/calebasse/statistics/statistics.py - Calebasse - Redmine Entr’ouvert

calebasse / calebasse / statistics / statistics.py @ fdb98b89

       # -*- coding: utf-8 -*-
       import os
       import tempfile
       import csv
       from collections import OrderedDict
       from datetime import datetime, timedelta
       from dateutil.relativedelta import relativedelta
       from django.db import models
       from django.db.models import Count
       from django.utils import formats
       from django.conf import settings
       from calebasse.dossiers.models import PatientRecord, FileState
       from calebasse.personnes.models import Worker
       from calebasse.actes.models import Act
       from calebasse.agenda.models import Event
       STATISTICS = {
           'patients_per_worker_for_period' :
+              {
               'display_name': 'Enfants suivis par intervenant sur une période',
               'category': 'Patients',
               'comment': """Liste et décompte des patients par intervenant pour les
                   patients ayant eu au moins un acte proposé avec cet intervenant
                   sur la plage de dates spécifiée. La date de début de la plage par
                   défaut est le 1er janvier de l'année en cours. La date de fin de
                   la plage par défaut est aujourd'hui. Si aucun patient ou
                   intervenant n'est spécifié, tous les patients et les intervenants
                   sont pris en compte. A l'inverse, si des patients ou des
                   intervenants sont indiqués, seuls ceux-ci sont pris en compte.
                   """
           },
           'active_patients_by_state_only' :
+              {
               'display_name': "Dossiers actifs selon l'état du dossier à une date donnée",
               'category': 'Patients',
               'comment': """Listes des patients dont le dossier était actif à une date donnée.
                   Rappel des états actifs des dossiers : CMPP : diagnostic
                   ou traitement, CAMSP : suivi, SESSAD: Traitement.
                   La date par défaut est aujourd'hui.
                   """
           },
           'active_patients_with_act' :
+              {
               'display_name': 'Listes des dossiers avec un acte validé, ou un acte '
                   'proposé seulement, sur une période et triés par état du dossier '
                   'en fin de période',
               'category': 'Patients',
               'comment': """Listes des dossiers avec un acte validé ou un acte
                   proposé seulement sur une période et triés par état du dossier en
                   fin de période.
                   La date de début de la plage par
                   défaut est le 1er janvier de l'année en cours. La date de fin de
                   la plage par défaut est aujourd'hui.
                   """
           },
           'closed_files' :
+              {
               'display_name': 'Dossier clos sur une période et durée de la prise '
                   'en charge',
               'category': 'Patients',
               'comment': """Liste des dossiers clos avec leur durée de la prise en
                   charge sur la plage de dates spécifiée. Le nombre de dossier et la
                   durée moyenne de la prise en charge est également donnée. La date
                   de début de la plage par défaut est le 1er janvier de l'année en
                   cours. La date de fin de la plage par défaut est aujourd'hui.
                   """
           },
           'annual_activity' :
+              {
               'display_name': "Activité annuelle",
               'category': 'Intervenants',
               'comment': """Tableaux de synthèse annuelle. La date saisie
                   indique l'année à traiter. Si aucune date n'est spécifiée ce sera
                   l'année en cours. Si aucun intervenant n'est spécifié, les
                   tableaux de synthèse globaux sont retournés. Si des intervenants
                   sont indiqués, les tableaux de synthèse pour chaque intervenant
                   sont retournés.
                   """
           },
           'patients_details' :
+              {
               'display_name': "Synthèse par patient",
               'category': 'Patients',
               'comment': """Tableaux de synthèse par patient. Si aucun patient n'est
                   indiqué, une synthèse pour chaque patient est retournée. La plage
                   de date permet de selectionner les patients pour lesquels au
                   moins au acte a été proposé durant cette période. Si un dossier a
                   été clos durant cette période mais qu'aucun acte n'a été proposé
                   durant cette période, il ne sera pas pris en compte. La date de
                   début de la plage par défaut est le 1er janvier de l'année en
                   cours. La date de fin de la plage par défaut est aujourd'hui. """
           },
           'patients_synthesis' :
+              {
               'display_name': 'Synthèse sur les patients avec un acte valide sur '
                   'la plage de date',
               'category': 'Patients',
               'comment': """Patients ayant eu au moins un acte validé
                   sur la plage de dates spécifiée. La date de début de la plage par
                   défaut est le 1er janvier de l'année en cours. La date de fin de
                   la plage par défaut est aujourd'hui.
                   """
           },
           'acts_synthesis' :
+              {
               'display_name': 'Synthèse sur les actes',
               'category': 'Actes',
               'comment': """Synthèse sur les actes
                   sur la plage de dates spécifiée. La date de début de la plage par
                   défaut est le 1er janvier de l'année en cours. La date de fin de
                   la plage par défaut est aujourd'hui.
                   """
           },
           'acts_synthesis_cmpp' :
+              {
               'display_name': 'Synthèse sur les dossiers facturés au CMPP',
               'category': 'Patients',
               'services': ['CMPP', ],
               'comment': """Synthèse sur les dossiers facturés au CMPP selon que ce
                   soit en diagnostic, en traitement ou les deux,
                   sur la plage de dates spécifiée. La date de début de la plage par
                   défaut est le 1er janvier de l'année en cours. La date de fin de
                   la plage par défaut est aujourd'hui.
                   """
           },
           'mises' :
+              {
               'display_name': 'Synthèse sur les pathologies MISES',
               'category': 'Patients',
               'comment': """Synthèse sur les pathologies
                   sur la plage de dates spécifiée. La date de début de la plage par
                   défaut est le 1er janvier de l'année en cours. La date de fin de
                   la plage par défaut est aujourd'hui.
                   """
           },
           'deficiencies' :
+              {
               'display_name': 'Synthèse sur les déficiences',
               'category': 'Patients',
               'comment': """Synthèse sur les déficiences
                   sur la plage de dates spécifiée. La date de début de la plage par
                   défaut est le 1er janvier de l'année en cours. La date de fin de
                   la plage par défaut est aujourd'hui.
