOfHEk3jhhX8OmEng9XpxOp34/X6MRuOgWzAfkYkjvx2o5sJYIKPcJd9YVCo1oynMpNFooq7ZDjQoiHVXtcGfWTVkQZpYFHysBjlSKQ9HGR4hPj6+X5W1UOrr6+ns7OCEEy4JKkJFUWLq2+EMLI5gMBiGVKKxtDOWnbEURYmpgFD48/VGyg91r+G8i6GMTGyyN3RbFEUZsl8URYVGo+o3OFqx4m3Gjx9PScnEsHcaCATYt28fDz30W7xeL1deedWgs9lobdVoNP2ey+Vy8corr7BgwYJ+O9/19PTw2Wef8cgjvUF4V1yxaND+02q1w9IFw5UJadAlEsnXgq1bt6JSqZg9e86I9+iWfL3ZsuUz7rjjdpKTU5gzZw4nnTSLrKwsmpub2bZtG//+9/s0NjYwf/6ZLF68+JhsM/r228u59957eOGF55k9ezYzZ55IcnIyhw/XsmPHDt577z26u7u48sqruPDCC78R/Sxd7hKJZMzw+bzcdNNNfPTRh7z44stR16sl33x6C6v8lQ8++DdNTY1h6VuKopCRYWbhwoX84Ac3DlinYLhs376dJ598kg0bPqWzs6Of98NqzeTaa6/l6quvibnmhDToEonkPxaPx8POnTvx+31MnDgpZrel5JtHbx2HKqqqqqioqKCrq4vExCRycnIoLCxg3Li8ES35DIbf72ffvn3U1dVRWXkQp9NJamoa+fn55OfnYbNljfmGKdKgSyQSiUQiGRayUpxEIpFIJNKgSyQSiUQikQZdIpFIJBKJNOgSiUQikUikQZdIJBKJRBp0iUQikUgk0qBLJBKJRCKRBl0ikUgkEok06BKJRCKRSIMukUgkEolEGnSJRCKRSCTSoEskEolEIpEGXSKRSCQSadAlEolEIpFIgy6RSCQSiUQadIlEIpFIJNKgSyQSiUQiDbpEIpFIJBJp0CUSiUQikUiDLpFIJBKJRBp0iUQikUikQZdIJBKJRCINukQikUgkEmnQJRKJRCKRxILmy7hpm9dNvdtOmauT9h4XakVFgkZHviGBHEMiaTojakUZ8PweEeCgowN/wD/iZzCqteTHJxN6l2aPk6YeB4ijxyxGE+laQ/j9AwEqHO0IEeg9oECy1kiWIWHI+3oCfiocbWH3MGn15BgTw57FKwIcOAZtLIhPHvX7qnJ2Yvf1jPh8tUpFQVwKOpUKd8BPpaOdgBCjeyhFwWYwkaLVD/vUCkc7Hr8v7FiKzogthvd39N204w8Ewo5nGk2kRsjKUKzvqKelxxX8/3i1ltNSbOhU6pivYfd7aXLbEQzepwoKBkWFolKhV2tJ0RpQhrh2t6+HGlcX4sj7UsCsTyBDZxzyubp8PdQ4O8OOZejjMevjwo45/F6qnJ1H7zECUnVGMmN8f6G0e93UubpHJYpDvfcuXw/17m72Ojtp97oAhQSNFqsujmJjEsk6I4ZhvO8j19vlaKfb50EA8Wod4/Tx5Mclkaw1oB/G9SJlu9njpMzRTr3XSXePGxAYNXqy9fFMjE8lZRjPKxAccHbi8XmDxwxqDflxyagUZcjzy+xt+EJ0oFalpiA+BU3IuQI45OzEGaKjdGoNBXHJg9qRI5Q72ukJ0Qeh+ir0HrWubrq87pHPnhWF/PiUYb3rr7RB7/B5WNVUydKWA7zncfR1kxLSZTBZY+DyhHS+lVFAaaI5qmB2+Xq48cCnfOxxjPhZfmay8OuS09AqR1/axvZaLqreFva75fmzWJiRH6HkPEwuXwshAnS2IZG/j59HnsE05GBmyr4PIUR5/S51HLcWzg4TPoffy7UHN7DZbR9xG28zZfCHiWeO+r39q3YXd3YcHvH5J+niWDHpbMw6Iy1eN1eXf8K2UQwQjhj0jwtP5rTU7GGf+vChLfzV0RZ27Lvxqfx5/Kn9Bm/R+LjtMN+u3IQ9bLAlWJ0/hwUZeTE/R1OPizsqN7PeF6IkFBV7dWdSYkqLfcDl7uaEPR/QIwIx9Bug0rBAa+A0YwoXmAuZZEof0AAccnUxrewjCGnrdYkWfls4l7QhBlPVjnamln0Y8o3DmznTuThzQvjv3HbO2v8xDRGDrOHwkqWEK8eVDvu8HV3NzD+wPuwZh4fg3wUnc1Z6br+/OAM+3m06yKvNlfyfuzOqvrOptVwel8q5abmckpJNkkY34J2cAR//bj7ES80H+D9X5PV6rzlOrePbcSlclJ7HzGQbiYNcL3KStLOriTcaK1juaGG3zxOlTwRZah2XGlO41FzASck24tWDmxCfENxTtZWXupuDx2xqLf8qmMO8FNugvS6E4NaDG1npPjrgutSYxNMTzyQ5pF1+EeCx2p080VEXPJaoUvNG/mzOTMsZ/B4I7jy0hTdC9EGovgp9ltcayri1+cCIZXSqWsuKKeeSq48fMxt73FzujR4nP9u/nkWHd7IqaIiVCE2jsNvn4d6Ow8yuWMed+9dx0NkRdd6hQQFl5P9USnRdp4743UDCkBnxu7Webh6v3o7d7x2yLywRz64MMIrUjbaNI1ZS/ftlNM+hVY5tu1AUElFG3DpVlPu/4mxnWWMF/iFmiY09Lp6s34NdBPpdQxnmA33aVstWX09Q9kEBIXi7qYIAYlhvyBpr36FAwM/7Hge/7KjlhPK1/PrAJtoGmXlYIq7xj+5mXqzfizeWAUSkrEd5a4oCCaOUCWWEwqAoo5fFaPe2+708cnALl9fs4BV354D6rs7v44nuJi44tIUb9q5hU3tdVO9Vt9/LHyo/5+KabX3GnCgGV6HK7+Wx7ibOrNzMkn0fsamjnqHe0mG3nfvKNzCvfB0PdtaxOyiT/d/lYb+XP9qbmH9wE7eWfcyhCA9MVA9dxLutC/h4oGYH1e7uGM4N1z3qAZ4sUm93iQC/qt1BubNj2PfQKmOjB3WKMuZ29rgYdJffxy8PbuQfjpaQselQA1/BY92NPFi1FV8gwFedHuC5rgaer9+Hb7TuZMmXwh+bKyhztA8623i1YT/LXJ2j/yYCPl5pOYgrytfwUlcDDW7H8Wm0ENzfUcsvDmykO4bBaK9iEzzUfIBVLdXDHHj8Z+AXgmdqdvHL9uq+OWBsvOLu4mfVW2mJGFx5RYB/1OzinrYqlGHoln+6OvhV9TZaQ5Z0Itlnb+XG/Z/w687DeMRw9KzgKUcr3y/7mE/b64bdR6s93fyhajudo/XUDcK6Hie/q9pGm9fzHyN7x8XlvqWznlfsrWHHUlCYqDMyXp+ARwSodHdT5e+hPkRg81QafpQ9Fa1q6HGHCchV1DHP2kJd7ceKdiF4vLGciXEpzB+BK7j/JxNOPJD3JbcRwACMU1RoY3ySBEUdZg40itLr4Yg0mEBzhMJKRCFeiT7qP9bj3V2+Hv5yeDe/LZob1ZW4x97Kn1oOHpN77ehsYrW7K+rftvs8rG2rYZFt4oivn4FCiqKEyVAAcCFoFYJIFfdidxPnNFVySeb4mOSyLuDn14d3UhSXREl8yjGXsQwULMOQX/UxnP3kKwrxMc51vIh+krjP2c4TrZURcgwTtQZKDIkIBBVuOzU+D/UiwJGFhnhF4ee2yf3iE6qdXfym9SBKiE6IB6ZqjRTpE9ApCgc8Diq9LupFAG/Qi6hiibVkwHiHSlcXPzqwkX/3OPrNRFMVhSxFTZrWQADo9Lmp8vtpj9BKa3xueg59xpOaU5hqSh9WP7/Q3cDEhjJuzJoS03r6SPg/ewsT6vbwk9zSMdGH4xVVr8cxBhIVFWM9Rx9zg+4XgnfbaukKEwSF32dO5LuZEzD2rd35RIBd3a280VjOsq4GykSAe6wTmJyQHlWh+CIE6xR9PC9MPCvmDtMoqjF5wfsDPu6v2UGWIYHxcaMISBP92zhdH8/rJfPRxPjcY2XQC1VqXi6Zj00XF5sbSFFIUveueaVp9fyx6JSobsVDjjYur90e5lT7QUo2V5iLovq/hopXGAl/627grNZqLjbnhynqbr+XPx/eTVmMs9ihvom3WippHWS29XxLJReYCzHFuAYayZJEK/89bkaYBPlEgE6fh71dTfypqYKNvqNmvQt4sbmC8y2FMQdUbfS6ub9qK48Wn4xZaxy5qAuIDP1cnJTJXfknxTy/PZaBRo/ZpnLKMGIh4tXaMN30RWcjNRHBrHek5bEkd3pwoOgXgjJnOx+2VLO0rYrtfi+3JGczPy23nw7b2FFHS4iX0gjck57PDTmlwesFhKDc2cGqlkO83FbNLr+X7yfbOHeAdjj8Xu6t3MxHEcY8BYXz4pK5MXMi0xIzetsmeoN5N3fW81JjBW84Wgj1Y63zebiv8jP+OnE+acMICu0Ugocb91NkTGbBEGvdI8WO4I/NByiJS+bCjLwRTwNEn4cu8ujL409jnDExRs+WMmiMxNfCoAsEO93dYZ9lqdbAxZZCTCEfgkZRcVKShZmJZq60t7C6tZpLLcVRR94KfWvoocYLhTSNfsD16OPJhz0OnqjezgNFc0nW6Ed2EYV+M2ANKlI1hpg8FmOJGkjR6If18QaVkUrNjAFG8vFRXH65ujhmJmYct7Z5heCR+j2ckGgmty9qOoDgvZZDPNXdeEzucdDZwf91Hb2WHphvMPFeyJriux4727qaRhT0B5Cq1gafP5ITTBlMNZm5qvwTdgeOBqJt97qpdHUNa8b9qqOVaYf38JPc6SOOrFaUvnXMCDlJ1eq/FPlOVGtGJNtHNN4OVydhjmRFxTXWCWHKXKPAtIR0piakc7G5kFcaK7g6c0LUgUlnRPBvhqJwiWV8uHFQYHJCGpMS0rjUXMRz9Xu5LnvKgIP6T9pqecfZHvQOKIAJhfstxfx31hTiQj1UCmhVKs5My2VOso0ZNTu5r/kATSFa/TVPN5c1Vw7bq1QZ8PNg7Q4KjYkUxiWNyfusFQF+fXgXecZEpiakjVQdh0XWHyFZoxuFrBx7jotlcIvw0epOfw9Ob8+As7lppgx+mjcz5gjNryJ/7m5mad1eer4G6/+SCGXX4+Sl+n3Bd1fl6ubh+n3HaIALq1qrqAwxpFPVWn6bdxKlEYO/V5sq6BlF2uJgTEvM4Fvx4cqtQvhxD3O90Qv8vqWSt5sr5Xp63/v1RL4zEaC5xzmgociLS+L2/JlR014DCJyB8Oh/p4A6j33Q691TOIcsffQBnSvg5+mGMlpCZpwCuCU1h+9nTw035hHEqTVcl1vKjSlZ/b1KzQdpHWZalwA+6nHy2+qtwz53OPfY5HXzUPU2mgaJJ/gmMOYGXUEhN8IdJwJ+flm5mY3th49ZwEIwAvFLwhYxslYQPNRUwcdtNcdUzX0FHBDfAK0bPsgap6goVevCPoY/tVWxpasBrwjwUv0+NnnDFcG5UZSlLoYljpYeF8vba8N8L+clWpmSkMb1qeGpTy/am6mIIUp3pMRFzixE8D/hRiXC1Rgp660iwG/qvuCL7tYIY8SIs8G+XDFXRnGmQl7EUpQK+MWhLaxoPECjx8lwhvgqFBIjBnotCH5VtZW3Gito8jiHzMyIZL+9jU0hAwIFOFGj579sk2JautCr1NyUU8psdfiE690ee7++FXMAABcqSURBVL+6A9HIUFSoI9aTX+hu4YX6fUNmTgQzVIbAgEJ8xPf4oqONpw5/gfsYDpIVvloKecxd7ipF4VyThRe7m8LcUM8723m+Yh3n6U3Mj09joimdUlMGmYaEIdd+BaLf+nKd38urDeUxGbx5yZlk6hOOaTtPiUtmoi6Bh9qq8PSpxSYR4N6a7RTEJVE4zPV0IXrdv6E0+3t4rbEipgCgk5OsZI3BGjP0rn2903KI1Bg8KHnGJE5KNH8F51FHmajRc7V1Aotrd6CI3lXbwwE/T9TuYrHXw8NtVWG/X2IyMy/RwqrDu8KOxxKuuK6tlvdDZms2ReHijALUisK56XkkNR+gs0+ptQvBe82VlMSnHvOgIXfAT21EtH6WSoUmZBmsVw4FkdnhVyRn4exx8jd7S/DYNl8Pv636nCcmnB7M5ffFGDUdbQ19j7ub1xrLhxwMKyicl5ZLwjH05n3U1UhzDGbXqNJwdlpumBFUgBmmDNKaKmjte/oAsNrr4r3qLZxYb2RhfDpTEjM4wZRBuj6BhCFyuScmWqC5PGyg8ZHXxUfVW5lRt5sL4lIoTbIyM9GMWZ8wZG74ju5makLejQDON2XEvBYMYNHHc1myjU2th8LexsbOeqYnWQY9N1tr4O7UXO5tPOol6EHw+6YKJselcFZ67oAptxoltlmoW6XiWWsJd9Xvpa6vrQrwcOshpsSlstBcMKxASkF/fQwKK1urMHcPHT+SYzAxO8n69Q+KU4CzMvKY21zOWq874gNVeM9j5z2PHdoOUajWcaExiTNTx3Fyio30AaIzFZR+a+hbfB6uqNka05v5XHfqMTfoCYqaH2RPocxj5xVH76ccANb7PNx/6HP+UHxKTEVLQmfi2giB2+PzcGXNtph6/bPCk/sZ9EpnJ+4hineoVArF8SmD5rBXiQA31u0mlgTE35uLx8SgCwQVjo4hUxo1KhXFkWvCEQNGBYX5qdnc5Wjj123VweNvujpZW72V9r4PWQEma3T8IGsKdVFmIkOl/dj9Xt5sDR8cnGJIYkpfTEGOMZEbTGZ+39UQ/Ptz7TVcYZtI1jEsRuEK+Hm5bm9fsZOjTNboyYyQGeVI/mzIqx6nNbLAOp5dFZ/yacjg5E1XJ0XV2/l5/okYVZpej4UYesIbbQ39DVcHb1RvjUnWG5Osx9Sg39tRCx01Q/7uQl08J6dk9ZvVnpBk5bL4VJ5ytIZ7K1DY4nWzpe/66SoNF+hNLEjJZn5aDrYBBuAzksxcH5/B3x0t/f62zedhW1cDdNVjVmk4X2/irJQszk7PI3MAmamM4q4vTcgYlrFRgJMSzRhbDxHqu7LH4NJ2iwCXmwtp8br5feshnH2apE4EuLt2J1lGE5PiU6Oe2yNEzFUmv5WeR4ffyz2N5XT1hVd2CMG9dV+QazRxwjD0kkJ/fawCftiwLyY9eG9qHrMSLWMe43Vc0tbSdEYeK5jDzQc3stHrpmeALjvg9/K4vYXH7S2c27CX/82eximpOcMYScXwO2Xs1vnMOiN3586govwTPg+JIH7O0cr4w7u5LXf6l+oO/F3NDv7Z3TTob0rUOj4tvQBVTAFOSgy/GBsB9gvBbVWf8+EQLulzdXG8PPW8CK9P/2cyqNRcZ5vMp/ZWPuyL/PUA9SHuOYHCTy0TmGxK43AUgz7UGvLu7haedx6tSBUPLM4oCAaT6VVqFmbk8/uu+uAzlvu9rG+t5ju2icPqyR3uLpY17A8tSEgAQXWPk+32Ft52deAM+aMBuCY9f8jqb0d6YlJ8KndnTeX6qi3BiG4X8Nf2Worjkrk6ohrc8Zb143Fv1QDSHafWcE/BbFwVn/KqqwP3ANdvCfh5ztXBc64OTm7az83WiXzbWtzPQ5mg1nJvwSxcBzbwqrMdzwDXawq53pnNB/ihtYTzzYX9Bhz9iwgJsnTDz1LQa/TEKwquEDmq83rwBALohwjcNag03Jg9h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\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

 