                   """
           },
           'patients_protection' :
+              {
               'display_name': 'Synthèse sur les mesures de protection des patients '
                   'à une date donnée',
               'category': 'Patients',
               'comment': """La date par défaut est aujourd'hui.
                   """
           },
+      }
       ANNUAL_ACTIVITY_ROWS = ['total', 'pointe', 'non_pointe', 'absent', 'percent_abs', 'reporte', 'acts_present', 'abs_non_exc', 'abs_exc', 'abs_inter', 'annul_nous', 'annul_famille', 'abs_ess_pps', 'enf_hosp', 'non_facturables', 'facturables', 'perdus', 'doubles', 'really_facturables', 'factures', 'diag', 'trait', 'restants_a_fac', 'refac', 'nf', 'percent_nf', 'patients', 'intervenants', 'days', 'fact_per_day', 'moving_time', 'moving_time_per_intervene', 'moving_time_per_act']
       ANNUAL_ACTIVITY_COLUMN_LABELS = ['Janv', 'Fév', 'Mar', 'T1', 'Avr', 'Mai', 'Jui', 'T2', 'Jui', 'Aoû', 'Sep', 'T3', 'Oct', 'Nov', 'Déc', 'T4', 'Total']
       class AnnualActivityProcessingColumn():
           total = 0
           pointe = 0
           non_pointe = 0
           absent = 0
           percent_abs = 0
           reporte = 0
           acts_present = 0
           abs_non_exc = 0
           abs_exc = 0
           abs_inter = 0
           annul_nous = 0
           annul_famille = 0
           abs_ess_pps = 0
           enf_hosp = 0
           non_facturables = 0
           facturables = 0
           perdus = 0
           doubles = 0
           really_facturables = 0
           factures = 0
           diag = 0
           trait = 0
           restants_a_fac = 0
           refac = 0
           nf = 0
           percent_nf = 0
           patients = 0
           intervenants = 0
           days = 0
           fact_per_day = 0
           moving_time = timedelta()
           moving_time_per_intervene = timedelta()
           moving_time_per_act = timedelta()
       def annual_activity_month_analysis(statistic, start_day, analyses, key, i, trim_cnt, participant=None):
           rd = relativedelta(months=1)
           sd = start_day + i * rd
           ed = sd + rd
           acts = None
           moving_events = None
           if participant:
               acts = Act.objects.filter(date__gte=sd.date(),
                   date__lt=ed.date(), patient__service=statistic.in_service,
                   doctors__in=[participant])
               moving_events = Event.objects.filter(event_type__label='Temps de trajet',
                   start_datetime__gte=sd, end_datetime__lt=ed,
                   services__in=[statistic.in_service],
                   participants__in=[participant])
           else:
               acts = Act.objects.filter(date__gte=sd.date(),
                   date__lt=ed.date(), patient__service=statistic.in_service)
               moving_events = Event.objects.filter(event_type__label='Temps de trajet',
                   start_datetime__gte=sd, end_datetime__lt=ed,
                   services__in=[statistic.in_service])
           analyses[key].append(AnnualActivityProcessingColumn())
           analyses[key][i+trim_cnt].patients = acts.aggregate(Count('patient', distinct=True))['patient__count']
           analyses[key][i+trim_cnt].intervenants = acts.aggregate(Count('doctors', distinct=True))['doctors__count']
           analyses[key][i+trim_cnt].days = acts.aggregate(Count('date', distinct=True))['date__count']
           for me in moving_events:
               analyses[key][i+trim_cnt].moving_time += me.timedelta()
           for a in acts:
               if a.is_new():
                   analyses[key][i+trim_cnt].non_pointe += 1
               elif a.is_absent():
                   state = a.get_state()
                   if state.state_name == 'REPORTE':
                       analyses[key][i+trim_cnt].reporte += 1
                   else:
                       analyses[key][i+trim_cnt].absent += 1
                       if state.state_name == 'ABS_NON_EXC':
                           analyses[key][i+trim_cnt].abs_non_exc += 1
                       elif state.state_name == 'ABS_EXC':
                           analyses[key][i+trim_cnt].abs_exc += 1
                       elif state.state_name == 'ABS_INTER':
                           analyses[key][i+trim_cnt].abs_inter += 1
                       elif state.state_name == 'ANNUL_NOUS':
                           analyses[key][i+trim_cnt].annul_nous += 1
                       elif state.state_name == 'ANNUL_FAMILLE':
                           analyses[key][i+trim_cnt].annul_famille += 1
                       elif state.state_name == 'ABS_ESS_PPS':
                           analyses[key][i+trim_cnt].abs_ess_pps += 1
                       elif state.state_name == 'ENF_HOSP':
                           analyses[key][i+trim_cnt].enf_hosp += 1
               else:
                   analyses[key][i+trim_cnt].acts_present += 1
                   if statistic.in_service.name == 'CMPP':
                       if not a.is_billable():
                           analyses[key][i+trim_cnt].non_facturables += 1
                       elif a.is_lost:
                           analyses[key][i+trim_cnt].perdus += 1
                       elif a.get_state().state_name == 'ACT_DOUBLE':
                           analyses[key][i+trim_cnt].doubles += 1
                       elif a.is_billed:
                           analyses[key][i+trim_cnt].factures += 1
                           if a.invoice_set.latest('created').first_tag[0] == 'D':
                               analyses[key][i+trim_cnt].diag += 1
                           else:
                               analyses[key][i+trim_cnt].trait += 1
                       else:
                           analyses[key][i+trim_cnt].restants_a_fac += 1
                           if a.invoice_set.all():
                               analyses[key][i+trim_cnt].refac += 1
           analyses[key][i+trim_cnt].total = analyses[key][i+trim_cnt].non_pointe + analyses[key][i+trim_cnt].reporte + analyses[key][i+trim_cnt].absent + analyses[key][i+trim_cnt].acts_present
           analyses[key][i+trim_cnt].pointe = analyses[key][i+trim_cnt].total - analyses[key][i+trim_cnt].non_pointe
           percent_abs = 100
           if not analyses[key][i+trim_cnt].pointe or not analyses[key][i+trim_cnt].absent:
               percent_abs = 0
           elif analyses[key][i+trim_cnt].absent:
               percent_abs = (analyses[key][i+trim_cnt].absent/float(analyses[key][i+trim_cnt].pointe))*100
           analyses[key][i+trim_cnt].percent_abs = "%.2f" % percent_abs
           if statistic.in_service.name == 'CMPP':
               analyses[key][i+trim_cnt].facturables = analyses[key][i+trim_cnt].acts_present - analyses[key][i+trim_cnt].non_facturables
               analyses[key][i+trim_cnt].really_facturables = analyses[key][i+trim_cnt].facturables - analyses[key][i+trim_cnt].perdus - analyses[key][i+trim_cnt].doubles
               analyses[key][i+trim_cnt].nf = analyses[key][i+trim_cnt].perdus + analyses[key][i+trim_cnt].doubles + analyses[key][i+trim_cnt].non_facturables # + analyses[key][i+trim_cnt].reporte
               percent_nf = 100
               if not analyses[key][i+trim_cnt].pointe or not analyses[key][i+trim_cnt].nf:
                   percent_nf = 0
               elif analyses[key][i+trim_cnt].nf:
                   percent_nf = (analyses[key][i+trim_cnt].nf/float(analyses[key][i+trim_cnt].pointe))*100
               analyses[key][i+trim_cnt].percent_nf = "%.2f" % percent_nf
               if analyses[key][i+trim_cnt].days:
                   analyses[key][i+trim_cnt].fact_per_day = "%.2f" % (analyses[key][i+trim_cnt].really_facturables / float(analyses[key][i+trim_cnt].days))
           if analyses[key][i+trim_cnt].moving_time and analyses[key][i+trim_cnt].intervenants:
               analyses[key][i+trim_cnt].moving_time_per_intervene = analyses[key][i+trim_cnt].moving_time / analyses[key][i+trim_cnt].intervenants
           if analyses[key][i+trim_cnt].moving_time and analyses[key][i+trim_cnt].acts_present:
               analyses[key][i+trim_cnt].moving_time_per_act = analyses[key][i+trim_cnt].moving_time / analyses[key][i+trim_cnt].acts_present
       def annual_activity_trimester_analysis(statistic, start_day, analyses, key, i, trim_cnt, participant=None):
           analyses[key].append(AnnualActivityProcessingColumn())
           rd = relativedelta(months=1)
           sd = start_day + i * rd
           start = start_day + (i-2) * rd
           end = sd + rd
           acts = None
           if participant:
               acts = Act.objects.filter(date__gte=start.date(),
                   date__lt=end.date(), patient__service=statistic.in_service,
                   doctors__in=[participant])
           else:
               acts = Act.objects.filter(date__gte=start.date(),
                   date__lt=end.date(), patient__service=statistic.in_service)
           for row in ANNUAL_ACTIVITY_ROWS:
               if row == 'percent_abs':
                   pointe = analyses[key][i+trim_cnt-1].pointe + analyses[key][i-2+trim_cnt].pointe + analyses[key][i-3+trim_cnt].pointe
                   tot_abs = analyses[key][i+trim_cnt-1].absent + analyses[key][i-2+trim_cnt].absent + analyses[key][i-3+trim_cnt].absent
                   percent_abs = 100
                   if not pointe or not tot_abs:
                       percent_abs = 0
                   elif tot_abs:
                       percent_abs = (tot_abs/float(pointe))*100
                   analyses[key][i+trim_cnt].percent_abs = "%.2f" % percent_abs
               elif row == 'percent_nf':
                   pointe = analyses[key][i+trim_cnt-1].pointe + analyses[key][i-2+trim_cnt].pointe + analyses[key][i-3+trim_cnt].pointe
                   tot_nf = analyses[key][i+trim_cnt-1].nf + analyses[key][i-2+trim_cnt].nf + analyses[key][i-3+trim_cnt].nf
                   percent_nf = 100
                   if not pointe or not tot_nf:
                       percent_nf = 0
                   elif tot_nf:
                       percent_nf = (tot_nf/float(pointe))*100
                   analyses[key][i+trim_cnt].percent_nf = "%.2f" % percent_nf
               elif row == 'patients':
                   analyses[key][i+trim_cnt].patients = acts.aggregate(Count('patient', distinct=True))['patient__count']
               elif row == 'intervenants':
                   analyses[key][i+trim_cnt].intervenants = acts.aggregate(Count('doctors', distinct=True))['doctors__count']
               elif row == 'fact_per_day':
                   if analyses[key][i+trim_cnt].days:
                       analyses[key][i+trim_cnt].fact_per_day = "%.2f" % (analyses[key][i+trim_cnt].really_facturables / float(analyses[key][i+trim_cnt].days))
               elif row == 'moving_time_per_intervene':
                   if analyses[key][i+trim_cnt].moving_time and analyses[key][i+trim_cnt].intervenants:
                       analyses[key][i+trim_cnt].moving_time_per_intervene = analyses[key][i+trim_cnt].moving_time / analyses[key][i+trim_cnt].intervenants
               elif row == 'moving_time_per_act':
                   if analyses[key][i+trim_cnt].moving_time and analyses[key][i+trim_cnt].acts_present:
                       analyses[key][i+trim_cnt].moving_time_per_act = analyses[key][i+trim_cnt].moving_time / analyses[key][i+trim_cnt].acts_present
               else:
                   setattr(analyses[key][i+trim_cnt], row, getattr(analyses[key][i+trim_cnt-1], row) + getattr(analyses[key][i-2+trim_cnt], row) + getattr(analyses[key][i-3+trim_cnt], row))
       def annual_activity_synthesis_analysis(statistic, start_day, end_day, analyses, key, participant=None):
           analyses[key].append(AnnualActivityProcessingColumn())
           acts = None
           if participant:
               acts = Act.objects.filter(date__gte=start_day.date(),
                   date__lt=end_day.date(), patient__service=statistic.in_service,
                   doctors__in=[participant])
           else:
               acts = Act.objects.filter(date__gte=start_day.date(),
                   date__lt=end_day.date(), patient__service=statistic.in_service)
           for row in ANNUAL_ACTIVITY_ROWS:
               if row == 'percent_abs':
                   tot_abs = 0
                   pointe = 0
                   for i in (3, 7, 11, 15):
                       pointe += analyses[key][i].pointe
                       tot_abs += analyses[key][i].absent
                   percent_abs = 100
                   if not pointe or not tot_abs:
                       percent_abs = 0
                   elif tot_abs:
                       percent_abs = (tot_abs/float(pointe))*100
                   analyses[key][16].percent_abs = "%.2f" % percent_abs
               elif row == 'percent_nf':
                   tot_nf= 0
                   pointe = 0
                   for i in (3, 7, 11, 15):
                       pointe += analyses[key][i].pointe
                       tot_nf += analyses[key][i].nf
                   percent_nf = 100
                   if not pointe or not tot_nf:
                       percent_nf = 0
                   elif tot_nf:
                       percent_nf = (tot_nf/float(pointe))*100
                   analyses[key][16].percent_nf = "%.2f" % percent_nf
               elif row == 'patients':
                   analyses[key][16].patients = acts.aggregate(Count('patient', distinct=True))['patient__count']
               elif row == 'intervenants':
                   analyses[key][16].intervenants = acts.aggregate(Count('doctors', distinct=True))['doctors__count']
               elif row == 'fact_per_day':
                   if analyses[key][16].days:
                       analyses[key][16].fact_per_day = "%.2f" % (analyses[key][16].really_facturables / float(analyses[key][16].days))
               elif row == 'moving_time_per_intervene':
                   if analyses[key][16].moving_time and analyses[key][16].intervenants:
                       analyses[key][16].moving_time_per_intervene = analyses[key][16].moving_time / analyses[key][16].intervenants
               elif row == 'moving_time_per_act':
                   if analyses[key][16].moving_time and analyses[key][16].acts_present:
                       analyses[key][16].moving_time_per_act = analyses[key][16].moving_time / analyses[key][16].acts_present
               else:
                   val = 0
                   if row == 'moving_time':
                       val = timedelta()
                   for i in (3, 7, 11, 15):
                       val += getattr(analyses[key][i], row)
                   setattr(analyses[key][16], row, val)
       def strfdelta(tdelta, fmt):
           if not tdelta:
               return '0'
           d = {"days": tdelta.days}
           d["hours"], rem = divmod(tdelta.seconds, 3600)
           d["minutes"], d["seconds"] = divmod(rem, 60)
           return fmt.format(**d)
       def annual_activity_build_tables(statistic, analyses, key, label, data_tables):
           table_1 = []
           table_1_label = label + ' - général'
           table_1.append([table_1_label] + ANNUAL_ACTIVITY_COLUMN_LABELS)
           rows = []
           row = ['Proposés']
           for column in analyses[key]:
               row.append(column.total)
           rows.append(row)
           row = ['Non pointés']
           for column in analyses[key]:
               row.append(column.non_pointe)
           rows.append(row)
           row = ['Absences']
           for column in analyses[key]:
               row.append(column.absent)
           rows.append(row)
           row = ['Reportés']
           for column in analyses[key]:
               row.append(column.reporte)
           rows.append(row)
           row = ['Présents']
           for column in analyses[key]:
               row.append(column.acts_present)
           rows.append(row)
           if statistic.in_service.name == 'CMPP':
               row = ['Facturables']
               for column in analyses[key]:
                   row.append(column.really_facturables)
               rows.append(row)
               row = ['Facturés']
               for column in analyses[key]:
                   row.append(column.factures)
               rows.append(row)
               row = ['Diagnostics']
               for column in analyses[key]:
                   row.append(column.diag)
               rows.append(row)
               row = ['Traitements']
               for column in analyses[key]:
                   row.append(column.trait)
               rows.append(row)
               row = ['Restants à facturer']
               for column in analyses[key]:
                   row.append(column.restants_a_fac)
               rows.append(row)
               row = ['Dont en refact.']