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_76 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'PLTE' 41 6 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 59 826 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:56:38] "POST /myclass/api/Invoice_Inscrption_With_Split_Session_By_Inscription_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:56:38.428325 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:56:39] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:57:16.096272 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:57:16.099272 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:57:16.103272 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:57:16.105272 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:16] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:16] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:16] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:57:16.120273 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:16] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:57:16.154295 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:57:16.157282 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:16] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:16] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:57:32.363531 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:57:32.367528 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:32] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:32] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:57:41.251175 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:57:41.254148 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:41] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:41] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:57:54.838963 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:57:54] "GET /myclass/api/GerneratePDF_Partner_Invoice/JXT3OAh0wK_bmdmZ9V3_K4gCtfktxUiFCA/68f4c3e5b2209aad16855997 HTTP/1.1" 200 - +INFO:root:2025-10-19 12:59:16.983534 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:59:16] "POST /myclass/api/Get_Personnalisable_Collection/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:59:17.002532 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:59:17] "POST /myclass/api/Get_List_Partner_Document_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:59:23.714960 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:59:23] "POST /myclass/api/Get_List_Partner_Document_with_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 12:59:25.603028 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 12:59:25.615938 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:59:25] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 12:59:25] "POST /myclass/api/Get_List_object_owner_collection_Stored_Files/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 13:02:33.892100 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 13:02:33.893100 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 13:02:33.893100 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 13:02:33.893100 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 13:02:33.893100 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 13:22:48.266800 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 13:22:48.266800 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 13:22:48.266800 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 13:22:48.266800 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 13:22:48.267786 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +INFO:werkzeug:WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://localhost:5001 +INFO:werkzeug:Press CTRL+C to quit +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 13:23:05.559929 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 13:23:05.559929 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 13:23:05.559929 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 13:23:05.559929 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 13:23:05.559929 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 13:27:20.662719 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:20] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:20.716750 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:20] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:29.551837 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:27:29.554347 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:27:29.557378 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:29] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:29.564385 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:29] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:29] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:29.578382 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:29] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:29.603151 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:27:29.605151 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:29] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:30] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:34.853110 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:27:34.856105 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:34] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:34] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:38.134512 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:38] "POST /myclass/api/Create_Invoice_Avoir_Total/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:38.243210 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:27:38.246207 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:27:38.247208 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:27:38.253207 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:38] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:38] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:38] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:39] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:40.780422 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:27:40.783420 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:40] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:41.339012 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:27:41.343016 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:41] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:41] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:42] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:57.893659 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

 

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_77 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

 

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_77 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'PLTE' 41 6 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 59 816 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:59] "POST /myclass/api/Invoice_Inscrption_With_Split_Session_By_Inscription_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:27:59.131291 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:27:59] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:28:07.127606 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:28:07.130605 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:28:07.133606 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:28:07] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:28:07.139604 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:28:07.145610 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:28:07] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:28:07] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:28:07] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:28:07.188117 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:28:07.190117 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:28:07] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:28:08] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:28:09.009854 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:28:09] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:28:09.056370 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:28:09] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:28:11.007819 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:28:11] "GET /myclass/api/GerneratePDF_Partner_Invoice/JXT3OAh0wK_bmdmZ9V3_K4gCtfktxUiFCA/68f4cb3d8191aabe4f2d9366 HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 13:31:16.561339 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 13:31:16.561339 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 13:31:16.561339 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 13:31:16.561339 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 13:31:16.561339 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 13:31:16.638887 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:16.640885 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:16.641887 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:16] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:31:16.646402 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:16.650821 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:16.654600 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:16] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:31:16.658605 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:16] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:16] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:16] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:17] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:31:20.558825 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:20.560825 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:20] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:20] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:31:23.413583 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:23] "POST /myclass/api/Create_Invoice_Avoir_Total/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:31:23.489940 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:23.491938 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:23.494938 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:23.499938 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:23] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:23] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:23] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:24] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:31:29.844285 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:29.846286 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:29] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:31:30.352367 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:30.354365 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:30.360381 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:30.367366 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:30.370367 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:30] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:30] "POST /myclass/api/Get_Given_SessionFormation_From_Id/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:30] "POST /myclass/api/Get_Given_SessionFormation_List_Automatic_Traitement_From/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:31:30.831054 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:31:30.836018 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:30] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:31] "POST /myclass/api/Audit_Session_Action_Inscrit/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:31] "POST /myclass/api/Get_Editable_Document_By_Partner_By_Collection/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:31] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:31] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:31:45.945267 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

 

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_78 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

' + dest = <_io.BufferedRandom name='./temp_direct/Partner_Invoice_NEW_Invoice_78.pdf'> + path = '' + link_callback = None + xhtml = False + context_meta = None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.files:FileObject 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\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

 

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_78 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'PLTE' 41 6 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 59 823 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:47] "POST /myclass/api/Invoice_Inscrption_With_Split_Session_By_Inscription_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:31:47.087210 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:31:48] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 13:34:15.480316 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 13:34:15.480316 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 13:34:15.481316 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 13:34:15.481316 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 13:34:15.481316 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 13:34:16.855996 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:34:16.863998 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:34:16.879997 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:34:16] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:34:16.900000 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:34:16.910529 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:34:16] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:34:16.927084 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:34:16.934629 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:34:16] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:34:16] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:34:18] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:34:19] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:34:20.452499 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:34:20.456473 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:34:20] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:34:20] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:34:22.782460 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:34:22] "GET /myclass/api/GerneratePDF_Partner_Invoice/JXT3OAh0wK_bmdmZ9V3_K4gCtfktxUiFCA/68f4cc2114c6c360131447e6 HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:01.599098 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:01] "POST /myclass/api/Get_Given_Personnalisable_Fields_By_template_ref_interne/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:07.407348 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:07] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:07.449349 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:07] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:15.653750 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:15] "POST /myclass/api/Create_Invoice_Avoir_Total/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:15.744322 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:15.757344 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:15.767345 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:15.786381 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:15] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:15] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:16] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:17] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:21.146518 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:21.149517 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:21.150517 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:21.153516 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:21] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:21] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:21.166536 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:21] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:21] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:21.202535 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:21.205535 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:21] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:22] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:24.350979 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:24.353993 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:24] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:25.262485 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:25.265485 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:25.270595 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:25] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:25.279486 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:25.280485 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:25] "POST /myclass/api/Get_Given_SessionFormation_From_Id/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:25] "POST /myclass/api/Get_Given_SessionFormation_List_Automatic_Traitement_From/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:25] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:25] "POST /myclass/api/Get_Editable_Document_By_Partner_By_Collection/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:25] "POST /myclass/api/Audit_Session_Action_Inscrit/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:26.245796 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:35:26.248797 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:26] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:26] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:35.660110 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

sssssssss

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_79 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

sssssssss

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_79 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'PLTE' 41 6 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 59 829 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:36] "POST /myclass/api/Invoice_Inscrption_With_Split_Session_By_Inscription_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:35:36.549309 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:35:37] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:36:18.368784 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:36:18] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:36:18.393780 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:36:18.404814 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:36:18.410292 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:36:18] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:36:18] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:36:18.430306 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:36:18] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:36:18.444823 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:36:18.468858 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:36:19] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:36:21.169591 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:36:21.175577 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:36:21] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:36:21] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:36:22.559086 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:36:22] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:36:22] "GET /myclass/api/GerneratePDF_Partner_Invoice/JXT3OAh0wK_bmdmZ9V3_K4gCtfktxUiFCA/68f4cd0745c45d01e9d24971 HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 13:42:31.730371 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 13:42:31.730371 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 13:42:31.730371 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 13:42:31.731371 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 13:42:31.731371 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 13:43:21.856071 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 13:43:21.856071 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 13:43:21.856071 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 13:43:21.856071 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 13:43:21.857070 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 13:44:36.183688 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 13:44:36.183688 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 13:44:36.183688 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 13:44:36.183688 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 13:44:36.183688 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 13:45:08.419191 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 13:45:08.419191 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 13:45:08.419191 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 13:45:08.419191 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 13:45:08.419191 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 13:47:50.794148 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:47:50] "POST /myclass/api/Get_Given_Personnalisable_Fields_By_template_ref_interne/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:48:29.990708 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:30] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:48:30.039318 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:30] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:48:39.446081 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:39] "POST /myclass/api/Create_Invoice_Avoir_Total/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:48:39.554743 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:48:39.556742 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:48:39.560742 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:48:39.566745 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:39] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:39] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:39] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:40] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:48:44.007020 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:48:44.010014 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:44] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:48:44.525035 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:48:44.529023 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:44] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:45] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:45] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:48:54.208297 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

sssssssss

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_80 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

sssssssss

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_80 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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', Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'PLTE' 41 6 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 59 822 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:55] "POST /myclass/api/Invoice_Inscrption_With_Split_Session_By_Inscription_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:48:55.698722 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:48:56] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:49:39.261747 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:49:39] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:49:39.274810 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:49:39.285383 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:49:39.291381 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:49:39] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:49:39.299366 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:49:39] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:49:39.315366 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:49:39] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:49:39.327381 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:49:41] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:49:42.486113 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:49:42] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:49:42.502502 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:49:42] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:49:44.338817 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:49:44] "GET /myclass/api/GerneratePDF_Partner_Invoice/JXT3OAh0wK_bmdmZ9V3_K4gCtfktxUiFCA/68f4d02623eaca59cb0fff1d HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:49:51] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 13:53:18.467241 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 13:53:18.467241 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 13:53:18.468229 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 13:53:18.468229 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 13:53:18.468229 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 13:53:19.054209 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:19] "POST /myclass/api/Create_Invoice_Avoir_Total/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:53:19.230297 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:53:19.235847 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:53:19.243875 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:53:19.247886 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:19] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:19] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:19] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:20] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:53:23.478587 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:53:23.481120 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:23] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:53:23.995583 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:53:23.999530 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:24] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:24] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:25] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:53:33.943408 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

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SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
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  Date Facture   19/10/2025 
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sssssssss

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Facture n° NEW_Invoice_81 

Destinataire : qsdqs


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Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

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Intitulé de la formation : CONDUIRE UN PROJET  

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DésignationQuantitéPrix unitaire HTTotal HT
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CONDUIRE UN PROJET
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29/09/2025 - 07/10/2025
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11500.0 €1500.0 €
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 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
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Condition de règlement :    

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Date échéance : 19/10/2025 

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 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
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BIC
QNTOFRP1XXX

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JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

sssssssss

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_81 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
29/09/2025 - 07/10/2025
\n
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'PLTE' 41 6 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 59 813 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:35] "POST /myclass/api/Invoice_Inscrption_With_Split_Session_By_Inscription_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:53:35.311320 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:53:36] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:54:14.988653 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:54:14.992649 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:54:14] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:54:15.000658 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:54:15.006658 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:54:15.014801 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:54:15] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:54:15] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:54:15] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:54:15.059329 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:54:15.062316 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:54:15] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:54:17.639246 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:54:17.646225 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:54:17] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:54:17] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:54:17] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:54:19.061289 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:54:19] "GET /myclass/api/GerneratePDF_Partner_Invoice/JXT3OAh0wK_bmdmZ9V3_K4gCtfktxUiFCA/68f4d13d731b57703cd5d00d HTTP/1.1" 200 - +INFO:root:2025-10-19 13:55:45.465513 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:55:45] "POST /myclass/api/Get_Given_Personnalisable_Fields_By_template_ref_interne/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:18.623083 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:18] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:18.670229 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:18] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:22.740417 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:22] "POST /myclass/api/Create_Invoice_Avoir_Total/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:22.851903 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:57:22.854901 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:57:22.858901 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:57:22.863905 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:22] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:22] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:23] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:24] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:26.343636 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:57:26.347633 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:26] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:26.805654 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:57:26.806652 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:26] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:27] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:28] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:32.453386 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
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  Date Facture   19/10/2025 
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sssssssss

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Facture n° NEW_Invoice_82 

Destinataire : qsdqs


  -

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Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

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Intitulé de la formation : CONDUIRE UN PROJET  

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
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CONDUIRE UN PROJET
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29/09/2025 - 07/10/2025
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\n  ouii   tt ccc ommentttdddsdqqd - INSCCCC uuu - iiiiiiiiiiiiimeomooossqsds INCS yy
11500.0 €1500.0 €
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 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
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Condition de règlement :    

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Date échéance : 19/10/2025 

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 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
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BIC
QNTOFRP1XXX

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JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

sssssssss

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_82 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
29/09/2025 - 07/10/2025
\n
 
\n  ouii   tt ccc ommentttdddsdqqd - INSCCCC uuu - iiiiiiiiiiiiimeomooossqsds INCS yy
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'PLTE' 41 6 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 59 824 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:33] "POST /myclass/api/Invoice_Inscrption_With_Split_Session_By_Inscription_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:33.650395 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:34] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:39.262540 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:39] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:39.277535 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:57:39.291552 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:39] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:39.307560 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:39] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:39.323115 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:57:39.333155 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:39] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:39.338139 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:41] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:42.141057 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 13:57:42.165137 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:42] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:42] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:57:44.122557 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:44] "GET /myclass/api/GerneratePDF_Partner_Invoice/JXT3OAh0wK_bmdmZ9V3_K4gCtfktxUiFCA/68f4d22c731b57703cd5d016 HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:57:49] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:58:05.171353 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:58:05] "POST /myclass/api/Get_Given_Personnalisable_Fields_By_template_ref_interne/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:58:22.381645 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:58:22] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-19 13:58:22.443266 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 13:58:22] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - +INFO:werkzeug: * Detected change in 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\Session_Formation.py', reloading +INFO:werkzeug: * Restarting with stat +DEBUG:matplotlib:matplotlib data path: C:\Users\Cherif\Documents\myclass.com\Siteweb\Elyos_Ftion_Continue\Ela_back\Back_Office\venv\Lib\site-packages\matplotlib\mpl-data +DEBUG:matplotlib:CONFIGDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib:interactive is False +DEBUG:matplotlib:platform is win32 +DEBUG:matplotlib:CACHEDIR=C:\Users\Cherif\.matplotlib +DEBUG:matplotlib.font_manager:Using fontManager instance from C:\Users\Cherif\.matplotlib\fontlist-v390.json +INFO:root:2025-10-19 14:04:34.296107 : ++++ ENVIRONNEMENT DEVELOPPEMENT ++++ +INFO:root:2025-10-19 14:04:34.296107 : ++ DATABASE mongodb://localhost:27017/cherifdb_dev_fi ++ +INFO:root:2025-10-19 14:04:34.296107 : ++ DBNAME Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'cherifdb_dev_fi') ++ +INFO:root:2025-10-19 14:04:34.297109 : ++ FLASK PORT 5001 ++ +INFO:root:2025-10-19 14:04:34.297109 : ++ LMS_BAS_URL mysy-hosting.com/ ++ +WARNING:werkzeug: * Debugger is active! +INFO:werkzeug: * Debugger PIN: 479-264-725 +INFO:root:2025-10-19 14:04:34.540676 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:34] "POST /myclass/api/Create_Invoice_Avoir_Total/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:04:34.624569 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:04:34.626574 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:04:34.628601 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:04:34.634074 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:34] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:34] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:35] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:35] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:04:44.284788 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:04:44.286115 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:04:44.288667 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:04:44.292086 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:44] "POST /myclass/api/Is_Partnair_Has_Digital_Signature/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:04:44.299105 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:44] "POST /myclass/api/Get_Given_SessionFormation_List_Automatic_Traitement_From/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:44] "POST /myclass/api/Get_Given_SessionFormation_From_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:04:44.831006 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:04:44.834989 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:44] "POST /myclass/api/GetAllClassStagiaire/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:45] "POST /myclass/api/Audit_Session_Action_Inscrit/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:45] "POST /myclass/api/Get_Editable_Document_By_Partner_By_Collection/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:45] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:04:53.032495 : Security check : IP adresse '127.0.0.1' connected +DEBUG:xhtml2pdf.document:pisaDocument options: + src = '

 

\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

sssssssss

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_83 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
29/09/2025 - 07/10/2025
\n
 
\n      ccc ommentttdddsdqqd - INSCCCC uuu faire un avoirrr22 - iiiiiiiiiiiiimeomooossqsds INCS yy faire un avoirrr
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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\n\n\n\n\n\n\n\n
SASU JMJ FORMATION
BP 60540 97206 FORT DE FRANCE CEDEX
Email: jmjformation@gmail.com
Tel: 0596 97 58 46
\n

\n
\n\n

 

\n\n\n\n\n\n\n\n\n
  Date Facture   19/10/2025 
\n

sssssssss

\n\n\n\n\n\n\n\n
\n

Facture n° NEW_Invoice_83 

Destinataire : qsdqs


  -

\n
\n

Client : qsdqs qsdsq mysy1000formation+05@gmail.com\n  

\n

Intitulé de la formation : CONDUIRE UN PROJET  

\n
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DésignationQuantitéPrix unitaire HTTotal HT
\n
CONDUIRE UN PROJET
\n
29/09/2025 - 07/10/2025
\n
 
\n      ccc ommentttdddsdqqd - INSCCCC uuu faire un avoirrr22 - iiiiiiiiiiiiimeomooossqsds INCS yy faire un avoirrr
11500.0 €1500.0 €
\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Totaux
 Total HT1500.0 €
 TaxesPrestations de formation en exonération de TVA, article 261-4-4a du CGI
 Total1500.0 €
\n

 

\n

Condition de règlement :    

\n

Date échéance : 19/10/2025 

\n

 

\n

 

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 Code banque
16958
Code guichet
00001
N° de compte
04285131654
Clé RIB
46
Monnaie
EUR
 IBAN
FR76 1695 8000 0104 2851 3165 446
\n

BIC
QNTOFRP1XXX

\n
\n

 

\n

 

\n

JMJ FORMATION
Siége social : 15 Rue Georges Eucharis, Espace POSEIDON, Dillon Stade, 97200 Fort-de-France
Adresse Postale : BP 60540 97206 FORT DE FRANCE CEDEX
Siret : 799 674 064 00032 - Numéro de déclaration d’activité : 97 97 01982 97 – QUALIOPI : N° RNQ 3318
Tel : 06 96 50 23 33 / 0596 97 58 46/ Mail : jmjformation@gmail.com / Web : www.jmjformation.com  