               for column in analyses[key]:
                   row.append(column.refac)
               rows.append(row)
           row = ['Patients']
           for column in analyses[key]:
               row.append(column.patients)
           rows.append(row)
           row = ['Intervenants']
           for column in analyses[key]:
               row.append(column.intervenants)
           rows.append(row)
           row = ['Jours']
           for column in analyses[key]:
               row.append(column.days)
           rows.append(row)
           if statistic.in_service.name == 'CMPP':
               row = ['Facturables / jour']
               for column in analyses[key]:
                   row.append(column.fact_per_day)
               rows.append(row)
           row = ['Temps de déplacement']
           for column in analyses[key]:
               row.append(strfdelta(column.moving_time, "{hours}h {minutes}m"))
               if column.moving_time.days:
                   row.append(strfdelta(column.moving_time, "{days}j {hours}h {minutes}m"))
           rows.append(row)
           row = ['Temps de déplacement par intervenant']
           for column in analyses[key]:
               row.append(strfdelta(column.moving_time_per_intervene, "{hours}h {minutes}m"))
               if column.moving_time_per_intervene.days:
                   row.append(strfdelta(column.moving_time_per_intervene, "{days}j {hours}h {minutes}m"))
           rows.append(row)
           row = ['Temps de déplacement par acte']
           for column in analyses[key]:
               row.append(strfdelta(column.moving_time_per_act, "{hours}h {minutes}m"))
               if column.moving_time_per_act.days:
                   row.append(strfdelta(column.moving_time_per_act, "{days}j {hours}h {minutes}m"))
           rows.append(row)
           table_1.append(rows)
           data_tables.append(table_1)
           table_2 = []
           table_2_label = label + ' - absences'
           table_2.append([table_2_label] + ANNUAL_ACTIVITY_COLUMN_LABELS)
           rows = []
           row = ['Pointés']
           for column in analyses[key]:
               row.append(column.pointe)
           rows.append(row)
           row = ['Absences']
           for column in analyses[key]:
               row.append(column.absent)
           rows.append(row)
           row = ['% absences / pointés']
           for column in analyses[key]:
               row.append(column.percent_abs)
           rows.append(row)
           row = ['Excusées']
           for column in analyses[key]:
               row.append(column.abs_exc)
           rows.append(row)
           row = ['Non excusées']
           for column in analyses[key]:
               row.append(column.abs_non_exc)
           rows.append(row)
           row = ["De l'intervenant"]
           for column in analyses[key]:
               row.append(column.abs_inter)
           rows.append(row)
           row = ["Annulés par nous"]
           for column in analyses[key]:
               row.append(column.annul_nous)
           rows.append(row)
           row = ['Annulés par la famille']
           for column in analyses[key]:
               row.append(column.annul_famille)
           rows.append(row)
           row = ['ESS PPS']
           for column in analyses[key]:
               row.append(column.abs_ess_pps)
           rows.append(row)
           row = ['Hospitalisations']
           for column in analyses[key]:
               row.append(column.enf_hosp)
           rows.append(row)
           table_2.append(rows)
           data_tables.append(table_2)
           if statistic.in_service.name == 'CMPP':
               table_3 = []
               table_3_label = label + ' - non fact.'
               table_3.append([table_3_label] + ANNUAL_ACTIVITY_COLUMN_LABELS)
               rows = []
               row = ['Pointés']
               for column in analyses[key]:
                   row.append(column.pointe)
               rows.append(row)
               row = ['Présents']
               for column in analyses[key]:
                   row.append(column.acts_present)
               rows.append(row)
               row = ['De type non fact.']
               for column in analyses[key]:
                   row.append(column.non_facturables)
               rows.append(row)
               row = ['De type fact.']
               for column in analyses[key]:
                   row.append(column.facturables)
               rows.append(row)
               row = ['Perdus']
               for column in analyses[key]:
                   row.append(column.perdus)
               rows.append(row)
               row = ['En doubles']
               for column in analyses[key]:
                   row.append(column.doubles)
               rows.append(row)
               row = ['Non facturables']
               for column in analyses[key]:
                   row.append(column.nf)
               rows.append(row)
               row = ['% NF / pointés']
               for column in analyses[key]:
                   row.append(column.percent_nf)
               rows.append(row)
               table_3.append(rows)
               data_tables.append(table_3)
       def run_annual_activity(statistic, start_day, analyses, key, label, data_tables, participant=None):
           analyses[key] = list()
           trim_cnt = 0
           for i in range(0, 12):
               annual_activity_month_analysis(statistic, start_day, analyses, key, i, trim_cnt, participant)
               if not (i + 1) % 3:
                   trim_cnt += 1
                   annual_activity_trimester_analysis(statistic, start_day, analyses, key, i, trim_cnt, participant)
           end_day = datetime(start_day.year+1, 1, 1)
           annual_activity_synthesis_analysis(statistic, start_day, end_day, analyses, key, participant)
           annual_activity_build_tables(statistic, analyses, key, label, data_tables)
       def annual_activity(statistic):
           if not statistic.in_service:
               return None
           start_day = datetime(datetime.today().year, 1, 1)
           if statistic.in_year:
               start_day = datetime(statistic.in_year, 1, 1)
           data_tables = list()
           analyses = dict()
           if not statistic.in_participants:
               run_annual_activity(statistic, start_day, analyses, 'global', str(statistic.in_year) + ' - Tous', data_tables, participant=None)
           else:
               for participant in statistic.in_participants:
                   run_annual_activity(statistic, start_day, analyses, participant.id, str(statistic.in_year) + ' - ' + str(participant), data_tables, participant=participant)
           return [data_tables]
       def patients_per_worker_for_period(statistic):
           if not statistic.in_service:
               return None
           data_tables = []
           data = []
           data.append(['Intervenants', 'Nombre', 'Patients'])
           values = []
           if not statistic.in_end_date:
               statistic.in_end_date = datetime.today()
           if not statistic.in_start_date:
               statistic.in_start_date = datetime(statistic.in_end_date.year, 1, 1)
           acts = None
           if statistic.in_patients:
               acts = Act.objects.filter(date__gte=statistic.in_start_date,
                   date__lte=statistic.in_end_date,
                   patient__service=statistic.in_service,
                   patient__in=statistic.in_patients)
           else:
               acts = Act.objects.filter(date__gte=statistic.in_start_date,
                   date__lte=statistic.in_end_date,
                   patient__service=statistic.in_service)
           analyse = dict()
           for act in acts:
               for intervene in act.doctors.all():
                   if statistic.in_participants:
                       if intervene in statistic.in_participants:
                           analyse.setdefault(intervene, []).append(str(act.patient))
                   else:
                       analyse.setdefault(intervene, []).append(str(act.patient))
           o_analyse = OrderedDict(sorted(analyse.items(), key=lambda t: t[0]))
           for intervene, patients in o_analyse.iteritems():
               lst = list(set(patients))
               values.append([str(intervene), len(lst), lst])
           data.append(values)
           data_tables.append(data)
           return [data_tables]
       def active_patients_by_state_only(statistic):
           if not statistic.in_service:
               return None
           if not statistic.in_start_date:
               statistic.in_start_date = datetime.today()
           active_states = None
           if statistic.in_service.name == 'CMPP':
               active_states = ('TRAITEMENT', 'DIAGNOSTIC')
           elif statistic.in_service.name == 'CAMSP':
               active_states = ('SUIVI', )
           else:
               active_states = ('TRAITEMENT', )
           patients = [(p.last_name, p.first_name, p.paper_id) \
               for p in PatientRecord.objects.filter(service=statistic.in_service) \
                   if p.get_state_at_day(
                       statistic.in_start_date).status.type in active_states]
           data_tables_set=[[[['En date du :', formats.date_format(statistic.in_start_date, "SHORT_DATE_FORMAT"), len(patients)]]]]
           data = []
           data.append(['Nom', 'Prénom', 'N° Dossier'])
           p_list = []
           for ln, fn, pid in patients:
               ln = ln or ''
               if len(ln) > 1:
                   ln = ln[0].upper() + ln[1:].lower()
               fn = fn or ''
               if len(fn) > 1:
                   fn = fn[0].upper() + fn[1:].lower()
               p_list.append((ln, fn, str(pid or '')))
           data.append(sorted(p_list,
               key=lambda k: k[0]+k[1]))
           data_tables_set[0].append(data)
           return data_tables_set
       def patients_protection(statistic):
           if not statistic.in_service:
               return None
           if not statistic.in_start_date:
               statistic.in_start_date = datetime.today()
           patients = PatientRecord.objects.filter(protectionstate__isnull=False).distinct()
           protection_states = [p.get_protection_state_at_date(
                   statistic.in_start_date) for p in patients
                   if p.get_protection_state_at_date(statistic.in_start_date)]
           analyse = {}
           for state in protection_states:
               analyse.setdefault(state.status.name, 0)
               analyse[state.status.name] += 1
           data_tables_set=[[[['En date du :', formats.date_format(statistic.in_start_date, "SHORT_DATE_FORMAT"), len(protection_states)]]]]
           data = []
           data.append(['Mesure de protection', 'Nombre de dossiers'])
           data.append(analyse.items())
           data_tables_set[0].append(data)
           return data_tables_set
       def active_patients_with_act(statistic):
           def process(patients_list, title):
               data_tables = []
               data = []
               data.append([title, len(patients_list), '', ''])
               data_tables.append(data)
               data = []
               data.append(['Nom', 'Prénom', 'N° Dossier'])
               p_list = []
               for p in patients_list:
                   ln, fn, pid = p.last_name, p.first_name, p.paper_id
                   ln = ln or ''
                   if len(ln) > 1:
                       ln = ln[0].upper() + ln[1:].lower()
                   fn = fn or ''
                   if len(fn) > 1:
                       fn = fn[0].upper() + fn[1:].lower()
                   p_list.append((ln, fn, str(pid or '')))
               data.append(sorted(p_list,
                   key=lambda k: k[0]+k[1]))
               data_tables.append(data)
               return data_tables
           if not statistic.in_service:
               return None
           if not statistic.in_end_date:
               statistic.in_end_date = datetime.today()
           if not statistic.in_start_date:
               statistic.in_start_date = datetime(statistic.in_end_date.year, 1, 1)
           active_states = None
           if statistic.in_service.name == 'CMPP':
               active_states = ('TRAITEMENT', 'DIAGNOSTIC')
           elif statistic.in_service.name == 'CAMSP':
               active_states = ('SUIVI', )
           else:
               active_states = ('TRAITEMENT', )
           data_tables_set = []
           data_tables = []
           data = []
           data.append(['Période', 'Jours'])
           data.append([("%s - %s"
               % (formats.date_format(statistic.in_start_date, "SHORT_DATE_FORMAT"),
               formats.date_format(statistic.in_end_date, "SHORT_DATE_FORMAT")),
               (statistic.in_end_date-statistic.in_start_date).days+1)])
           data_tables.append(data)
           data_tables_set.append(data_tables)
           acts_base = Act.objects.filter(
               date__gte=statistic.in_start_date,
               date__lte=statistic.in_end_date,
               patient__service=statistic.in_service)
           acts_valide = acts_base.filter(valide=True)
           acts_valide_patients_ids = acts_valide.order_by('patient').\
               distinct('patient').values_list('patient')
           acts_valide_patients = PatientRecord.objects.filter(
               id__in=[patient[0] for patient in acts_valide_patients_ids])
           all_patients_ids = acts_base.order_by('patient').distinct('patient').\
               values_list('patient')
           acts_not_valide_patients = PatientRecord.objects.filter(
               id__in=[patient[0] for patient in all_patients_ids
                   if not patient in acts_valide_patients_ids])
           p_val = dict()
           for p in acts_valide_patients:
               p_val.setdefault(p.get_state_at_day(statistic.in_end_date).status, []).append(p)
           for k, v in p_val.items():
               data_tables_set.append(process(v, "Patients avec un acte validé et dans l'état '%s' en date du %s" % (k, formats.date_format(statistic.in_end_date, "SHORT_DATE_FORMAT"))))
           p_val = dict()
           for p in acts_not_valide_patients:
               p_val.setdefault(p.get_state_at_day(statistic.in_end_date).status, []).append(p)
           for k, v in p_val.items():
               data_tables_set.append(process(v, "Patients avec sans acte validé et dans l'état '%s' en date du %s" % (k, formats.date_format(statistic.in_end_date, "SHORT_DATE_FORMAT"))))
           return data_tables_set
       def closed_files(statistic):
           if not statistic.in_service:
               return None
           data_tables = []
           data1 = []
           data1.append(['Période', 'Jours',
               'Nombre de dossiers clos durant la période', 'PEC totale', 'PEC moyenne', "Dossiers qui ne sont plus clos"])
           data2 = []
           data2.append(['Nom', 'Prénom', 'N° Dossier', 'Date de clôture', 'Durée de la PEC', "N'est plus clos"])