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0RhuKMU/Zqs5woAbCtKhprveBAHIQQQGjEdhNo44S15MLyMfCwfVuRhT0EydN0YcEVHPLYVJKLESJ5v9wzEYLueXZKvhVCEXveRMAeA/Px8bN68qU1BVgDgt99O4bffTjcKI9we9Ho9fvzxx1Z5tIvFYjz99LN47733MHLkyGaF+Z1apkwmw7Jly7Fly1b07t2n4TeJxAzLlz+NTz/9PwwdOqxTNd/W1gnRvR3ISSAQYOLESQgPH2ky7ZUrl3DixIk29aOioiJ8++23JpdGxGIxFi5cBDc39/taPjGB3laTBsehl42hx7yCCIVqZbufq+J12F6cgoomBu/nrJ0RZPNfr1pbkRmed/ZtZFrhAeytK8dNRXmXdpRJPTzxumsgHJvMxOtBWFt0E6cr8rrNSe5mXSUO1hSj6RRmtJkVJrn4QMgO2+g0wbNnzx5kZma0fXIoFGLjxg0oLi7uUBkKCgpw8OCPrcrv6aefxcqVK9u8vUwkEmHkyJF4//0P4OPjCx8fH3z++edYvXp1qxy/7mfs7e2xbNkyk+vDHCfA9u2RyM3NbXXf++mnn5CUlGgy7ahRYzB27Nj7vi2YQG8HjhJzND2NRglCja793q+xNSU4r6oxuD7LxbeRQxcHYJyzF3ybrJcrQDhQnNqlWrKQ4/CYey8ssHOHWWMFHtm8DmtzopFW1/UBOAjAn5X5KDTyrk+5+MNFYsE6aSeRkpKCPXu+b7emePNmEg4fPtyhEKDR0dFITU0xmW7WrNl49tlnTWrlzQscDqGhofjiiw2IjPwOERFTWhURjQEMGTIU06fPMNlPUlKSceTIEeh0OpPPzMrKwu7du0xq9NbWNli0aCHs7Ozu+3ZgAr0daHjeqJBpr26qIx67i1INDkSZa2GHUCOmY0exBZ519DTIf0d1IdK7OHqZrVCM1Z4DMNnC1qDzXNCq8K+s6yju4m10PBHO1hSj6fTJSyjBKAcpBGDaeWeg1WoRGbkNJSUd07A3bfoKOTk57fs2dDqcP3/epLm7Rw8nLF68uMN7jzmOQ79+/eDl5cWOVG0DVlZWmDdvvkmnL4FAgG3bvjVp8eF5Hvv27WuVZSgiIgJDhgxljcAEevuo1KjQ1JvVHByshe3bKpGsqMBxheG+2kecvI2e7iXkODzo4gP7JuvZubweJ0symvFT7zxczSzxludABIvMDETnobpybM6L7ZT1/ObIV9chpd7wCMiJFrZwkJizDtpJXL58Gfv37+vwc8rLy7B3795WaWVNKSsra5Uz3oIFCxEQEMga7S4SHByMJ56Yb1KjLikpxt69P7QYEjYxMQH79v1gMs8ePZywcOGidltlWp7cARUV5Q2n8rX3Lz8/v83+J+2Febm3VTsnHtdrDDUWO04At3Zs3eJB+LEkDalNBOAEiSVG9fBo9j5vSzu8aNsTa+/w8tYD2FKehWluAW0Kj9vmjg4g1NYZa2XBWJ59HcX035VzFYDPy7PhZW6LJ9x6dcladrlGiXwjE4Yhtq7d7mnf9rr7e2h9tbW1iIzc1qqDSYjIpDa7Y8d2TJ48GSEhIW0uhylzOxFhxIgRXb7Hl9EyYrEYjzzyCH7++RiysjJbtILs3r0LERERGDBggMHvarUae/bsQVFRocl+9cQTT6BPnz5d861yAqxY8Qr0el2HnuPr64fffjvTJZOObhPoZdp6FKg7LwCDABxczCzuujk1X1WLPYoSg+v9Le1bfVb2nWQpa7C/qgAcGpvspzvIWlwLFnMCTHf1x9rqgkbRJG7yOpwty8Z8Wd8uNu1wmOTkhdWqGrxSnNKo9BVE+KgwCd4Wdhjp0PlepyX1ChQZrJ8T3Mys7+0Rjwjf5NyAo7DzYuALOQGe9Q2DuJPPqb969QqOHv3J4Azxpnh5eWPgwIE4cGAfWtqDXVtbi507d6J3795tOme8oCAfdXUKWFg0P1kOCuqNgIAAJlHvAXx9fTFv3jy8++6/W0ynUNRix47vEBQUBAuLxuNcVFQUDh48aFKYe3v7YPbsR7t0371QKOzwRFEg6L6JZhcJdA4v5dyAGdd5Fd1LZIadfSbAWnj3NLA6vQ4bc2OR3WTGZgbgIXt3CNvxvqfLshHbZLvbEJEEU1x8TdeJTQ88Y9UDXynKGq5pAewsy0SEqx96iLt2RigRCLBQGoQMVQ2+qCls9FuiXot3c6KwwcwS/padG0zB6Bo9ocsOPelEiY73aks69Ym9OQGepsGdOxkvK8WGDRtMCnOe57F8+XIMGzYcly5dQn5+y+cPHD16BFOnTsXo0aNbXZbS0lKIRC1/84GBgd2i/TBaI7wEeOihh/Dzzz8jKup6i2mPHTuGGTNmYNSo//aHuro67N69GzU11Sb3tS9duhRyuZxVeiNFq4vI0muRrFN32l+9TtPla8MtoeJ12J2fgA3VBYZailCM0T082mw7KFTXYUd5lsF9D9r2hIeRuO1NsRCIMMvFz3CSoFHiXHlOt9SLg8gMKzxDEWFuWN5f1XX4JCcaFdrOjX1dayTMKzgOTmLmkdxReJ7HkSNHceWK6UAeffr0xcSJk+Dj49OqEKB1dQps2vQVampa77hZV6cEZ2Ki7OHh2Satn9G1uLm5Y9GiRRCLW7ZE1dersG3bNigU/z2O+dKlizh27IjJvhQWNgSTJ0d0W1S8+16g/69AADJU1ViXcRUri5JhTDStdPGDczuOiv29PBfntapG05QAgRCzXP1bPTkYaN8Ts80ar5frAPxYkoG6Dq79tBZPCxu86dEffY2Ent1eU4xv8+I71UnO+EE4BP09HoTj70BOTg42bfrKpJlQr9fjqaeegouLMziOw4wZM+DhYTqi1p9/XsCxY8da//21ok27y+GI0XrGjh2HkSNNB5v5/fffce7cWQBAVdWtMwBMOU9yHLB8+VPsKNv7UaDzAIo0KmTXK1r9l1VfizhFOc5X5OHTzGuYm3QGaytyUGvEQjDD3AaP9AyAoI3OX1VaNXaUZRg0xmRrZwRZt/54RjuRGR4zYp7fVV+Nq910aAsHYJi9G/4h7Qf3JtqUGsDHZRk4UprZaSfS2TSz7FKkVbEvugPodDrs378fhYUFJtOOGzce48aNx+11czc3NyxfvtykZkVE+Pbbb02a529jbW1lMkpYfn4etFota8B7CDs7Oyxa9KTJbYQajRqRkZEoLS3F6dOncfbsGZP9Z8qUhzBixAhWyUbosgXpXgIRLDrRw9lGKGqXh3AdgBezrqKtxth6AHVEBpHbGmnHIjO84z0Yju1Yu71YVYCjTbZeuXIcHnX1b7Nn+IgeHphQlIyTTQTajyXpGNFDDjHX9fM2DsBUFx8kq6rwUWkmFHfUWwnxeCcvDr4Wtgi1delwXs05lZVq6u/tr43jsKdnENwknXeQjYDjIOoks2NycjK2bv26VWkXL17SaM8xx3GYPDkC33+/2+TRqTdvJuHAgR/xwgsvmJwAODk5Q6fTtWi+TU6+CY1Gw9bR7zHCw8MxderD2Lv3+xbTXblyGXv2fI9ffvnFZH+QSCRYsmQpbGxsWAV3hUAnMrayTXjPMxQDjIRIbb8pgYNlO7wFedw66rOzCROZ4VOvQejbjndU6nU4VGoYMGGUhR0G2bm2+XkuEgtMdZDiZElaYy29rhxP1pQitB3PbA8WAiGelwcjW1WL7YpS3Kkzxes1eCv7Or70D4dnB7fUuZlbw47jUN1I4+eQV19zj39uHAY7yuBzDx65Wl9fj61bv4ZSaXpnyvTpMzB0qGEgjx49euDZZ5/Ds88+Y2Jew+Hbb7di8uTJ8Pf3bzGtVCqFlZUVeL75iXViYgJSUlIwaNAgNqLfS8JFJMKCBQtw8uSvqKgob1GGfPLJxyaXV4h4LFiwEH379u22dxCLxR32cpdIJN0WpKjDAp3jjOvNPcUW8DL/35xFPW5hj1Weoehv69KuTXQxNSXYcodneoNA5ATYXZDUrmdW67V/6cl3bh/j8WNJGvrZukDUTR3KXmSGVV4DkJb6B8422bb4s6oGn+VE49++Q2HdgaNebSSWcOMEqCZ9IwvB77UlWKjXwkbIHKTayqVLl3DkyE8m092KCDbPYKvR7bFg7NhxGDduPE6f/q3F55SXl+O777bj7bf/0eLZ1dbW1vD19W9xLzrHCXDu3DmEhoayvej3GL1798YTTzyBL75Yb1IxNIVM5oE5c+Z0WzheIsLbb/8Dw4cP75gyKhB2m9MmCyzTBgaIzPCUkzcecQtAD3H74oVriceh0nSjv0XWVSCyrqJTy7y9uhCLlFXw7UatMNDSHv/2GIDF6ReR2sQj/bOqAnjlJ+IZWT9I2mkqlptbw0tsgZtqxR02IeB4fS1K1UrYWLKYzm1BoVDgq6++bDFy153aef/+oS0K/KVLl+G33061qJVwHIcdO77DlClTjWr7t3F2dkb//v1NBpfZtWsnHnroIbYf/R5DKBRi9uxHceTIT8jKyurQs5544gn4+3df+xIRpFLZ3yoCIfNybyUPmtvgcNB4LPUIbrcwB26Fed1eXdRt5c7l9ThSkt5pDmmtZZi9G95072PUj2JdSSpOdWBbnaVQhCFGHAdreT1Ol2d3+7v+3fn1119x5cplk+lcXFzxxBPzWtSoAWDgwIGYOXOWyefxPI9vv/3G4AzyRhqHSISRI0eZPNylrKwU33yzFXV1HQ9mFR8fh+zsbNYxOgkPDw8sWbKsQ7sR+vULxrRp05kF5n7X0C0BvO3iB3+L1mlt5eo6PFV00+B6oroOefW1kFm0fxlBT4QjJekope7dZrOlIhdz3YPQ08yq2/IUchxmu/ohS1WNf5VnGUwy3smNgZu5Nbza4SDGAZjg6IGvKnJQcofw5gB8VZqBSc7eXRr69n+JwsJCbN68uRWnoXGYO/cxBAUFmXymhYUF5s+fj6NHfzLpfX78+M945JFZmDRpUosTBF9fvxbDiQLA3r170LNnTzz33PMmJx3NER0djVdfXQWxWIznn38BEydONBnYhmFCaxQIEBERgUOHDuL69WttF1KiW2edy2QyVpn3u4YuBjDWQYZHXP1a9bdI3g9rHQ1jqOcQj49ybqCyA0FS8uoV2FmVj+7eNZuk1+BUaVarwvLwnRi+x1IownMewVhiZahNX9fV44PsKOS3MzxwiK0zhjbZf08AonRqHCpOY3vSW8mBAweQlJTQKi1rzpw5rdaQgoODMW/eApNroxzHYePGDSgvb95pSiaT4eGHp5l0LOJ5Hhs3bsTGjRtRU9M2B0mdToerV6/i7bffQnLyTcTHx2HlyhVYv/5zlJSUsI7SQZydnbFs2fJ23Tt06FBMmDCBnX7HNPS2I+EEWCbrhzO1pTjTZBvYr2oFduTH4wWvgW12XCMQfi5JQ3qTMK/2nADP2Uth2UkhbXkiHKkpwhVt4y1cm8rSMa2nH2xELTuUEBE685w0J7EFVnsNRGrKeZy/o0x6AD8qKyHKiQLa4QZoLRTjmZ6BOJd1DdVNpiAflaShv7UTwnvIOz3yv5Z4XKjIx0A7V9iIJH/rvp6eno6dO78zbRHhOCxevKRNGpJEIsHjjz+OI0d+QllZaYtpY2KicfToUcyfP99o5C+O4/DYY3Px88/HkJaW2uKzNBo11q//DElJiVi+/CmEhIS06JDE8zyqqqqwb98+fPnlRlRW/teHpa5OgfXrP8fVq1fx+utr0K9fPxaZrAOEh4cjImJqqyLB/dfaY4nFixfDwcGBVSAT6O2jp7k1Vkv74M+sa40iwykBbCjLxHB7KQbZ92zTM0s19dhZnm0QaW6JjQve9A2DWScF8CcCfPMTsbAgvtG2sQsaFc6X52KKq1+L9ws5rtM7hb+VA972CMXTmVeRfoeTnB7AHmVVu587zFGGCSVp2K+sbHQ9n3isybmBrebWCLRy6DShXqfXYnNuHD4uTcc8O3e87TMYtn9Toa7T6bBz5w7k5+ebHFz9/PwxderUNmtIgYGBWLhwIT755OMW7yUibNr0FcaNG9dsbG53dymWLFmKN99cY3ItVq/X45dfjuPcuXOYOHEiJk16EL17B8HVtScEAgGICDU1NcjNzcWff17A/v37kZubY/S5PM/jwoU/MG/eE1iz5g1Mnz4dlpaWbJBsB7a2tliwYAHOnTuLujpFq+6ZNGkShg+/O0FkOI5DcXERQsOCJAAAIABJREFU0tPTO+2Zjo6OXTo5YQK9GcY4eeHVqkK8W9U42loqr8enuTHYaOUAhzbEDj9dloUYXVMvYg6PuPrDojOP/OSAsc5e8ClORnITa8Dm4hSMdfKEZYvburrGrDXaUYZVymq8XpSEmjtiF3TEMG4rkuBptyCcybiIiibxEC5qVXg+9QI+8QlDSDu3F95JmUaFT7Ki8FFVPgDCF1V50GcQ3vEJ+1tq6lFRUdi1a6dJIc1xHJ555lk4O7cvpsTMmY/g4MGDyMhoeVDMz8/DgQP78eKLLxoNO8txHKZMmYITJ34xGU3sNiqVEocPH8JPPx2GSCSCubk5HB0doVSqUF1dDb1e1wrfgVtUV1dhzZrVuHEjCq+/vgY9evRgg2Q7GDhwIB599FF8++03Jvuera0dnnxy8V2bQHEch7fffqvTrDJEwLp1n2LGjBldVmZmP2oGM4EQS2T9MExkZiAMvldVYV9BEvhWiqNqnQbfl2agrkn6+Zb27QpMYwpniSWecjDUdH6vr8W1qqK7Up9iToD57kF42l6KztqRyQEY7SjFuy4BBi3BA/hNU4elaX/idFk26tsZS17D3zKxP3vzLD6qymuYgqgBbKjKx/sZV6DQ/73CjtbV1WH79kjU15uOrDd06HA88MAD7V6/lMvlmD9/fqvu3759O5KSkpr93d7eHmvWvIGgoN5tHEgJWq0WtbW1yM7ORmlpCTQadauF+Z3PsbOzYxHpOjKumplhzpy5kEpNL988/vjj6Nev310tr16vh1ar7aQ/TZeXlwn0FvCytMNb0r6wbCLSOQCfl6Yjprp1zjIXK/NxSmPo/DXXxQ82os4POCDkOEzr6Q+XJjPLKgB7SlKh4fV3pT6thCI85xGCKZ14nKqIE2CerA9W2bkZ/f26To0HMi7jzZQ/EFdT2upDYlS8HimKCnyYcRnh6Rew30gUOi2AWr0WujbsWrgX3HouXryIQ4cOtkKA8XjmmWcahXhtDw899DD69DEd3au8vAzffvst1OrmHU979eqFd95ZC0fH7tWQiQhTpz6E55573mR8ckbLBAYG4rHHHmsxjUwmw5w5c9gOg7aOh6wKWma8szeWV+bh/2qK//txA0jkdfg8NxbrrUa3uI5ap9dhb3EalE22V0VIrDDEwb3Lyi23sMXTtm5Y22TJ4KiiDE8ryhHcCTHV21UuM2v8y3MgclP/wDVd5xyrai0UY4XXQBSmXsCuJuvpt1vs05oi7FKUYpalA8LtpRhg6wJzkQSWQjEshUKoeR4qvQ4KrQpRNaU4W5mHSGUl1H8Ja2O2mNfspVjjPRj2RhwNtc0I+cNlWXCuLu6SunU2s8TEHh4tpqmsrMTWrVtb5a0+bdoMDBkypMPlcnFxwZIlS7Fq1QqTWvGxY0cxffqMZk/q4jgOYWFh+PjjT/DWW2+26iCZjsLzPGbOfARvv/02c87qDIVDKMSMGTNx/PhxJCYmGKlvPZYsWQovL29WWUygd7KJSCDEsx79cT7pDK7rG5tMtisrMKrwJhbK+jV7oEpibSl+UFU1ES/AbCdv9BB3nelOzAkw1cXXQKDnEuHHolT064R15fbS16YH3pKH4Jns6yjsJGuBm5kV1vmHQ559Ax9WGT/Jq5jXY6OiDBsVZbAVCOEjEMFdIIQ9J0Qd8SgjPa7rNKhvhcb9vpMPnvfo36yFxeiBOMTjleKULqvX52x7tijQeZ7H8ePH8ccf502uCwoEHBYsWGg0xGt7mDRpEvbsGYzLl1s+Z12pVCIychsGDBjQrCYsEAgwfvx4ODk54R//eBsxMdFd9x2JxViwYCFeeOFFODo6sgGxsyb2cjkWLVqEN95YY3BcamjoQEyd+hALItMOmMm9Ffha2uNVtyDYGjG9v1ucglSF8T20WuKxvzgNyiYCYoTIDOOcPLq83H1snLHU0nAQ+qGmECmKirvY6ThEOHniTRd/dKa7i6uZJV73GYwNroFw4wQtTlhqeD2idWr8rFFit7oWhzV1uKCtNynMewnF2O8xAC97hnbJcklHMDVBKygowMaNG0wKcyLC44/PR0hISKeVzdraGs8993yrYlqfOnUSp06dNDHhEKB///744osvMGPGzC4xzbq5ueOTTz7FqlWvMmHe2X2V4zBx4iQMHhxm0PeWLVsGV1dXVklMoHfdQPlwTz8ssnGBsImmncnr8Un2Daj0OoP7UhQV2FFraF59xEHeLZHMLIUizHU1PM0qidfjRFnmXa1TMSfAPPcgLLbr3GUHO5EET3uEYL/fCCy0doJrJwWjkHECvGjnhoO9xmBmT39YCP9exi29Xo+DB39Ebq7pkLv29g54/PHHOv0QjGHDhv11hjpMTii2bt2K4uJik0LBy8sb7733PjZt2oIRI8I75RAMe3sHLFiwELt27cb06dPZmnkX4ejoiMWLl9zhZEiYNOlBjB49hlUOE+jNDA6d9BwLgQjPe4Sgt5EtX7uUFThUlNIoxhoPwvHSTAOTcm+BCBEu3bc2NNDBDbPMbQ2u76/IRV694q62jZ1IglWe/THF3KZT20zIcRju4I5NvcZgl/cQPGZhD6927vPvLRDhFTs3HPAPx8cB4ehl5Qjub9jX09LSsGXLllvHHbfwx/N6LFiwsM2e5K3BzMwMy5cvh0QiMVmOqKhrOHLkp1adwmVtbY0JEyZg06bN+Oyz9Rg9egxsbe1aHTuciCAUChEY2AvPP/8Cdu/ejbff/gf8/Py6NDrZrfrmm/2jLoh2SIQW8+xIvPX2EB4ejgkTJt5SQCytsGDBQtjZdd/hSqbqorP/upoOqxm2IjMscZA3GkwIBEdx92/tcBSbY4W91EgZO2efsK+VPd6Th+BsVaHBbzGqakzUqhvWxau1GlQQb1AeuZk1/Cy7z7HGXmSGBa4BkFcXNhJEBCBNWQWZuXXjwVEkwQJHeaMDTngQLLtor7WnuQ0+8h6CvnesLXMAHDqh/5gJhBjv5IlwRxmSFRW4VF2IazUlSNHUIUWvRSHPAw2BeDmAE6CPQIheQglk5jYYZ++OXjbO8LO0g6CNA7uVxByrHeXoTr92XwvbZgVHcXExli9/yrTlRCzCjBkzu2z9sn//UHzwwUcoKjK9fVIgEKKurg7W1tamrWgcBzs7Ozz00EOYOHEi0tLSEBcXhytXrqCgIB95eXmoqalGfX09hEIhrKys4eTkDC8vT3h7e2PkyFHw8/ODs7Nzt4QYFYlEeOONNw3Wjxu/vwBOTk6dluft6H3Tpk1r2Rolk3VbRDwrKyssXrwEp0//hhkzZiIsLKzbvpeIiAgMGzasW2VUr169uvT5HBELes24f9AToUSrQq1WDZ1OC9xeM+cE4AQCmIskcJJYsDPV/4dQKBSorKyERqOBXq8Hx3EQiUSwtLSEk5MTc766y+h0Oqxfvx4TJ05A3779WIUwgc5gMBiMvyu1tbWwsrJisfKZQGcwGAwGg8GmQwwGg8FgMIHOYDAYDAaDCXQGg8FgMBhMoDMYDAaDwWACncFgMBgMJtAZDAaDwWAwgc5gMBgMBoMJdAaDwWAwGEygMxgMBoPBBDqDwWAwGAwm0BkMBoPBYDCBzmAwGAwGwxARqwLG/QARQaVSged5mJmZQSxmx6MyGIz2odfrUV9fDwAwNze/Z47gZRo6475AoVDgrbfewpNPLsKNGzdYhTAYjHbB8zyOH/8ZTz/9FNat+6RBsDOBzmB0EzU1Nfj552O4cSMK7u5urELu0DTy8/PATlFmMFpHQkIC/vnPf0Kr1WHRoidhZWXFBDqD0Z2kp6ehvLwUwcEhsLOzZxUCIDn5Jj788ANs3rwZPM+zCmEwTFBYWIj3338PcrkcH3zwATw8PO6p8jGBzrgvyM7OgZmZOQYOHAQLCwtWIQAOHTqEr776EsHBwRAI2FDAYLREfX09Nm3ahIqKCnz00Ufw9va+58rInOIY//Oo1WrEx8eBiODv7w+RqPXdnogamaNNCb4707dXSLYlzzvTcRzX6F6O48BxnNH0CoUCN2/ehFgshlQqa/Tb7XuM/Z+IjD73Tg3f2O/tLa+xe1tTnq56bnO09f1bWz9tKWNb3rm179KZfa8tfd1UPbVU562pY2PlNHWfXq/HrFmzsHjxYsjl8ja9W3dNmJlAZ9wHAr0eUVFRUKvr4ePj0+pBLTs7G9euXcOVK5dRUVEBBwdHjB49GsOGDYOTk5NB+sTEBJw+fQapqSkQiyUYP34cRo8eA2tr61blqdfrkZ6ejtjY2L/yrISjowPGjBmLsLAwgzwBYNu2bSgpKcaMGTNRX3/rPePj4/6yRgzE2LFj4ejo2JD+woULOH/+HOrqlLh27Rp4nsePPx7AmTOnAQDTpk1Dnz59UVZWhm+//QZCoRDLly9HYmISzpw5jczMTDz77HMICQkBEaGkpARxcXE4f/48iouLYW5uhpCQEISHj4S/v7/B4BgVdR3nzp3HgAGh8PHxxcmTJxEdHQ2VSgl/f3+MHj0GgwcPNvAa1ul02LRpE+rqFJg9+1FoNBqcPXsGsbGxePDBB/Hww9MAAEqlEjdu3MCFCxeQnp4OgYBD7959MGrUKPTp08dgMpebm4vvvtsOHx9fPPDAA7h27RpiYmKQn58HPz8/hIeHIySkv9FJIM/zKCwsxLVr1xAVdR25ubmwsrLGyJEjER4eDjc3t0bvX1paiu+++w48r8eyZcthb2+49BMfH4ejR4/C3d0dCxYsBMdxqK6uxtdfbwERYdmyZUhPT8eZM2eQlpaGxYsXY/DgMOTkZOP48V8QHx8HgEN4eDjGjx8PZ2fnVk8i8/Pzcf36NVy9eg1FRUWwtrbCyJGjEBYWBplMZjDBOH78Z0RHR2PKlCmwsbHFpUuXEB8fB41Gg5CQ/hgzZozBfXdOsuPi4nD16lUkJyejqqoK7u5ueOCBCRg8eDB++ukwSkpKMXXqVPj7+zfKNy8vr6GcxcVFsLa2xogR4Rg9ejREIhG+++47mJubY8GCBbC0tGy4T6FQIDo6GteuXUVmZiYUCgV8fHwxadIk9OnTG9u2RYKIx9y5jzX61nQ6HZKTkxEVFYUbN26gsrIC7u5SjB49GuHh4Q153Eaj0SAq6jr++OMPJCenQCp1x/jxD2DYsGFtUibaqw0wGP/TpKSkkFTqRoMHD6KSkhKT6Wtra2nz5s00YEAoeXjIKCDAn0JDQ8jDQ0YeHjKaNGkixcREN6TneZ6OHz9OgYEB5OPjTWFhgykgwI+8vDzotddeo6qqKpN5VldX0+bNm6hfv74kk7lTYGAAhYQEk6ennDw95TR16lSKj49vdE9lZSVNnTqFAgL86MknF1FAgD/5+nqTn58vSaVu5OEho2nTHqasrKyGe/bu3Uu+vt7k5eVJcrmUPD3l5OfnQ35+PhQWNohSUlKIiOj69etkZ2dBU6dG0CuvvEy+vt7k5uZKvXsHUWpqCmm1Wjp16hRNmjSRPDxk5O3tSaGh/cnHx5tkMnfq3TuIjhz5ifR6fUPeOp2O1q1bRzKZO82YMZ3CwgaRXC6lwMAAksulJJO5k5+fL3300UekUCgavWtWVhYNHTqEQkP707/+9U8KCupF7u6u5OhoS6dOnSSe5ykhIYFmz55F3t5e5OXlQSEhwdSrVwDJZO4UEOBPn376KdXX1zd67qFDh8jV1Ynmzp1LU6ZEkIeHjAID/cnDQ0YymTv5+/vS5s2bSaPRNLpPpVLRd99tp8GDB5JcLiUfHy8KDu5L3t636nX06FF06dIl4nm+4Z7Y2Fjy9/elmTNnGDzvNl999SXJZO60cePGhnsTExPJxkZCEydOoFWrVpKfnw+5ublSYKA/xcXF0qVLl2jQoIHk6Smn/v1DqG/fPuThIaO5c+dQbm6uyb6nUqlo//79NHJkOMnlUvL19aaQkGDy8vIguVxKI0YMp7Nnz9Adr0I8z9Py5cvI01NOS5YspuDgfuTl5UEBAX4kk7mTXC6l4cOH0dWrVxvlxfM8ZWdn0TPPPEN+fj4klbqRp6ec/P1v9VkvLw+aO3cOjRgxnAIDAyg7O7tROX/4YS+Fh48guVxKHh5SCgrqRV5eHiSTudPYsWNo7dq15OrqTMuXL6O6ujoiItLr9XT9+jWaNu1h8vLyJKnUjXx8vBry9/f3pWXLlpK3txfNmDGdysvLG/IsLS2ld9/9N/Xu3euv79KfgoP7NXw7Tz75ZKPvS6vV0ubNm8nPz4f8/f0oLGwweXl5kI+PF61bt460Wm2XjnVMoDP+5zlx4gS5u/ekpUuXGAiKptTV1dHatWtJLpdSRMRk+u677yguLo6Sk5PpwoULtHz5cpJK3WjatGlUXV1NREQVFRU0YEAo9evXh44fP07p6el05cqVv4TWYDp79qxJYf7GG2+QTOZO06Y9TPv376f4+Hi6efMm/f777zR//nySy6U0f/78hjyJiJKSkqhv3z4kl0tp/PhxFBkZSbGxMRQbG0s//LCXhg8fTnK5lNaseZ10Oh0RERUUFFBcXBz961//JKnUjd566y2Kjo6m2NhYSkhIaBB427dHkru7K8lk7jRp0gT6+ustdOnSJfr999+pqqqKDh48SAEB/jRo0EBat24d3bgRRSkpKXTjxg368MMPycvLgwIDAygmJqahvBqNhubMeZTkcin5+/vRq6+uovPnz1N8fBxFRUXRhx9+QIGBAeTu3pP27t3bqI4uX75M3t5eJJdLaejQIfTRRx/RH3/8QefPn6eysjKKioqioUOHkJ+fD61cuZIuXvyTbt68SfHx8bR161YaNGggeXl50KFDBxsJ2c8//4xkMnfy8JDRiy++QOfOnaP4+Di6evUqrVmzhmQyd/L0lNOVK5cb7qmvr6fPP/+M5HIpjRo1krZs2ULR0dGUlJRE165doxUrVpCnpwc98MD4RgJ1z5495O7ek955551GE507hd0rr7xM3t6edP78uYbr+/fvJze3W20xbtxY+vLLL+nixYv0+++/U35+Ps2dO4fkcint2rWTkpOTKTExkZYvX0aBgf4UGRnZYt+rr6+nDRs2kIeHjEaODKetW7dSTEw03bx5k6KiomjNmtcb8s3JyWm4r6qqiiZOnEByuZQGDhxAX3zxBV2/fp3i4uLoxIkTNHPmDJLLpTRz5kyqrKxsuC8jI4MiIiaTVOpGDz44iXbu3EFXr16luLhYOnfuHL344ovk4SEjuVxKY8eOaeiPKpWKPv10HXl4yCgoKJA++OB9unTpEiUkJFBUVBT93/99SgMG9G+YGH711ZcN7Xzp0kUKDu5HMpk7zZ8/nw4ePEg3btyg2NhYOnr0KD3++OMkl0tJLpfS6tWvNdxXUlJCS5YsJrlcSnPmPEpHjhyhuLg4unnzJp09e5Zmz55FMpk7rVy5oqGcSUlJJJO508SJE+j8+XOUnp5OJ0/+SoMHD6LRo0dRXFwcE+gMRnvR6/W0adMmkkrd6LPPPjOZft++fSSVutHcuXMazbxvU15eTk888QTJ5TLat+8H4nmeoqOjycNDRkuWLG40YUhMTKSUlBSjg/edg/jWrV+TXC6lBQsWNNJI7tQSpk6dQq6uznT48OGG67/+emuiMnPmDKMDxZEjP5G/vy8NGjSQioqK7tCUtfTPf/6DpFI3OnbsmMF9Op2O1q5dSzKZOz399NONBvJbmmYM9enTm8LCBtOZM6cbCcjbgvv9998jqdSNVq9e3TDYFRcXN2iQBw4cIJVKZZDv999/Tx4eMho/fhwVFBQ0/BYZuY1kMnd68MFJFB0d3ahOi4qKaPr0aeTj4027d+8itVpt8E6HDx8iLy8PmjDhgQaLiUKhoMWLnyR3d1fauHGjwWSvtraWli9fRjKZO23Zspl4niee5+nEiRPk4+NNERGTKS4u1iAvhUJBS5cuJXf3nrR58+aG6++88w7J5VI6evSo0b5QUlJCERGTqU+foAZLiV6vp//7v09JJnOnJUsWU0ZGRqP6rq6uosBAf+rXr2+jNs7JyaHo6OhmLQG3+95vv51qsDrduHHD6Lu89tqrJJW60fr16xuux8fHU9++fWjo0CF04cIFgz4QFxdHAwcOoMBAf4qKiiIiIqVSSS+//BLJZO60evVrlJeXZ5CfWq2mf/97LUmlbvTmm2+QVqslnufp119/JblcSmFhg+nkyZMGbazX6+nChQvUr18fcnbuQadOnSIiosLCwgYr0rp164xayyoqKmju3Lnk7t6Tdu3a1fC8zz//jKRSN3r55ZeorKzM4L60tDQaM2Y0+fh40aVLF4mI6JdffiGp1I0++OD9RmmjoqIoOzu7xbGgM2CurYz/aTQaDa5duwatVgMfn5a9UsvLyxEZuQ1isRivvLICnp6eBmkcHR0xZsxo6PU6XL58BTzPw83NDebm5rh+/ToSExMbnHWCgoLg7+/fokNMSUkJvvnmGzg5OWPVqlVGt8E4OTnh0UfnQCKRICYmuuF6TEwMBAIBHn/8CfTt29fgvgEDBkIu94BSqURZWWnD9ZqaWsTExAAgo/nV1NTg2rWr4Hkezz//fCMHII1Gg71796KiohzPP/88xowZa7BGKhaLMXPmI6isLMTJkyegVqsBAJmZGaipqUZYWBgiIiJgbm7e6D6hUIjJkyfDy8sbMTE3EBsb2/BbQkICRCIRXnzxJYSEhDSq099+O4XLly/h0UcfxSOPzIJEIjF4p2HDhsPfPxD5+fnIyclpWG+/dOkSevRwwpw5cwz2E1tbW2P06NHgeR75+QXQ6XRQq9XYuvVrCAQCrFy5En379jPIy8rKCrNnz4JIJEJ8fDx0Oh1qamqQnJwMsVjSbByEysoKZGVlQSaTQyaTNZTx4sWL0Om0ePHFl+Dt7d2ovgUCIfz8/FFSUoQLFy5Ar9cDAORyOUJCQlqMiKjRaPDNN9+A4zi88sor6N+/v9F3mThx4l/r+/FQKusAAEVFRaiursIDD9xaG27aB273faVS2RB4JS4uDt9/vwseHp545plnIZVKDfKTSCSwt3cAAISGhkIkEkGpVGLjxg3geR4vvPAixo0bZ9DGAoEAffv2hYODI4RCAXr16gUA+OOP35GUlIixY8dh2bJlsLOzM8jT1tYWYrHoL3+L3gCArKwsREZGwsnJGa+88gp69OhhcJ+vry9GjBgBrVaLxMREEBEcHBzA8zx+//13pKenN6QNDQ2Fh4dHlzvHMac4xv809fX1iI6+AUtLKwQG9moxbVpaGi5e/AO2tvbYt28fjh49YiQVh5SUZAgEAiQl3RLePXr0wBtvvIk1a1bj2WefwcqVKxERMQW2trYmy5eSkoKiokJYWVljx47vDITcbW4PDpWVldDptOB5QlpaOiQSCWQyqdF7BAIOAoEAYrEYFhaWjQR2XFwsvL19jXrr1tTUIDY2Br169WoQLLdRKGpx7NgxCAQCnDt3HqmpaQAMg9IoFHXo0UOK/Pw8qFQq2NraIj+/ABqNBoGBgc2+p6WlJYYPH4HU1BQUFRUBAKqqqpCamgorKyuDSZZWq8Xly5chEomQkZGJd9/9d7NOjqWlJairq0NtbS0AIDs7G0VFBZg5cxYsLMybqUMhAIKdnR2EQiGSk5ORmJgIgHDo0CGcPXvW6H35+fkgArKzs1BdXYW6OiVSUpLh4OBgdKJ4u40rK8sxZcqUBoGlUCgQExONwMAgowLQysoKzz33PJYvX4o1a15HYWEh5syZY9SBsinZ2dmIiooCxwlw7NjPuHDhgtF0hYVF4DgOCQnxqKtTwtLSCteuXf1r0jjAqNPb7X4nkUgaHMESExMgEokxe/ajzXqJ19fXIzk5GRzHoWdPt7/uS0RsbCyGDBmKyZMnNysUq6urkZSUgCFDhjU4qp05cwYcx2HWrNmwsbExel9tbS3S0tIgk3k0OCpevXoVhYX5cHR0wqZNmyESCY2OBdeuXWuYrBARgoODsXjxEkRGbsOzzz6Dl156GePHj4eZmRnzcmcwOkpWVhZKSorRu3cfODg4tJg2MzMTZmYW4HkeBw7sazGtWCyBg4MjOI6DUCjEo4/OgUgkxtq1/8Krr67CyZMn8eqrryEwMLDFrTfZ2dl/aXDV2L+/5TxFIjGEQhE4ToCysmKkpqbA2toGMpnxwbGmphbFxUVwcXGFi4tLo0lETU01pk59yOhAk5KSAqWyDqGhAwwEb1lZOYqKCiAWS3D69KlWlFcIiUQCnudx+fIl8DyPgQMHmahbMQBqGETLysqQnZ0NV9eeBoKgqqqqYbJz5colXL16uYUnc5BIxBAIuIb2Njc3R0hI/0YTnjs9qktKigFwkEqlEAgEqK6uhkJRC57nm5nw3fn+IlhaWkIkEiMvLxd5eTl48MEIWFsbFyxJSTchFIowaNDgBqGVnp6G6upqTJr0oIE3NXBri9WECROwefPXWLv2Hbz//rs4ffo3vPzyywgPH9li3ysqKkJ9veqOdyET/d0BQqEQGo0GyckpsLCwaHZyUlFRgaKiYtjZ2cPBwQFarRZZWdkg4iGTyZoVyrW1tUhMTICFhQV8fX0bnqXTaREQEGBUU75NamoKRCIxBg0aBFtbW9TV1aGwsBDm5uaQSt2bvS8nJwdVVZUYNGgw3NxuTSIKCvIhEolRV6fAnj27Tfbz2+UyNzfHypWrYG/vgA0b1uOpp5ZizpzHsGLFSri7uzOBzmB0hIKCAhAR+vbta3SbUFNtj+M4PPLIbKxevdqo6bbpYHp7e5WFhQUee+wx9O/fH5s3b8Lhw4eRkZGBr7/+Gn5+/kbvJyKo1bfMkXPnPo7XX3/d5PsIhUIIhUKUl5cjIyMdAwcOanZrUmZmJioqyjF27LhGwXSysrIgFkswYMAAo5pydnY2RCIxgoODDQS+Wq0Gx3Hw8/PH119/DUfHHibLbGdnB51Oh5iYGNjbO7Q4sOl0OkRH34BOp4OX160lkry8PJSWFhvdIqTT6aBQKCASibB79x7RMj2ZAAAgAElEQVQEBgaaLI+lpeVfZtIE6PV6eHp6GhV8t7bARUOjUTcsTRAReJ7HkCFD8dVXm0weysFxHGxsbJCamgqOEyAkpL/Re9RqNdLT0yEUiuDhIW8oT25uLgQCAYKCgpoNiCQSiTB58mQEBwdj584d+Prrr/HKK69g/fr1GD58RLNl43k9iAijRo3BJ5980qr+bmNjg8LCQqSnp8HOzh5ubsbbsqSkBEVFhfD29oZUKgXHcTAzk0AgEECpVDabR0VFBWJibmDSpMkN5vHb6VuqayJCTk4OOI6DTCaDUCiEWCyGWCyGVquFRqNp9t68vDzU1dXB09Ozob/rdDoAwGuvrcbcuY+Z3FMvFAobJikODg54+eWXMXJkOD799FP88MNeZGVlYdOmza2ynDCBzmAYQa/XIyYmBjyvh6+vr8kT1pycekCn0+LmzSRYWlo2a6IzZs4VCAQQCATo06cPPvjgQ7i5uePLLzdg586d+Mc//mlUI+E4Dl5et9ZECwryYWVlZXJQvU1iYiLU6noMGDCw2YEuMTEBHCdopPHV19cjISEBOp3WqHalUqkQExMNrVYDDw/D393d3WFuboEbN66CCCatHv8dNHNRUlICb29v9OzZs9lB+fz584iLi8WQIcMQEBAAAEhIiAeARu9x52TB398fmZkZqKysbHV5FAoFrl+/DqFQ2OwkQKVSITo6Cu7u0ob4BRYWFjA3t0BVVRV0Ol2rB+jy8goIhUK4uroaba+KinLEx8fBwsK8YQKo0Whw48attvD29mm2790OjiKTyfDqq6/By8sbb7zxOj7//HMMHhzWbL93dHSERCJBamoKVCoVXF1dW/ku5cjPz0NY2JBmJ5O3td6goAiYmZlBIBDAyckZt5asUqBWq41ah65fvw6JRIJRo0Y1TDatrKz++kYKoFQqjVoqNBoNrl+/jvp6JYKCggDcWo93de0JrVaLmJhYhIUNMRDMer0eFy/+CZ7nERIS0nDd1tYORDxSU9NgZWXVapO5Xq+HUCiESCRCWNgQfPnlV1izZg1OnDiOkyd/xWOPPd6lYx5zimP8Twv0qKjr0Ot5BAX1Npk+NHQA/P0DER8fh2PHjkGr1RrVAhQKRcP/4+Li8OWXG1FSUtKQzsrKChERERCLxUhISGjx4JOAgAC4urri/PlzOHHiF4OY6jzPIyMjw+BEp4yMW9pcSEiIUQGh0WiQnp4OkUgEX1+fhoFMrVYjLS0VSmV1wxplU00xKioKjo494OfnZ/C7lZUVpk+fCWtrO2zfHom6ujqjA35hYWET824xqqqqoFarUVtba1AnPM8jOTkZ//nPR9Dr9Zg79zE4OztDp9MhLS0NIpEYvr6+BgOypaUlBg0aDL1ej02bvmpYd2/6Tv/f3nlHx1Vdi/u700fSqGtmNCpWteUqG4MLmGLAlICBAAmmOLz3CxB4TkKABMgjgVASQhJqEpKQAAYC5NEMxhhsggFj44JxxUWWbFnF6l1TNeX8/pA8nhmNpJFkmZLzrWXW4mpuOefuu/c5++y9T2VlZdg929ra+OKLnUyePDWs8E4ohw5VcvhwLdOnzwgakaKiIoqKiigr28fy5cuDM7lIV3ZbW1vYseTkZAIBP9u3b+83W/T5fGzYsJEDB8qZPHlKsBCR1+tl69bPSU5OCSuuckT2amtrWbp0aVjwlVqtZt68U8jMtNHQ0NjvOUIpLh7PxImTqK2t4a233ooq7w0NDTQ3N/cbKLpcLkpLpw/oOj8S0Dhz5szgOzvttNNIT8/gueeeZfny5djtdrxeL16vF7vdzooVK3j00UdQFBVW69GiPIWFhZhMibz//mo2bdrYT3b8fj979uzhk08+ITc3n3Hj8oJ/u+SSSzAa43j66adZu3Ytbrc7OGNvbW3l6aef5rXXXiM+PoHi4vHB8+bOnYtWq2P58rf4/PPPo8prZWVl8LvsHRhs4E9/+mPQo6AoCmlpaVxxxRUEAoHgOvtYov7Vr371K6n6Jd9EmpubeeKJxxGi16DX19ezf//+fv8qKyuxWq2kpKRgMBh4//3VrF+/Ho1Gg9VqRQhBe3s7q1at4qc/vZWGhkbmzJmDoij85S9/6fuIXZSUlKAoCnZ7NytXvsvatR9z8cWXMG/evAFddiaTCY1Gy4cfrmHdunXExcWTkpIMKLS2trJixdvcdtutOBxOZs6ciUajoauri6VLn6WmppobbrghqmFubm7iqaeewu128z//syTovuzp6WHZsmW0tbWTk5NLTk4OXq8XlUqFRqOhoqKcJ554jFmzZnP55d/p55LXaDRkZGTw7rsr2bRpIx0dHVgsFnQ6HXa7nc8++4z777+fd99dybx584JejhUr3mbt2o9pbW1h48aNpKSkBF3xTU1NrFy5kjvu+BlVVYdYtOhKrr/+BvR6PS0tLfzjH3/H4XCyZMmSqFHKNpuNLVs+Z/v2bZSVlWGxWDGZTLjdbsrLy/nLX/7Cvffew+zZs4Pu/m3btvHqq69y1llnc/7534pawWvDho2sXr2KSy+9jNNOO63PbawnNTWFlStXsnHjRnw+LxaLBZVKRUdHO2vXfsIdd9zOvn1lzJ07Nziz8/v9vPnmm+zZs4eMjAxsNhter5eamhpeeulFHnjgPoQQLFp0FXPnzkWlUlFVVcUTTzzOpEmTueKKRf1mpsuXL+fuu39BRUUFU6dORa/X43K5WLNmDStXvsO0adP47nevGNCDo9FoSE1NY+XKd9i0aSM+n5+0tDQ0Gg12u51169bxs5/9lN27dzN79uzg/V9//XV27tzBdddd32+gcUTGXnzxn9TUVHPdddcHg/nS0tKIi4sLDl7Xrv2YgwcrWbv2Yx555BGeeeYfOBwOfD4fixcvJi8vL+iFAVi/fh2bNm0mOTmF1NRU/H4/9fX1LFu2jDvvvJ329nZmzpzJpZdeGpTbnJwcGhsbWL/+E95+ezmbN29m7959rF69igcffJAVK5bj9XpJTk7myiuvDK6FJycn43K52bRpAx999BFJSckkJycjhKClpYVXX32FW265GZMpkcmTJ+P1evnFL+7iX/96ORi8KYSgra2NpUuXsm/fXhYvXhw1K+KYIjOVJd9UNm3aKAoK8oPFJrKyMqP+u+qqq4I5yE6nUzzyyCMiJydLZGVlivT0ZFFcXCiyszOFzWYRJSXjxQMP3C+6u7uEEEJUVlaKSy65WNhsVlFQkCcWLrxQzJx5gsjMtIhFi64Iy6UeCLvdLv7whz8IiyVdZGVlitTURDF58kRhs1mEzWYVEyaMF48++kiwqExNTY2YMWO6OPHEE8KKdoSye/dukZeXKy66aGFYdbSenh7xxz8+IdLTk0VWVqZITo4XBQV5Yt++vUIIIVasWCFsNqt44IEHgsVoouWpr169Wpx44kyRlZUpzOY0UVRUIAoK8kRmpllYrRliyZIl4sCBA8F85yVL/kfk548T119/XV+VN6swm9PE1KmTRVaWta+dxeKhh34rWluP5vyWlZWJoqKCfu2I5LPPPhPnnLNA2GxWYbWaxbhxOWLSpBJhtWYIq9UsLrpoodi8eXMwl3zp0qV9eeJ/jXo9r9crHnjgfmGxZIg33ni939+WLl0qiot7q6KlpSWFvC+LyMnJErfffrtoa2sLy69+8MEHgzKXmWkREydOEOnpyeKUU04Wp512qsjOtonly5cHz1mz5oN+hYEi86d/8IMbhMWSIQoLC8S5554jzj77LJGdnSlOO+3UqHnl/d+lVzz//POipGSCyM7OFBkZKaKwME+MG5ctMjMtIjc3W/zv//5cNDU1BgvKLFq0SBQW5ocVDQrl8OHDYv78M8QJJ8zoJ/9ut1u8/PLL4sILLxAmk0YkJRlFamqiuOCCb4lnnnlaXHHFFcJqNQfzyENrMdx0043CbE4TWVmZwmrNEMXFhcJqzRCFhfli/vwzRHa2Tdx33339nqe9vV089tijYs6cWcJgQCQm6oXVmiEWL75GPPvss2L27FmitHSq2Lt3T7/z7rrrLmGzWfp0QUrfPc3CZrOKqVOniL///amgXO7atUvMn3+GsNksYtq0KeLiiy8SU6dOFhZLurj55h+HFYUaKxQh5EbIkm8m69Z9wqpVq4b8XXHxeL73ve+FzTB27drJO++sZNeuXTgcdtLTM5gxYwbz589n0qRJYWvdjY0NLFv2Jp99tpnGxkZyc3OZOnUql112GWZzbOuSHo+HTZs28emn69m5cyddXV1YLFamTJnCggULKCkpCc4i9+3bxz//+QJms5kbb7wp6rr7+vXreffdlRQXF3PNNYvDZmkOh