           if not statistic.in_end_date:
               statistic.in_end_date = datetime.today()
           if not statistic.in_start_date:
               statistic.in_start_date = datetime(statistic.in_end_date.year, 1, 1)
           closed_records = FileState.objects.filter(status__type='CLOS',
               date_selected__gte=statistic.in_start_date,
               date_selected__lte=statistic.in_end_date). \
               order_by('patient').distinct('patient').\
               values_list('patient')
           closed_records = PatientRecord.objects.filter(service=statistic.in_service, id__in=[patient[0]
               for patient in closed_records])
           total_pec = 0
           p_list = []
           not_closed_now = 0
           for record in closed_records:
               ln = record.last_name or ''
               if len(ln) > 1:
                   ln = ln[0].upper() + ln[1:].lower()
               fn = record.first_name or ''
               if len(fn) > 1:
                   fn = fn[0].upper() + fn[1:].lower()
               current_state = ''
               close_date = record.last_state.date_selected.date()
               if record.get_current_state().status.type != 'CLOS':
                   not_closed_now += 1
                   current_state = record.get_current_state().status.name + \
                       ' le ' + formats.date_format(record.get_current_state(). \
                       date_selected, "SHORT_DATE_FORMAT")
                   close_date = FileState.objects.filter(status__type='CLOS',
                       patient=record).order_by('-date_selected')[0].date_selected.date()
               p_list.append((ln, fn, str(record.paper_id or ''),
                   close_date,
                   record.care_duration_since_last_contact_or_first_act,
                   current_state))
               total_pec += record.care_duration_since_last_contact_or_first_act
           data2.append(sorted(p_list,
               key=lambda k: k[0]+k[1]))
           avg_pec = 0
           if closed_records.count() and total_pec:
               avg_pec = total_pec/closed_records.count()
           data1.append([("%s - %s"
               % (formats.date_format(statistic.in_start_date, "SHORT_DATE_FORMAT"),
               formats.date_format(statistic.in_end_date, "SHORT_DATE_FORMAT")),
               (statistic.in_end_date-statistic.in_start_date).days+1,
               closed_records.count(), total_pec, avg_pec, not_closed_now)])
           data_tables.append(data1)
           data_tables.append(data2)
           days_states = {}
           for record in closed_records:
               for state, duration in record.get_states_history_with_duration():
                   days_states.setdefault(state.status, 0)
                   days_states[state.status] += duration.days
           data = []
           data.append(["Etat des dossiers", "Nombre de jours total", "Nombre de jours moyen par dossier"])
           values = []
           for status, duration in days_states.iteritems():
               values.append((status.name, duration, duration/closed_records.count()))
           data.append(values)
           data_tables.append(data)
           return [data_tables]
       def patients_details(statistic):
           if not statistic.in_service:
               return None
           data_tables_set = []
           if not statistic.in_end_date:
               statistic.in_end_date = datetime.today()
           if not statistic.in_start_date:
               statistic.in_start_date = datetime(statistic.in_end_date.year, 1, 1)
           acts = None
           if statistic.in_patients:
               acts = Act.objects.filter(date__gte=statistic.in_start_date,
                   date__lte=statistic.in_end_date,
                   patient__service=statistic.in_service,
                   patient__in=statistic.in_patients)
           else:
               acts = Act.objects.filter(date__gte=statistic.in_start_date,
                   date__lte=statistic.in_end_date,
                   patient__service=statistic.in_service)
           analyse = dict()
           for act in acts:
               analyse.setdefault(act.patient, []).append(act)
           o_analyse = OrderedDict(sorted(analyse.items(),
               key=lambda t: t[0].last_name))
           data = []
           data.append(['Période', 'Jours',
               'Nombre de dossiers'])
           data.append([("%s - %s"
               % (formats.date_format(statistic.in_start_date, "SHORT_DATE_FORMAT"),
               formats.date_format(statistic.in_end_date, "SHORT_DATE_FORMAT")),
               (statistic.in_end_date-statistic.in_start_date).days+1,
               len(o_analyse))])
           data_tables_set.append([data])
           for patient, acts in o_analyse.iteritems():
               data_tables = list()
               data_tables.append([["%s %s" % (str(patient), str(patient.paper_id))]])
               data_tables.append([["Durée de la prise en charge (depuis le premier acte)"], [[patient.care_duration_since_last_contact_or_first_act]]])
               data = []
               data.append(["Statut de l'acte", 'Nombre'])
               values = []
               values.append(('Proposés', len(acts)))
               np, absent = 0, 0
               act_types = dict()
               for a in acts:
                   if a.is_new():
                       np += 1
                   elif a.is_absent():
                       absent += 1
                   act_types.setdefault(a.act_type, []).append(a)
               values.append(('Non pointés', np))
               values.append(('Présents', len(acts) - np - absent))
               values.append(('Absents', absent))
               data.append(values)
               data_tables.append(data)
               data = []
               data.append(["Types d'acte", "Nombre d'actes proposés", "Nombre d'actes réalisés"])
               values = []
               for act_type, acts in act_types.iteritems():
                   values.append((act_type, len(acts), len([a for a in acts if a.is_present()])))
               data.append(values)
               data_tables.append(data)
               data = []
               data.append(["Historique", "Nombre de jours par état"])
               values = []
               for state, duration in patient.get_states_history_with_duration():
                   values.append(("%s (%s)" % (state.status.name,
                       formats.date_format(state.date_selected,
                       "SHORT_DATE_FORMAT")), duration.days))
               data.append(values)
               data_tables.append(data)
               contacts = FileState.objects.filter(patient=patient, status__type='ACCUEIL').order_by('date_selected')
               recontact = 'Non'
               last_contact = None
               first_acts_after_contact = None
               if len(contacts) == 1:
                   last_contact = contacts[0]
               elif len(contacts) > 1:
                   recontact = 'Oui'
                   last_contact = contacts[len(contacts)-1]
               if last_contact:
                   # inscription act
                   first_acts_after_contact = Act.objects.filter(patient=patient, date__gte=last_contact.date_selected).order_by('date')
                   if first_acts_after_contact:
                       first_act_after_contact = first_acts_after_contact[0]
                       if first_act_after_contact.date <= statistic.in_end_date.date() and first_act_after_contact.date >= statistic.in_start_date.date():