4M1a9awd+9euru7MRj0/PSnP0Or1bJixdts3ryZ008/gwULFgwaN1BeXs4HH3zA1q2fU1dXh8lkYtKkyZx88snMnTs36Dru6OjgyisX0djYwEsv/Qu3283q1b3uU4/Hg9lsYcqUyZx++hmUlpaGzZY3b97E8uXLo7Yjmqv7/fffZ8uWLRw4UIFOp6O4uJhZs2Zz6qmnBiP9fT4fzz23lMrKSi666GJmzZrV71put5snn/wzbW1t/Nd//Xe/5Qe/38+uXbv6atFvo7m5GYvFQmlpKXPnnsyMGTP6zagdDgerV6/mo48+orx8P3l5+UyZMpkFC85h3brevOVrr72W4uLxCCFYvXoVn3yyjtmzZ7Nw4cKobW5vb+f999/nk0/WUlVVRWpqKtOnT2fhwoUUFhbFHAPS25bVbNu2nfb2NjIyzEyePIl5805lxowZwYC8xsZGnnqqd7vdW265NWpqZnV1Nf/4x99JTEziJz/5SVTvR0tLC21tbXR0dJCcnExqagpqtYZFi65g69YtvPvuKk488aSwczo7O1mz5gM+/PDDvowUPVOmTOGcc86ltbWVjz/+iPPOO5+zzz47anzGkaUQt9tNUlISGRkZNDc3s2jRFWg0Gv71r38FAzFD4yjWrl3L2rUfU1ZWht3eTVFRMVOmTOWMM86gqKgo2D4hBFVVVbz99tts3LiBjo4OiouL++rqnx1zfMdokAZd8o0lEPATCAwt3qHR6pFrmw6Hg0AggEajISEhYdAdnBwOB16vF71ej8FgGFERiUAggN1ux+/3o9VqgwFBkb85Egw1kIGL5TdH2njE/XrEUAkhgkF+seBwOOjp6UGtVhEXF99Pge/evZvvfvc75Obm8vrrbxAXF4cQgq6uLgKBwIDtHE47Io2xy+VCURTi4uKiDniOtFOtVg/4To/0zWC/EULQ3d2Nz+dDq9UOKiNHn8+Fy+VGr9cHjX60ew3nXbhcLjweDxqNmvj4hBHttBYICOz2bvx+PxqNhvj4+H73FUIEi9cMtNGIEAH8/gCKAmp17HHXW7du5bLLvk1qahrvvbdqwIC73ra6URQV8fG98ubz+YLPHaucALz11pvcfPOPOfHEk3jhhX8OmEng9XpxOp34/X6MRuOgWzAfkYkjvx2o5sJYIKPcJd9YVCo1oynMpNFooq7ZDjQoiHVXtcGfWTVkQZpYFHysBjlSKQ9HGR4hPj6+X5W1UOrr6+ns7OCEEy4JKkJFUWLq2+EMLI5gMBiGVKKxtDOWnbEURYmpgFD48/VGyg91r+G8i6GMTGyyN3RbFEUZsl8URYVGo+o3OFqx4m3Gjx9PScnEsHcaCATYt28fDz30W7xeL1deedWgs9lobdVoNP2ey+Vy8corr7BgwYJ+O9/19PTw2Wef8cgjvUF4V1yxaND+02q1w9IFw5UJadAlEsnXgq1bt6JSqZg9e86I9+iWfL3ZsuUz7rjjdpKTU5gzZw4nnTSLrKwsmpub2bZtG//+9/s0NjYwf/6ZLF68+JhsM/r228u59957eOGF55k9ezYzZ55IcnIyhw/XsmPHDt577z26u7u48sqruPDCC78R/Sxd7hKJZMzw+bzcdNNNfPTRh7z44stR16sl33x6C6v8lQ8++DdNTY1h6VuKopCRYWbhwoX84Ac3DlinYLhs376dJ598kg0bPqWzs6Of98NqzeTaa6/l6quvibnmhDToEonkPxaPx8POnTvx+31MnDgpZrel5JtHbx2HKqqqqqioqKCrq4vExCRycnIoLCxg3Li8ES35DIbf72ffvn3U1dVRWXkQp9NJamoa+fn55OfnYbNljfmGKdKgSyQSiUQiGRayUpxEIpFIJNKgSyQSiUQikQZdIpFIJBKJNOgSiUQikUikQZdIJBKJRBp0iUQikUgk0qBLJBKJRCKRBl0ikUgkEok06BKJRCKRSIMukUgkEolEGnSJRCKRSCTSoEskEolEIpEGXSKRSCQSadAlEolEIpFIgy6RSCQSiUQadIlEIpFIJNKgSyQSiUQiDbpEIpFIJBJp0CUSiUQikUiDLpFIJBKJRBp0iUQikUikQZdIJBKJRCINukQikUgkEmnQJRKJRCKRxILmy7hpm9dNvdtOmauT9h4XakVFgkZHviGBHEMiaTojakUZ8PweEeCgowN/wD/iZzCqteTHJxN6l2aPk6YeB4ijxyxGE+laQ/j9AwEqHO0IEeg9oECy1kiWIWHI+3oCfiocbWH3MGn15BgTw57FKwIcOAZtLIhPHvX7qnJ2Yvf1jPh8tUpFQVwKOpUKd8BPpaOdgBCjeyhFwWYwkaLVD/vUCkc7Hr8v7FiKzogthvd39N204w8Ewo5nGk2kRsjKUKzvqKelxxX8/3i1ltNSbOhU6pivYfd7aXLbEQzepwoKBkWFolKhV2tJ0RpQhrh2t6+HGlcX4sj7UsCsTyBDZxzyubp8PdQ4O8OOZejjMevjwo45/F6qnJ1H7zECUnVGMmN8f6G0e93UubpHJYpDvfcuXw/17m72Ojtp97oAhQSNFqsujmJjEsk6I4ZhvO8j19vlaKfb50EA8Wod4/Tx5Mclkaw1oB/G9SJlu9njpMzRTr3XSXePGxAYNXqy9fFMjE8lZRjPKxAccHbi8XmDxwxqDflxyagUZcjzy+xt+EJ0oFalpiA+BU3IuQI45OzEGaKjdGoNBXHJg9qRI5Q72ukJ0Qeh+ir0HrWubrq87pHPnhWF/PiUYb3rr7RB7/B5WNVUydKWA7zncfR1kxLSZTBZY+DyhHS+lVFAaaI5qmB2+Xq48cCnfOxxjPhZfmay8OuS09AqR1/axvZaLqreFva75fmzWJiRH6HkPEwuXwshAnS2IZG/j59HnsE05GBmyr4PIUR5/S51HLcWzg4TPoffy7UHN7DZbR9xG28zZfCHiWeO+r39q3YXd3YcHvH5J+niWDHpbMw6Iy1eN1eXf8K2UQwQjhj0jwtP5rTU7GGf+vChLfzV0RZ27Lvxqfx5/Kn9Bm/R+LjtMN+u3IQ9bLAlWJ0/hwUZeTE/R1OPizsqN7PeF6IkFBV7dWdSYkqLfcDl7uaEPR/QIwIx9Bug0rBAa+A0YwoXmAuZZEof0AAccnUxrewjCGnrdYkWfls4l7QhBlPVjnamln0Y8o3DmznTuThzQvjv3HbO2v8xDRGDrOHwkqWEK8eVDvu8HV3NzD+wPuwZh4fg3wUnc1Z6br+/OAM+3m06yKvNlfyfuzOqvrOptVwel8q5abmckpJNkkY34J2cAR//bj7ES80H+D9X5PV6rzlOrePbcSlclJ7HzGQbiYNcL3KStLOriTcaK1juaGG3zxOlTwRZah2XGlO41FzASck24tWDmxCfENxTtZWXupuDx2xqLf8qmMO8FNugvS6E4NaDG1npPjrgutSYxNMTzyQ5pF1+EeCx2p080VEXPJaoUvNG/mzOTMsZ/B4I7jy0hTdC9EGovgp9ltcayri1+cCIZXSqWsuKKeeSq48fMxt73FzujR4nP9u/nkWHd7IqaIiVCE2jsNvn4d6Ow8yuWMed+9dx0NkRdd6hQQFl5P9USnRdp4743UDCkBnxu7Webh6v3o7d7x2yLywRz64MMIrUjbaNI1ZS/ftlNM+hVY5tu1AUElFG3DpVlPu/4mxnWWMF/iFmiY09Lp6s34NdBPpdQxnmA33aVstWX09Q9kEBIXi7qYIAYlhvyBpr36FAwM/7Hge/7KjlhPK1/PrAJtoGmXlYIq7xj+5mXqzfizeWAUSkrEd5a4oCCaOUCWWEwqAoo5fFaPe2+708cnALl9fs4BV354D6rs7v44nuJi44tIUb9q5hU3tdVO9Vt9/LHyo/5+KabX3GnCgGV6HK7+Wx7ibOrNzMkn0fsamjnqHe0mG3nfvKNzCvfB0PdtaxOyiT/d/lYb+XP9qbmH9wE7eWfcyhCA9MVA9dxLutC/h4oGYH1e7uGM4N1z3qAZ4sUm93iQC/qt1BubNj2PfQKmOjB3WKMuZ29rgYdJffxy8PbuQfjpaQselQA1/BY92NPFi1FV8gwFedHuC5rgaer9+Hb7TuZMmXwh+bKyhztA8623i1YT/LXJ2j/yYCPl5pOYgrytfwUlcDDW7H8Wm0ENzfUcsvDmykO4bBaK9iEzzUfIBVLdXDHHj8Z+AXgmdqdvHL9uq+OWBsvOLu4mfVW2mJGFx5RYB/1OzinrYqlGHoln+6OvhV9TZaQ5Z0Itlnb+XG/Z/w687DeMRw9KzgKUcr3y/7mE/b64bdR6s93fyhajudo/XUDcK6Hie/q9pGm9fzHyN7x8XlvqWznlfsrWHHUlCYqDMyXp+ARwSodHdT5e+hPkRg81QafpQ9Fa1q6HGHCchV1DHP2kJd7ceKdiF4vLGciXEpzB+BK7j/JxNOPJD3JbcRwACMU1RoY3ySBEUdZg40itLr4Yg0mEBzhMJKRCFeiT7qP9bj3V2+Hv5yeDe/LZob1ZW4x97Kn1oOHpN77ehsYrW7K+rftvs8rG2rYZFt4oivn4FCiqKEyVAAcCFoFYJIFfdidxPnNFVySeb4mOSyLuDn14d3UhSXREl8yjGXsQwULMOQX/UxnP3kKwrxMc51vIh+krjP2c4TrZURcgwTtQZKDIkIBBVuOzU+D/UiwJGFhnhF4ee2yf3iE6qdXfym9SBKiE6IB6ZqjRTpE9ApCgc8Diq9LupFAG/Qi6hiibVkwHiHSlcXPzqwkX/3OPrNRFMVhSxFTZrWQADo9Lmp8vtpj9BKa3xueg59xpOaU5hqSh9WP7/Q3cDEhjJuzJoS03r6SPg/ewsT6vbwk9zSMdGH4xVVr8cxBhIVFWM9Rx9zg+4XgnfbaukKEwSF32dO5LuZEzD2rd35RIBd3a280VjOsq4GykSAe6wTmJyQHlWh+CIE6xR9PC9MPCvmDtMoqjF5wfsDPu6v2UGWIYHxcaMISBP92zhdH8/rJfPRxPjcY2XQC1VqXi6Zj00XF5sbSFFIUveueaVp9fyx6JSobsVDjjYur90e5lT7QUo2V5iLovq/hopXGAl/627grNZqLjbnhynqbr+XPx/eTVmMs9ihvom3WippHWS29XxLJReYCzHFuAYayZJEK/89bkaYBPlEgE6fh71dTfypqYKNvqNmvQt4sbmC8y2FMQdUbfS6ub9qK48Wn4xZaxy5qAuIDP1cnJTJXfknxTy/PZaBRo/ZpnLKMGIh4tXaMN30RWcjNRHBrHek5bEkd3pwoOgXgjJnOx+2VLO0rYrtfi+3JGczPy23nw7b2FFHS4iX0gjck57PDTmlwesFhKDc2cGqlkO83FbNLr+X7yfbOHeAdjj8Xu6t3MxHEcY8BYXz4pK5MXMi0xIzetsmeoN5N3fW81JjBW84Wgj1Y63zebiv8jP+OnE+acMICu0Ugocb91NkTGbBEGvdI8WO4I/NByiJS+bCjLwRTwNEn4cu8ujL409jnDExRs+WMmiMxNfCoAsEO93dYZ9lqdbAxZZCTCEfgkZRcVKShZmJZq60t7C6tZpLLcVRR94KfWvoocYLhTSNfsD16OPJhz0OnqjezgNFc0nW6Ed2EYV+M2ANKlI1hpg8FmOJGkjR6If18QaVkUrNjAFG8vFRXH65ujhmJmYct7Z5heCR+j2ckGgmty9qOoDgvZZDPNXdeEzucdDZwf91Hb2WHphvMPFeyJriux4727qaRhT0B5Cq1gafP5ITTBlMNZm5qvwTdgeOBqJt97qpdHUNa8b9qqOVaYf38JPc6SOOrFaUvnXMCDlJ1eq/FPlOVGtGJNtHNN4OVydhjmRFxTXWCWHKXKPAtIR0piakc7G5kFcaK7g6c0LUgUlnRPBvhqJwiWV8uHFQYHJCGpMS0rjUXMRz9Xu5LnvKgIP6T9pqecfZHvQOKIAJhfstxfx31hTiQj1UCmhVKs5My2VOso0ZNTu5r/kATSFa/TVPN5c1Vw7bq1QZ8PNg7Q4KjYkUxiWNyfusFQF+fXgXecZEpiakjVQdh0XWHyFZoxuFrBx7jotlcIvw0epOfw9Ob8+As7lppgx+mjcz5gjNryJ/7m5mad1eer4G6/+SCGXX4+Sl+n3Bd1fl6ubh+n3HaIALq1qrqAwxpFPVWn6bdxKlEYO/V5sq6BlF2uJgTEvM4Fvx4cqtQvhxD3O90Qv8vqWSt5sr5Xp63/v1RL4zEaC5xzmgociLS+L2/JlR014DCJyB8Oh/p4A6j33Q691TOIcsffQBnSvg5+mGMlpCZpwCuCU1h+9nTw035hHEqTVcl1vKjSlZ/b1KzQdpHWZalwA+6nHy2+qtwz53OPfY5HXzUPU2mgaJJ/gmMOYGXUEhN8IdJwJ+flm5mY3th49ZwEIwAvFLwhYxslYQPNRUwcdtNcdUzX0FHBDfAK0bPsgap6goVevCPoY/tVWxpasBrwjwUv0+NnnDFcG5UZSlLoYljpYeF8vba8N8L+clWpmSkMb1qeGpTy/am6mIIUp3pMRFzixE8D/hRiXC1Rgp660iwG/qvuCL7tYIY8SIs8G+XDFXRnGmQl7EUpQK+MWhLaxoPECjx8lwhvgqFBIjBnotCH5VtZW3Gito8jiHzMyIZL+9jU0hAwIFOFGj579sk2JautCr1NyUU8psdfiE690ee7++FXMAABcqSURBVL+6A9HIUFSoI9aTX+hu4YX6fUNmTgQzVIbAgEJ8xPf4oqONpw5/gfsYDpIVvloKecxd7ipF4VyThRe7m8LcUM8723m+Yh3n6U3Mj09joimdUlMGmYaEIdd+BaLf+nKd38urDeUxGbx5yZlk6hOOaTtPiUtmoi6Bh9qq8PSpxSYR4N6a7RTEJVE4zPV0IXrdv6E0+3t4rbEipgCgk5OsZI3BGjP0rn2903KI1Bg8KHnGJE5KNH8F51FHmajRc7V1Aotrd6CI3lXbwwE/T9TuYrHXw8NtVWG/X2IyMy/RwqrDu8KOxxKuuK6tlvdDZms2ReHijALUisK56XkkNR+gs0+ptQvBe82VlMSnHvOgIXfAT21EtH6WSoUmZBmsVw4FkdnhVyRn4exx8jd7S/DYNl8Pv636nCcmnB7M5ffFGDUdbQ19j7ub1xrLhxwMKyicl5ZLwjH05n3U1UhzDGbXqNJwdlpumBFUgBmmDNKaKmjte/oAsNrr4r3qLZxYb2RhfDpTEjM4wZRBuj6BhCFyuScmWqC5PGyg8ZHXxUfVW5lRt5sL4lIoTbIyM9GMWZ8wZG74ju5makLejQDON2XEvBYMYNHHc1myjU2th8LexsbOeqYnWQY9N1tr4O7UXO5tPOol6EHw+6YKJselcFZ67oAptxoltlmoW6XiWWsJd9Xvpa6vrQrwcOshpsSlstBcMKxASkF/fQwKK1urMHcPHT+SYzAxO8n69Q+KU4CzMvKY21zOWq874gNVeM9j5z2PHdoOUajWcaExiTNTx3Fyio30AaIzFZR+a+hbfB6uqNka05v5XHfqMTfoCYqaH2RPocxj5xVH76ccANb7PNx/6HP+UHxKTEVLQmfi2giB2+PzcGXNtph6/bPCk/sZ9EpnJ+4hineoVArF8SmD5rBXiQA31u0mlgTE35uLx8SgCwQVjo4hUxo1KhXFkWvCEQNGBYX5qdnc5Wjj123VweNvujpZW72V9r4PWQEma3T8IGsKdVFmIkOl/dj9Xt5sDR8cnGJIYkpfTEGOMZEbTGZ+39UQ/Ptz7TVcYZtI1jEsRuEK+Hm5bm9fsZOjTNboyYyQGeVI/mzIqx6nNbLAOp5dFZ/yacjg5E1XJ0XV2/l5/okYVZpej4UYesIbbQ39DVcHb1RvjUnWG5Osx9Sg39tRCx01Q/7uQl08J6dk9ZvVnpBk5bL4VJ5ytIZ7K1DY4nWzpe/66SoNF+hNLEjJZn5aDrYBBuAzksxcH5/B3x0t/f62zedhW1cDdNVjVmk4X2/irJQszk7PI3MAmamM4q4vTcgYlrFRgJMSzRhbDxHqu7LH4NJ2iwCXmwtp8br5feshnH2apE4EuLt2J1lGE5PiU6Oe2yNEzFUmv5WeR4ffyz2N5XT1hVd2CMG9dV+QazRxwjD0kkJ/fawCftiwLyY9eG9qHrMSLWMe43Vc0tbSdEYeK5jDzQc3stHrpmeALjvg9/K4vYXH7S2c27CX/82eximpOcMYScXwO2Xs1vnMOiN3586govwTPg+JIH7O0cr4w7u5LXf6l+oO/F3NDv7Z3TTob0rUOj4tvQBVTAFOSgy/GBsB9gvBbVWf8+EQLulzdXG8PPW8CK9P/2cyqNRcZ5vMp/ZWPuyL/PUA9SHuOYHCTy0TmGxK43AUgz7UGvLu7haedx6tSBUPLM4oCAaT6VVqFmbk8/uu+uAzlvu9rG+t5ju2icPqyR3uLpY17A8tSEgAQXWPk+32Ft52deAM+aMBuCY9f8jqb0d6YlJ8KndnTeX6qi3BiG4X8Nf2Worjkrk6ohrc8Zb143Fv1QDSHafWcE/BbFwVn/KqqwP3ANdvCfh5ztXBc64OTm7az83WiXzbWtzPQ5mg1nJvwSxcBzbwqrMdzwDXawq53pnNB/ihtYTzzYX9Bhz9iwgJsnTDz1LQa/TEKwquEDmq83rwBALohwjcNag03Jg9hT2uTl53tgf9IRu9Lh6s2sYfik7GrBtdsJlWUfE920R2OTt4vrsp6Gna7vPwYPU2Hiueh00fN+LrB4ajB4/TWqnqeH0apYlmXpl4No9bJjBLoyNpiE5Y1ePkvys383bTga9VsE1JQgr3ZE/rl0P7p5ZKVrYc+lJbEhAB7EP88wv/16avY2mPCMQuPXlGE7fZJmEeYMnnelMGl5gLR1SBr0cEeKc5PDd5gkbPnJSssKtNMZm5RH/U9ekCnms+iDswvLKoT7s6uLRmO5fVHv33ndod3NZUzgvOdjqECCokDfAtQyLnZRQM6x7z03K4NaMQU0gLmkWA39fvZUtnI//J2AwJPFlyBi9nl3K21kjGEAp9g6+Hmw7v5M9V26KuI2caEnhywum8kDWN07RG0pXBh8prvG6ur93BU9Xb+5UE1kYZrI9kb4WAEP2WSowqVdQqnNEGS1ZdHHePO4ETImIE/ulo5ZnDu/Ecg4DiVI2eu8bNYJ4uLqy/XnN18mTtLlx+/zdK7o5bLXcFsOjjuCF3GlfaStjZ2ci6zkbWdNVT7uuhKooQHxQB7qrdycxECzlGU8h4sn+OtkVRmKkb2i3pR2BQj12zVSiclz6O/3V2cHNTefB4gwhwX+1OHlJrY6yU11u0Ikw4FRWzdcaYZr1a1di10URvvWNDDEFgpq9ZpsKZaTl8v7OBB9vDXa6lah03ZU0ecR5pjauLv3XVhx0r1sWxvbspYu1dIVWrI3Qa9pnXyca2OuZHqRc+WuJR+JYxkYeLTo5agESIAO4BlL1OUfH9rMmUOzv4a3dTcICwy+/loaptXG0uimmSHW0NvUilYYLWMOS3ElAUVMe43sIcjY5U9dDvOVNrHHRGlKDRcXHmBBaYC/iiq5l1HfWs625kn9fFvojALAG0CcEDzQc5JdnGScmZUb+ly20lLDDns7e7hU2djazpaqDM62J/lECvViH4bUslc5JtzAq5Xv90QIUdznZOTssZVj81ubtxRMhGukY/ZAxUvKIKxjpNSkjl/uxpXFu1haaQa/2+pZKpcSlEVn0wKKphv+98YxK/yT2BKw98GmZn/txWw7S4FDwxDGai56HDfK0xWEtlMNI0huPicDruu62pUEjS6Dk1LZd5abn8yO/loLOTnZ0NvNFWwzJPd1hFpP0BHxvbasjOmhTsj2h56LN08Sybel5Mxm6s3R9aRcX3siaxzdnBUntzUCB2+r38rHobnbGY9Ch56FN1cSybcm5MBWOitTGWgUQs49V8lZpnJ5xBdgzrumPZ1bFMKsQwfSJGlYbrsiazwdHKR33rwyYUlliKKR1mJayjrjnBhy3VNEUo3WXOdt45uKnf7yOVWLMQvN58gNPSco5pRbQZah0/sYznIkvhgPUSFEVBO9iATa3lttwZ7C1fF1yqAHjD082eut0xuiP7r6F/N8nKA0Unx7QD27EOGPx15hTOsBTGps+GuLdCb+GZ2Sk2ZqXYuKWvAMzuriaWt1XztquDdnFUStsQvNtSxQlJ1gFrcCRr9MxNyWJOShZLRIAKZyf7u5t5o7WKFRHXqxcBVjQfDDPo04z9A3R3dTXhCfhjriXgF4J3O+v7LZ+ajUPnkvcQCH67KhTOSh/HrY527myuCP6mHcGPD++gK+L9+xDD/qYVYFZyJndbJ/Lj+j04+s7vRHBL3Rcxedyi56EL/l58Kvkx5M8ryvGJiP9Stk8N7aQEtZZppnSmmdK52Dqe02p2cktIeU0fsNnVwWVCDGmIVShficIy9H10d46bQVX5Oj7sMwwBYKfPM6oeU6GMWIHdapvMEn/xEH3Ym1ISiyJTfYl9rVYUfpcznd8EBq/cplHUUQtCDEaBMZGfZE6irHor9SLApQlpfMdSPOLNbjq8Hp5uOxRFsUGslaxXOtu40d7CFFNsRXZOV+uYbjAFVV+dz8Nyjz3sfvlaAwvNBUMUP1J6DcsgOrQgLom7c0qprdxMecjSwD5/zygkvVf9fRnfs0phTGT7SHsmxKcwIT6FCyxFnFVfxvfqvggbdK9zdxAQYsjBm9I3eZgYn8LE+BTOMRfwSl0ZP2vYE1ZCudrdjSvgD84kJyVmMF2lYXvIu/qLs43vdTQwJzUrprbs7G7mY0d4mqJeUTE3yTrkuZHR4lpFxfezJlHm6uDZkMyJyigBvD4hRrTNrlpRuMxazA5nO090Hq09Xz/KFDZljGTlK2/Qyx3t7LW3cn5GwYCVzkwaHd+1TuCW1sqw6Zcj8PVc5xgfl8wd2dM4dGgLhwK+Lz0SoOQ4Vlwb+8GgwuSksUuHOyd9HNd0NfBeVyM/zpoStl3jsLwIwKftdewcZdGMyoCfVS1VTDbFFo18eaKVHxbNCf5/a48L774PWe6xB+XwDXcXC+rLuD5n2qhn/vNSsrjVOZ5bG/aGBUn9p9LocbKmrZqFGYUkaKL7OPQqNQvNhcxqOcjmkGwBh9/Xz59W53Gwoe0wZ2fkDbjsY1Rp+HbmeN5oqWS59+j13EL0pRD2GvQ8QyJnJqSxPaRaoRq4r2YbfzOayBkifa25x8XD1dv7uflvSEgn0ziy7KF0rYE7cmewv2I96wcowjNakjQ6fppbSkV5d9iWrN8kjktQXIPHwc8PbuLi6m08cHATe+2tAxZDqPc4+vlSC7VxQ6e+AF+xHP/elL3UbG43F4cFDo12RCgZJSKWHGM1P8yexq9sUyiNcVasV/q7Kz0BP882VRCqovJUar5lSOTCIf7lhcRBKMCyjsPUu+0xDyRCSdMZuSOnFBEhQHc3lbOls2H0MwNF4ZrMCSxJsn0jhoujocvXw32HPuOq2p3cXPYx2zsbBtyBscvXQ0dEwKNZYwj70O1+L48c+pzLa7dxS9ladnQ2DZjj3+BxcDjCa2XWaEkIqS+gUhSuzZxEQYgnzg+82+PkR+Xr+LyjIWogsl8I9jnauLtiAy+6wrNL0hQVi60lGEcRuzM+PplfZk0lS6UeM1WebTBxZ04p49XaY3KP/7jCMh0+D7879Dmvu7tQgPvaa/hnZz2Xm8ycnZJFtjERrUqDXwQ45Ojg4fo9YecnANNMaWHuzmiFZer9XpY1VMTcvXEaHaenZh/TTR2iKzoVV2ZOYJernb91NhCrryFaYZkWfw9vNlTEPJuKU2s5PS3nmLexSwjeaz5EWoyzVpWiYn5azlenlG+M/ZdrSCDLUhRzf0eLTt7Z3cyGiHzvW9LyuTZ7ypDXe7OhnP+q3x000Ot9Hta0VnNN1qQRNXtWso0n0gv5cchaZbMIcF/VVp6PO5M0nXFU3Zqg1vLDnGl84e7mvQF2khtI1iO/izJ3N282VsTsXjXr45mbnHlM3J+fdDXSPoytRPPjU5jeF1/hFQGW1n7BM32z32ecbbxdvo6r49M4IyWb8fEpaNVaBIImt52n6vexP2QLUTVwnik9KHM9gQDP1H7Bw10NgMKzzjbeKV/L1aYMTk3KpCQ+DZ1GQ0AI6l3d/KOhjM8jNhDKi0/rpxenmNJZkp7PL5oPhOWRv+Wxs6liHf8v0co5qTmk64woikJHj5t32mp4q7uR3VE2KLorLY/SUXrMFBTOTMvhTlcnP2oogzHwaSrAKUmZ/DJzIj86vIuOGOVroMIyq1qrsHQZY1Q7CnNSbFh1cWOm2sbUoAtgeeMB/tZXKONIdxwM+PhdZx2/66wjRaXGrKhwiAC1UVzrM3RxTDWZ+734yKC4z3weLoup6Eov5+lNzEq2jrlBP+LquT13BhX71/JvjyOm0o/RCsvs9nn4Tu32mO97oT6Bk1Iyj3kbD4kA1/cZmtgkWUV1YsZXqDZ/7Ep/OK7oyFlNAMHbzZU0hCiCOJWac9LzYoqWPyt9HLOb9rMpRIG+21bDRZaiEfWlWlG42jaJT7oaedVz1OX4YY+DZ2p3cVv+SaM2iLkGE/flTufggQ3sj3V/9ShBca+5OngtpsIyvdyWZGNWspVjsUHl3R210FEbs5Z70jopaNA/ba/jty0Hw3LPm0WAx+zNPGZvJk5Rka1S40Nw0O/vZ7RsKjXzUnOC7djQWc89zRVhgcJNIsCjXY082tVIsqLCqlLjRXAgyprzVJWGC9JyowyyFW7MmUa9x8HjXQ1hgZgNIsBvOuv4TWdd3+BXGdCrpaV3Z7/rckpjKn08FFpFxTWZE9jrbOfJrrFJfVQpCpeYC/nC0c5D7TUxa4xohWVuaoh9j4ckRcUHRtOYGnTVWKvNS63FPGQuIneAl90e8FPm90Y15jmKivuyp2HRH/sOON6u63EGE3flzqBAffziEL/cPdnCO/s/caXgoLOLtzrrw1T2TQkZFMRYBjjLkMD8xPBykS+5O9nR1TTiZ0rVGbgtp5SkkO/RBTzeVs2HrdXH5Js/IdHCLzInkXAcP7IvT77C7zw3OZMnsqYxWaWJ+v05RYD9fi8Ho6yTp6LwG+tESkKqpM1OsvBM1jQKB3Bld4gA+/zeqMY8TVH4iWU84wfYYSxOreWXhXO4O3UcSYO5TwYw5umKwi9Tc7mrYBYmjfaY9WiyRs9tuTNYcIyreUZ6k27OKeUyY/KI9eRws+T1x0FOx1znJ6i13JQ7gzeK5rEk0UJhDKM4LTBHo+eZcSdyamrON8YYnJKUyS+tE0mRC+H/Efy75RD7ItZHz08bhy7G7W8V4NvpeVgj5GVZU8WoNpg4MTmT+9PDi8gcFgF+V7uTeo991O1WKwqXWwq5LSXnP+6d61RqLsssZlnJfH6eks2kGL1jpWotj9om893MkjCvkEGl4ZLMCbxTcga/SslhYoxr1NPVWh61TeGakHTfaCRqdPw0/0ReGXciF+kTiKU2mwE4RxfHs7kzuT3/pGGVtI6VfGMiP8+eRskY1tPI1Mdxe+50pn2Nd/WM5LhMF9WKwsxkK9OSzNxkb2NzZz2fdjVR4bHTIgJ0iAAGFHJUGnJ1Rs5LyeHUtJwB61erUJiki8M2CsOYpo3r556LU2u5KmRUKISIupWgWlE4RxdPIKT4RE4MQq1WFC61FFHl7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Basepath: 'C:\\Users\\Cherif\\Documents\\myclass.com\\Siteweb\\Elyos_Ftion_Initiale\\Ela_back\\Back_Office_FI\\__dummy__' +DEBUG:xhtml2pdf.tags:Parsing img tag, src: +DEBUG:xhtml2pdf.tags:Attrs: {'src': , 'width': None, 'height': None, 'align': None, 'id': None} +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None] +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col 0 has width 50% +DEBUG:xhtml2pdf.tables:Col 1 has width 10% +DEBUG:xhtml2pdf.tables:Col 2 has width 20% +DEBUG:xhtml2pdf.tables:Col 3 has width 20% +DEBUG:xhtml2pdf.tables:Col widths: ['50%', '10%', '20%', '20%'] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None] +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:None +DEBUG:xhtml2pdf.tables:Col widths: [None, None, None, None, None, None] +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'PLTE' 41 6 +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 59 817 +DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13 +DEBUG:PIL.PngImagePlugin:STREAM b'sRGB' 41 1 +DEBUG:PIL.PngImagePlugin:STREAM b'gAMA' 54 4 +DEBUG:PIL.PngImagePlugin:STREAM b'cHRM' 70 32 +DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 114 9 +DEBUG:PIL.PngImagePlugin:STREAM b'tIME' 135 7 +DEBUG:PIL.PngImagePlugin:b'tIME' 135 7 (unknown) +DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 154 8192 +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:53] "POST /myclass/api/Invoice_Inscrption_With_Split_Session_By_Inscription_Id/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:04:53.851948 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:04:55] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:05:33.135720 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:05:33.137688 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:05:33.139689 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:05:33] "POST /myclass/api/getRecodedImage/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:05:33.145688 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:05:33] "POST /myclass/api/Get_Nb_User_Internal_Mail_Not_Read/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:05:33] "POST /myclass/api/LMS_Get_Partner_Data/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:05:33.158777 : Connexion du partner recid = 43598820dd270936c3d2fd822717d0f18f194b1a1b894aaf89 OK. Mise ŕ jour du firstconnexion et/ou lastconnexion : OK +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:05:33] "POST /myclass/api/get_partner_account/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:05:33.190779 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:05:33.193780 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:05:33] "POST /myclass/api/Get_List_Partner_Invoice_no_filter/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:05:34] "POST /myclass/api/Get_Partner_List_Partner_Client/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:05:34.764257 : Security check : IP adresse '127.0.0.1' connected +INFO:root:2025-10-19 14:05:34.767253 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:05:34] "POST /myclass/api/Get_Given_Partner_Invoice/ HTTP/1.1" 200 - +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:05:34] "POST /myclass/api/Get_Given_Partner_Invoice_Lines/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:05:36.348847 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:05:36] "GET /myclass/api/GerneratePDF_Partner_Invoice/JXT3OAh0wK_bmdmZ9V3_K4gCtfktxUiFCA/68f4d3e58b7f78a943b87142 HTTP/1.1" 200 - +INFO:root:2025-10-19 14:05:55.474700 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:05:55] "POST /myclass/api/Get_Given_Personnalisable_Fields_By_template_ref_interne/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:06:00.117630 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:06:00] "POST /myclass/api/Update_Partner_Document/ HTTP/1.1" 200 - +INFO:root:2025-10-19 14:06:00.205658 : Security check : IP adresse '127.0.0.1' connected +INFO:werkzeug:127.0.0.1 - - [19/Oct/2025 14:06:00] "POST /myclass/api/Get_Given_Partner_Document/ HTTP/1.1" 200 - diff --git a/Session_Formation.py b/Session_Formation.py index 9b932fc..77c6054 100644 --- a/Session_Formation.py +++ b/Session_Formation.py @@ -69,7 +69,8 @@ def Add_Update_SessionFormation(diction): 'attestation_certif', "distantiel", "presentiel", "prix_session", 'contenu_ftion', 'lms_class_code', 'session_ondemande', 'source', 'session_id', 'session_etape', 'pays', 'formateur_id', 'titre', 'location_type', 'is_bpf', 'site_formation_id', 'price_by', 'mode_animation', 'archive', - 'ent_account_automatic', 'lms_account_automatic', 'type_session', 'entre_scolaire', 'entre_scolaire'] + 'ent_account_automatic', 'lms_account_automatic', 'type_session', 'entre_scolaire', + 'memo', 'comment'] incom_keys = diction.keys() for val in incom_keys: @@ -313,6 +314,20 @@ def Add_Update_SessionFormation(diction): mycommon.myprint(str(inspect.stack()[0][3]) + " - Le champ 'location_type' est incorrecte.") return False, "Le champ 'location_type' est incorrect." + if ("memo" in diction.keys()): + mydata['memo'] = str(diction['memo']) + if (len(str(diction['memo'])) > 1000 ): + mycommon.myprint(str(inspect.stack()[0][3]) + " - Le champ 'memo' ne doit pas faire plus 1000 caractères.") + return False, "Le champ 'memo' ne doit pas faire plus 500 caractères." + + if ("comment" in diction.keys()): + mydata['comment'] = str(diction['comment']) + if (len(str(diction['comment'])) > 1000 ): + mycommon.myprint(str(inspect.stack()[0][3]) + " - Le champ 'commentaire' ne doit pas faire plus 1000 caractères.") + return False, "Le champ 'commentaire' ne doit pas faire plus 500 caractères." + + + if ("entre_scolaire" in diction.keys()): mydata['entre_scolaire'] = str(diction['entre_scolaire']).lower() if (str(diction['entre_scolaire']).lower() not in MYSY_GV.TRAINING_ENTREE_SCOLAIRE): @@ -1780,6 +1795,18 @@ def GetAllValideSessionPartner_List_Without_Scope_Action(diction): else: val['entre_scolaire'] = "" + if ("memo" in retVal.keys()): + val['memo'] = retVal['memo'] + else: + val['memo'] = "" + + + if ("comment" in retVal.keys()): + val['comment'] = retVal['comment'] + else: + val['comment'] = "" + + if ("is_bpf" in retVal.keys()): val['is_bpf'] = retVal['is_bpf'] else: @@ -1960,6 +1987,16 @@ def GetAllValideSessionPartner_List(diction): else: val['entre_scolaire'] = "" + if ("memo" in retVal.keys()): + val['memo'] = retVal['memo'] + else: + val['memo'] = "" + + if ("comment" in retVal.keys()): + val['comment'] = retVal['comment'] + else: + val['comment'] = "" + if ("is_bpf" in retVal.keys()): val['is_bpf'] = retVal['is_bpf'] @@ -2256,21 +2293,21 @@ def GetAllValideSessionPartner_List_filter_like(diction): filt_class_title = {} if ("class_title" in diction.keys()): - filt_class_title = {'title': {'$regex': str(diction['class_title']),"$options": "i"}} + filt_class_title = {'title': {'$regex': mycommon.regex_replace_cartere(str(diction['class_title'])),"$options": "i"}} filt_class_internal_url = {} if ("class_internal_url" in diction.keys()): - filt_class_internal_url = {'internal_url': {'$regex': str(diction['class_internal_url']),"$options": "i"}} + filt_class_internal_url = {'internal_url': {'$regex': mycommon.regex_replace_cartere(str(diction['class_internal_url'])),"$options": "i"}} filt_class_external_code = {} if ("class_external_code" in diction.keys()): - filt_class_external_code = {'external_code': {'$regex': str(diction['class_external_code']), "$options": "i"}} + filt_class_external_code = {'external_code': {'$regex': mycommon.regex_replace_cartere(str(diction['class_external_code'])), "$options": "i"}} filt_code_session = {} if ("code_session" in diction.keys()): - filt_code_session = {'code_session': {'$regex': str(diction['code_session']), "$options": "i"}} + filt_code_session = {'code_session': {'$regex': mycommon.regex_replace_cartere(str(diction['code_session'])), "$options": "i"}} filt_session_start_date = "" if ("session_start_date" in diction.keys()): @@ -2372,6 +2409,18 @@ def GetAllValideSessionPartner_List_filter_like(diction): else: val['entre_scolaire'] = "" + + if ("memo" in retVal.keys()): + val['memo'] = retVal['memo'] + else: + val['memo'] = "" + + if ("comment" in retVal.keys()): + val['comment'] = retVal['comment'] + else: + val['comment'] = "" + + if ("is_bpf" in retVal.keys()): val['is_bpf'] = retVal['is_bpf'] else: @@ -2747,6 +2796,16 @@ def GetAllValideSessionPartner_List_no_filter(diction): else: val['entre_scolaire'] = "" + if ("memo" in retVal.keys()): + val['memo'] = retVal['memo'] + else: + val['memo'] = "" + + if ("comment" in retVal.keys()): + val['comment'] = retVal['comment'] + else: + val['comment'] = "" + if ("is_bpf" in retVal.keys()): val['is_bpf'] = retVal['is_bpf'] else: @@ -3057,7 +3116,7 @@ def Add_Update_SessionFormation_mass(file=None, Folder=None, diction=None): 'session_status', 'date_debut_inscription', 'date_fin_inscription', 'attestation', 'formateur', 'code_session', "distanciel", "presentiel", "mode_animation", "prix_session", 'contenu_ftion', 'lms_class_code', 'session_ondemande', 'session_etape', 'formation_code_externe', 'formateur_email', 'titre', 'location_type', - 'is_bpf', 'type_session', 'entre_scolaire'] + 'is_bpf', 'type_session', 'entre_scolaire', 'memo', 'comment'] # Controle du nombre de lignes dans le fichier. total_rows = len(df) @@ -3548,7 +3607,7 @@ def Controle_Add_Update_SessionFormation_mass(saved_file=None, Folder=None, dict 'session_status', 'date_debut_inscription', 'date_fin_inscription', 'attestation', 'code_session', "distanciel", "presentiel", "mode_animation", "prix_session", 'contenu_ftion', 'lms_class_code', 'session_ondemande', 'session_etape', 'formation_code_externe','formateur_email', 'titre', 'location_type', - 'is_bpf', 'type_session', 'entre_scolaire'] + 'is_bpf', 'type_session', 'entre_scolaire', 'memo', 'comment'] # Controle du nombre de lignes dans le fichier. total_rows = len(df) @@ -3720,6 +3779,19 @@ def Controle_Add_Update_SessionFormation_mass(saved_file=None, Folder=None, dict return False, "Le champ 'entre_scolaire' est incorrect." + if ("memo" in df.keys()): + mydata['memo'] = str(df['memo'].values[n]).strip() + if (len( mydata['memo']) > 1000): + mycommon.myprint(str(inspect.stack()[0][3]) + " - Le champ 'memo' ne doit faire plus de 1000 caractères ") + return False, "Le champ 'memo' ne doit faire plus de 1000 caractères " + + if ("comment" in df.keys()): + mydata['comment'] = str(df['comment'].values[n]).strip() + if (len( mydata['comment']) > 1000): + mycommon.myprint(str(inspect.stack()[0][3]) + " - Le champ 'comment' ne doit faire plus de 1000 caractères ") + return False, "Le champ 'comment' ne doit faire plus de 1000 caractères " + + if ("is_bpf" in df.keys()): if (str(df['is_bpf'].values[n]).strip() not in ['0', '1']): @@ -4120,7 +4192,7 @@ def Add_Update_SessionFormation_mass_for_many_class(file=None, Folder=None, dict 'session_status', 'date_debut_inscription', 'date_fin_inscription', 'attestation', 'code_session', "distanciel", "presentiel", "mode_animation", "prix_session", 'contenu_ftion', 'lms_class_code', 'session_ondemande', 'session_etape', 'formation_code_externe', 'formateur_email', 'titre', 'location_type', - 'is_bpf', 'type_session', 'entre_scolaire'] + 'is_bpf', 'type_session', 'entre_scolaire', 'memo', 'comment'] # Controle du nombre de lignes dans le fichier. total_rows = len(df) @@ -4251,6 +4323,20 @@ def Add_Update_SessionFormation_mass_for_many_class(file=None, Folder=None, dict mycommon.myprint(str(inspect.stack()[0][3]) + " - Le champ 'entre_scolaire' est incorrecte.") return False, "Le champ 'entre_scolaire' est incorrect." + if ("memo" in df.keys()): + mydata['memo'] = str(df['memo'].values[n]).strip() + if (len(mydata['memo']) > 1000): + mycommon.myprint( + str(inspect.stack()[0][3]) + " - Le champ 'memo' ne doit faire plus de 1000 caractères ") + return False, "Le champ 'memo' ne doit faire plus de 1000 caractères " + + if ("comment" in df.keys()): + mydata['comment'] = str(df['comment'].values[n]).strip() + if (len(mydata['comment']) > 1000): + mycommon.myprint( + str(inspect.stack()[0][3]) + " - Le champ 'comment' ne doit faire plus de 1000 caractères ") + return False, "Le champ 'comment' ne doit faire plus de 1000 caractères " + if ("is_bpf" in df.keys()): is_bpf = str(df['is_bpf'].values[n]).strip() @@ -4668,7 +4754,7 @@ def Controle_Add_Update_SessionFormation_mass_for_many_class(saved_file=None, Fo 'session_status', 'date_debut_inscription', 'date_fin_inscription', 'attestation', 'code_session', "distanciel", "presentiel", "mode_animation", "prix_session", 'contenu_ftion', 'lms_class_code', 'session_ondemande', 'session_etape', 'formation_code_externe', 'formateur_email', 'titre', 'location_type', - 'is_bpf', 'type_session', 'entre_scolaire'] + 'is_bpf', 'type_session', 'entre_scolaire', 'memo', 'comment'] # Controle du nombre de lignes dans le fichier. total_rows = len(df) @@ -4866,6 +4952,19 @@ def Controle_Add_Update_SessionFormation_mass_for_many_class(saved_file=None, Fo mycommon.myprint(str(inspect.stack()[0][3]) + " - Le champ 'entre_scolaire' est incorrecte. Les valeurs acceptĂ©s sont : "+str(MYSY_GV.TRAINING_ENTREE_SCOLAIRE)) return False, "Le champ 'entre_scolaire' est incorrect. . Les valeurs acceptĂ©s sont : "+str(MYSY_GV.TRAINING_ENTREE_SCOLAIRE)+ " " + if ("memo" in df.keys()): + mydata['memo'] = str(df['memo'].values[n]).strip() + if (len(mydata['memo']) > 1000): + mycommon.myprint( + str(inspect.stack()[0][3]) + " - Le champ 'memo' ne doit faire plus de 1000 caractères ") + return False, "Le champ 'memo' ne doit faire plus de 1000 caractères " + + if ("comment" in df.keys()): + mydata['comment'] = str(df['comment'].values[n]).strip() + if (len(mydata['comment']) > 1000): + mycommon.myprint( + str(inspect.stack()[0][3]) + " - Le champ 'comment' ne doit faire plus de 1000 caractères ") + return False, "Le champ 'comment' ne doit faire plus de 1000 caractères " if ("is_bpf" in df.keys()): @@ -10985,7 +11084,7 @@ def Invoice_Partner_From_Session( diction): partner_invoice_line_data['order_line_formation'] = class_data[0]['internal_url'] partner_invoice_line_data['order_line_class_id'] = str(class_data[0]['_id']) - + partner_invoice_line_data['order_line_session_id'] = str(diction['session_id']) partner_invoice_line_data['order_line_qty'] = str(nb_participant_du_client) partner_invoice_line_data['order_line_prix_unitaire'] = str(prix_session) partner_invoice_line_data['order_line_montant_hors_taxes'] = str(total_ht) @@ -10993,12 +11092,13 @@ def Invoice_Partner_From_Session( diction): partner_invoice_line_data['invoice_header_id'] = str(inserted_invoice_id) partner_invoice_line_data['invoice_line_type'] = "facture" partner_invoice_line_data['invoice_header_ref_interne'] = partner_invoice_header_data['invoice_header_ref_interne'] + partner_invoice_line_data['tab_inscription_ids'] = tab_participant partner_invoice_line_data['update_by'] = str(my_partner['_id']) partner_invoice_line_data['valide'] = "1" partner_invoice_line_data['locked'] = "0" partner_invoice_line_data['partner_owner_recid'] = str(my_partner['recid']) - print(" #### partner_invoice_line_data = ", partner_invoice_line_data) + print(" #### partner_invoice_line_data 00 = ", partner_invoice_line_data) inserted_invoice_id = MYSY_GV.dbname['partner_invoice_line'].insert_one( partner_invoice_line_data).inserted_id if (not inserted_invoice_id): @@ -13378,13 +13478,14 @@ def Invoice_Partner_From_Session_By_Inscription_Id( diction): order_line_formation = titre formation order_line_qty = nb participants order_line_comment = la liste des personnes participans + tab_inscription_ids = contient l'_id des inscrits (tab_participant) """ partner_invoice_line_data = {} list_partner_invoice_line_champ = ['order_line_formation', 'order_line_qty', 'order_line_prix_unitaire', 'order_line_tax', 'order_line_tax_amount', 'order_line_montant_toutes_taxes', 'order_line_montant_hors_taxes', 'order_line_type_reduction', 'order_line_type_valeur', 'order_line_montant_reduction', 'order_header_ref_interne', 'order_line_comment', 'order_header_id', 'valide', 'locked', 'date_update', 'partner_owner_recid', 'invoice_header_ref_interne', 'invoice_line_type', - 'invoice_date', 'invoice_header_id', 'order_line_is_include_bpf', 'order_line_class_id'] + 'invoice_date', 'invoice_header_id', 'order_line_is_include_bpf', 'order_line_class_id', 'tab_inscription_ids'] # PreRemplir les champs @@ -13412,6 +13513,7 @@ def Invoice_Partner_From_Session_By_Inscription_Id( diction): nom_prenom_email_participant += local_nom+" "+local_prenom+" "+local_email+"\n" + partner_invoice_line_data['order_line_session_id'] = str(diction['session_id']) partner_invoice_line_data['order_line_formation'] = class_data[0]['internal_url'] partner_invoice_line_data['order_line_class_id'] = str(class_data[0]['_id']) partner_invoice_line_data['order_line_qty'] = str(nb_participant_du_client) @@ -13421,6 +13523,7 @@ def Invoice_Partner_From_Session_By_Inscription_Id( diction): partner_invoice_line_data['invoice_header_id'] = str(inserted_invoice_id) partner_invoice_line_data['invoice_line_type'] = "facture" partner_invoice_line_data['invoice_header_ref_interne'] = partner_invoice_header_data['invoice_header_ref_interne'] + partner_invoice_line_data['tab_inscription_ids'] = tab_participant partner_invoice_line_data['update_by'] = str(my_partner['_id']) partner_invoice_line_data['valide'] = "1" partner_invoice_line_data['locked'] = "0" @@ -13432,7 +13535,7 @@ def Invoice_Partner_From_Session_By_Inscription_Id( diction): partner_invoice_line_data['order_line_is_include_bpf'] = order_line_is_include_bpf - print(" #### partner_invoice_line_data = ", partner_invoice_line_data) + print(" #### partner_invoice_line_data 33 = ", partner_invoice_line_data) inserted_invoice_id = MYSY_GV.dbname['partner_invoice_line'].insert_one( partner_invoice_line_data).inserted_id if (not inserted_invoice_id): @@ -13460,6 +13563,14 @@ def Invoice_Partner_From_Session_By_Inscription_Id( diction): partner_invoice_line_data_detail = {} partner_invoice_line_data_detail['order_line_inscription_id'] = str(tmp_inscription_dat['_id']) partner_invoice_line_data_detail['order_line_inscription_type_apprenant'] = str(tmp_inscription_dat['type_apprenant']) + partner_invoice_line_data_detail['order_line_inscription_comment'] = str(tmp_inscription_dat['comment']) + partner_invoice_line_data_detail['order_line_inscription_memo'] = str(tmp_inscription_dat['memo']) + partner_invoice_line_data_detail['order_line_inscription_price'] = str(tmp_inscription_dat['price']) + partner_invoice_line_data_detail['order_line_inscription_email'] = str(tmp_inscription_dat['email']) + partner_invoice_line_data_detail['order_line_inscription_civilite'] = str(tmp_inscription_dat['civilite']) + partner_invoice_line_data_detail['order_line_inscription_nom'] = str(tmp_inscription_dat['nom']) + partner_invoice_line_data_detail['order_line_inscription_prenom'] = str(tmp_inscription_dat['prenom']) + if( "modefinancement" in tmp_inscription_dat.keys()): partner_invoice_line_data_detail['order_line_inscription_modefinancement'] = str(tmp_inscription_dat['modefinancement']) @@ -13468,6 +13579,7 @@ def Invoice_Partner_From_Session_By_Inscription_Id( diction): partner_invoice_line_data_detail['order_line_formation'] = class_data[0]['internal_url'] partner_invoice_line_data_detail['order_line_class_id'] = str(class_data[0]['_id']) + partner_invoice_line_data['order_line_session_id'] = str(diction['session_id']) partner_invoice_line_data_detail['order_line_prix_unitaire'] = str(prix_session) partner_invoice_line_data_detail['order_line_qty'] = "1" @@ -13489,7 +13601,7 @@ def Invoice_Partner_From_Session_By_Inscription_Id( diction): partner_invoice_line_data_detail['locked'] = "0" partner_invoice_line_data_detail['partner_owner_recid'] = str(my_partner['recid']) - print(" #### partner_invoice_line_data = ", partner_invoice_line_data) + print(" #### partner_invoice_line_data 77 = ", partner_invoice_line_data) inserted_invoice_id = MYSY_GV.dbname['partner_invoice_line_detail'].insert_one( partner_invoice_line_data_detail).inserted_id @@ -13970,7 +14082,7 @@ def Invoice_Splited_Partner_From_Session_By_Inscription_Id( diction): list_partner_invoice_line_champ = ['order_line_formation', 'order_line_qty', 'order_line_prix_unitaire', 'order_line_tax', 'order_line_tax_amount', 'order_line_montant_toutes_taxes', 'order_line_montant_hors_taxes', 'order_line_type_reduction', 'order_line_type_valeur', 'order_line_montant_reduction', 'order_header_ref_interne', 'order_line_comment', 'order_header_id', 'valide', 'locked', 'date_update', 'partner_owner_recid', 'invoice_header_ref_interne', 'invoice_line_type', - 'invoice_date', 'invoice_header_id', 'order_line_class_id'] + 'invoice_date', 'invoice_header_id', 'order_line_class_id', 'order_line_session_id'] # PreRemplir les champs @@ -14001,6 +14113,8 @@ def Invoice_Splited_Partner_From_Session_By_Inscription_Id( diction): partner_invoice_line_data['order_line_formation'] = class_data[0]['internal_url'] partner_invoice_line_data['order_line_class_id'] = str(class_data[0]['_id']) + partner_invoice_line_data['order_line_session_id'] = str(diction['session_id']) + partner_invoice_line_data['order_line_qty'] = str(nb_participant_du_client) partner_invoice_line_data['order_line_prix_unitaire'] = str(prix_session) partner_invoice_line_data['order_line_montant_hors_taxes'] = str(total_ht) @@ -14008,6 +14122,7 @@ def Invoice_Splited_Partner_From_Session_By_Inscription_Id( diction): partner_invoice_line_data['invoice_header_id'] = str(inserted_invoice_id) partner_invoice_line_data['invoice_line_type'] = "facture" partner_invoice_line_data['invoice_header_ref_interne'] = partner_invoice_header_data['invoice_header_ref_interne'] + partner_invoice_line_data['tab_inscription_ids'] = tab_participant order_line_is_include_bpf = "" if ("is_bpf" in session_data.keys()): @@ -14019,7 +14134,7 @@ def Invoice_Splited_Partner_From_Session_By_Inscription_Id( diction): partner_invoice_line_data['locked'] = "0" partner_invoice_line_data['partner_owner_recid'] = str(my_partner['recid']) - print(" #### partner_invoice_line_data = ", partner_invoice_line_data) + print(" #### partner_invoice_line_data 11 = ", partner_invoice_line_data) inserted_invoice_id = MYSY_GV.dbname['partner_invoice_line'].insert_one( partner_invoice_line_data).inserted_id if (not inserted_invoice_id): @@ -14031,11 +14146,9 @@ def Invoice_Splited_Partner_From_Session_By_Inscription_Id( diction): tab_date_invoice.append(str(now)) """ - 27/08/2024 - update pour faire le BPF - - on va crĂ©er une table de detail qui reprend le detail des inscription - - """ + 27/08/2024 - update pour faire le BPF + on va crĂ©er une table de detail qui reprend le detail des inscription + """ order_line_montant_hors_taxes_par_apprenant = round(total_ht , 2) for tmp_inscription_dat in MYSY_GV.dbname['inscription'].find({'session_id': str(diction['session_id']), @@ -14050,9 +14163,19 @@ def Invoice_Splited_Partner_From_Session_By_Inscription_Id( diction): partner_invoice_line_data_detail = {} partner_invoice_line_data_detail['order_line_inscription_id'] = str(tmp_inscription_dat['_id']) partner_invoice_line_data_detail['order_line_inscription_type_apprenant'] = str(tmp_inscription_dat['type_apprenant']) + partner_invoice_line_data_detail['order_line_inscription_comment'] = str(tmp_inscription_dat['comment']) + partner_invoice_line_data_detail['order_line_inscription_memo'] = str(tmp_inscription_dat['memo']) + partner_invoice_line_data_detail['order_line_inscription_price'] = str(tmp_inscription_dat['price']) + partner_invoice_line_data_detail['order_line_inscription_email'] = str(tmp_inscription_dat['email']) + partner_invoice_line_data_detail['order_line_inscription_civilite'] = str( + tmp_inscription_dat['civilite']) + partner_invoice_line_data_detail['order_line_inscription_nom'] = str(tmp_inscription_dat['nom']) + partner_invoice_line_data_detail['order_line_inscription_prenom'] = str(tmp_inscription_dat['prenom']) + partner_invoice_line_data_detail['order_line_inscription_modefinancement'] = str(tmp_inscription_dat['modefinancement']) partner_invoice_line_data_detail['order_line_formation'] = class_data[0]['internal_url'] partner_invoice_line_data_detail['order_line_class_id'] = str(class_data[0]['_id']) + partner_invoice_line_data['order_line_session_id'] = str(diction['session_id']) partner_invoice_line_data_detail['order_line_prix_unitaire'] = str(prix_session) partner_invoice_line_data_detail['order_line_montant_hors_taxes'] = str(total_ht) @@ -14074,7 +14197,7 @@ def Invoice_Splited_Partner_From_Session_By_Inscription_Id( diction): partner_invoice_line_data_detail['locked'] = "0" partner_invoice_line_data_detail['partner_owner_recid'] = str(my_partner['recid']) - print(" #### partner_invoice_line_data = ", partner_invoice_line_data) + print(" #### partner_invoice_line_data 22= ", partner_invoice_line_data) inserted_invoice_id = MYSY_GV.dbname['partner_invoice_line_detail'].insert_one( partner_invoice_line_data_detail).inserted_id """ @@ -14417,11 +14540,41 @@ def Invoice_Create_Secure_E_Document(diction): """ user['order_line_type_article'] = "formation" + """ + 19/10/2025 - aller recuperer les donnĂ©es des inscrits en faisant un lien entre partner_invoice_line['tab_inscription_ids'] + et partner_invoice_line_detail['order_line_inscription_id'] + """ + tab_inscription = [] + for inscription_id in retval['tab_inscription_ids']: + print(" ## ICI QURYYY = ", {'order_line_inscription_id':str(inscription_id), + 'valide':'1', 'locked':'0', + 'partner_owner_recid':str(retval['partner_owner_recid']), + 'invoice_header_ref_interne':str(retval['invoice_header_ref_interne'])}) + + invoice_inscription_id_data = MYSY_GV.dbname['partner_invoice_line_detail'].find_one({'order_line_inscription_id':str(inscription_id), + 'valide':'1', 'locked':'0', + 'partner_owner_recid':str(retval['partner_owner_recid']), + 'invoice_header_ref_interne':str(retval['invoice_header_ref_interne'])}, + {'order_line_inscription_type_apprenant':1, + 'order_line_inscription_comment':1, + 'order_line_inscription_memo':1, + 'order_line_inscription_price':1, + 'order_line_inscription_modefinancement':1, + 'order_line_inscription_nom':1, + 'order_line_inscription_prenom': 1, + 'order_line_inscription_email': 1, + 'order_line_inscription_civilite': 1, + }) + tab_inscription.append(invoice_inscription_id_data) + + print(" UUU tab_inscription = ", tab_inscription) + user['inscription_data'] = tab_inscription + Order_header_lines_data.append(user) """ - Recuperation des produits et services - """ + Recuperation des produits et services + """ query = query = [{'$match': {'$and': [filt_order_header_order_id, {'partner_owner_recid': str(my_partner['recid'])}]}}, {'$lookup': { @@ -14577,7 +14730,7 @@ def Invoice_Create_Secure_E_Document(diction): convention_dictionnary_data['order_header'] = Order_header_data convention_dictionnary_data['order_lines'] = Order_header_lines_data - #print(" ### Order_header_lines_data === ", Order_header_lines_data) + print(" ### Order_header_lines_data === ", Order_header_lines_data) # sourceHtml = contenu_doc_Template.render(params=Order_header_data, param_order_lines=Order_header_lines_data, company_data=company_data) @@ -15651,7 +15804,7 @@ def Add_SessionFormation_From_Quotation_Line(diction): 'lms_class_code', 'session_ondemande', 'source', 'session_etape', 'pays', 'formateur_id', 'titre', 'location_type', 'is_bpf', 'site_formation_id', 'price_by', - 'quotation_line_id', 'resa_inscrit', 'entre_scolaire'] + 'quotation_line_id', 'resa_inscrit', 'entre_scolaire', 'memo', 'comment'] incom_keys = diction.keys() for val in incom_keys: diff --git a/Template/invoice_RIB_PortCities_perso_tpl.html b/Template/invoice_RIB_PortCities_perso_tpl.html index f7f287d..048a99e 100644 --- a/Template/invoice_RIB_PortCities_perso_tpl.html +++ b/Template/invoice_RIB_PortCities_perso_tpl.html @@ -10,13 +10,13 @@
-
+
{{json_data.client_name}}
{{json_data.client_address}}
{{json_data.client_zip_ville}}
- {{json_data.client_pays}}
+ {{json_data.client_pays}}
@@ -28,7 +28,7 @@
- + @@ -42,7 +42,7 @@
- Origine : {{json_data.orign_order}}
+ Origine : {{json_data.orign_order}}
Période : {{json_data.periode}}
@@ -51,22 +51,23 @@ style="padding:12px 0px;text-align:left;font-family:Georgia, 'Times New Roman', Times, serif;color:#454349;font-size:0.9rem;width:100%;float:right;text-align:left;">
- - - - + + + + - - - - + + + + - - + + @@ -100,18 +101,14 @@
DescriptionQuantitéPrix unitaire (HT)Montant (HT)DescriptionQuantitéPrix unitaire (HT)Montant (HT)
{{json_data.packs}}
- -
{{json_data.qty}}{{json_data.unit_price}}{{json_data.montant}} {{json_data.packs}}
+ +
{{json_data.qty}}{{json_data.unit_price}}{{json_data.montant}}
 {{json_data.total_ttc}}  €
- -
-
+ +

Règlement : Virement bancaire

- - -

> Relevé d'identité bancaire

@@ -172,10 +169,22 @@
- +

-
+ +
+ + Termes et conditions
+ + Pas d'escompte accordé pour paiement anticipé.
+ En cas de non-paiement à la date d'échéance, des pénalités calculées à trois fois le taux d'intérêt légal + seront appliquées.
+ Tout retard de paiement entraînera une indemnité forfaitaire pour frais de recouvrement de 40€.
+ +
+