                           # inscription during the selected date range.
                           waiting_duration = first_act_after_contact.date - last_contact.date_selected.date()
                           data = []
                           data.append(["Date inscription", "Date accueil", 'Attente', 'Réinscription'])
                           values = []
                           values.append((first_act_after_contact.date, last_contact.date_selected.date(), waiting_duration.days, recontact))
                           data.append(values)
                           data_tables.append(data)
               closed_during_range_date = None
               try:
                   closed_during_range_date = FileState.objects.filter(patient=patient, status__type='CLOS',
                       date_selected__gte=statistic.in_start_date,
                       date_selected__lte=statistic.in_end_date).latest('date_selected')
               except:
                   pass
               care_duration = patient.care_duration_since_last_contact_or_first_act
               closure_date = ''
               if closed_during_range_date:
                   closure_date = closed_during_range_date.date_selected.date
               reopen = ''
               if closed_during_range_date and not patient.exit_date:
                   reopen = 'Oui'
               data = []
               data.append(["Durée de la prise en charge", "Clos pendant la période", "Actes suivants la clôture"])
               values = []
               values.append((patient.care_duration_since_last_contact_or_first_act, closure_date, reopen))
               data.append(values)
               data_tables.append(data)
               if patient.mdph_requests.exists():
                   data = []
                   data.append(["Demande(s) MDPH pendant la période", "Date de la demande", "Demande antérieure à la date de début saisie"])
                   values = []
                   for request in patient.mdph_requests.order_by('start_date'):
                       before = 'Non'
                       if request.start_date < statistic.in_start_date.date():
                           before = 'Oui'
                       values.append(('MDPH : ' + request.mdph.department, request.start_date, before))
                   data.append(values)
                   data_tables.append(data)
               if patient.mdph_responses.exists():
                   data = []
                   data.append(["Réponse(s) MDPH pendant la période", "Date de début", "Date de fin"])
                   values = []
                   for response in patient.mdph_responses.order_by('start_date'):
                       values.append(('MDPH : ' + response.mdph.department, response.start_date, response.end_date))
                   data.append(values)
                   data_tables.append(data)
               data_tables_set.append(data_tables)
           return data_tables_set
       def patients_synthesis(statistic):
           if not statistic.in_service:
               return None
           data_tables = []
           data = []
           data.append(['Période', 'Jours',
               'Nombre de dossiers avec un acte validé sur la période',
               "Nombre d'actes validés sur la période"])
           if not statistic.in_end_date:
               statistic.in_end_date = datetime.today()
           if not statistic.in_start_date:
               statistic.in_start_date = datetime(statistic.in_end_date.year, 1, 1)
           acts = Act.objects.filter(valide=True,
               date__gte=statistic.in_start_date,
               date__lte=statistic.in_end_date,
               patient__service=statistic.in_service)
           patients = acts.order_by('patient').distinct('patient').\
               values_list('patient')
           patients = PatientRecord.objects.filter(id__in=[patient[0]
               for patient in patients])
           active_states = None
           if statistic.in_service.name == 'CMPP':
               active_states = ('TRAITEMENT', 'DIAGNOSTIC', )
           elif statistic.in_service.name == 'CAMSP':
               active_states = ('SUIVI', 'BILAN', 'SURVEILLANCE', 'CLOS', )
           else:
               active_states = ('TRAITEMENT', )
           inscriptions = []
           for patient in patients:
               # Select patient if she has been in treament between the selected dates
               traitement_states_tmp = FileState.objects.filter(patient=patient, status__type__in=active_states, date_selected__lte=statistic.in_end_date).order_by('date_selected')
               traitement_states = []
               for ts in traitement_states_tmp:
                   if not ts.get_next_state() or ts.get_next_state().date_selected >= statistic.in_start_date:
                       traitement_states.append(ts)
               # Patient has been in treatment
               # We look for all the treament periods during the selected dates
               # A treament period ends if during the period the file has left treament state
               openings = []
               opening = []
               start = False
               for ts in traitement_states:
                   if start:
                       openings.append(opening)
                       opening = []
                       start = False
                   if ts.get_next_state() and not ts.get_next_state().status.type in active_states and ts.get_next_state().get_next_state() and ts.get_next_state().get_next_state().status.type in active_states:
                       start = True
                   opening.append(ts)
               openings.append(opening)
               # The first treatment state is the first one of each period matching the dates selected.
               # But there could be other treatment state before, like diag before treatment.
               # so We have to look for the very first treament state to look at the first act after
               first_tss = []
               for opening in openings:
                   if len(opening) >= 1:
                       first_ts = opening[0]
                       while first_ts.previous_state and first_ts.previous_state.status.type in active_states:
                           first_ts = first_ts.previous_state
                       contact = None
                       if first_ts.previous_state and first_ts.previous_state.status.type=='ACCUEIL':
                           contact = first_ts.previous_state.date_selected.date()
                       first_tss.append((contact, first_ts))
               # We look to the first act after the datebeginning
               for contact, first_ts in first_tss:
                   acts_tmp = Act.objects.filter(valide=True,
                       date__gte=first_ts.date_selected,
                       patient=patient).order_by('date')
                   if acts_tmp and acts_tmp[0].date >= statistic.in_start_date.date() and acts_tmp[0].date <= statistic.in_end_date.date():
                       waiting_duration = 0
                       if contact:
                           waiting_duration = (acts_tmp[0].date - contact).days
                       inscriptions.append((patient, contact, first_ts.date_selected.date(), acts_tmp[0].date, waiting_duration))
           if statistic.inscriptions:
               patients = PatientRecord.objects.filter(id__in=[p.id for p, _, _, _, _ in inscriptions])
               acts = acts.filter(patient__in=patients)
           nb_patients = patients.count()
           data.append([("%s - %s"
               % (formats.date_format(statistic.in_start_date, "SHORT_DATE_FORMAT"),
               formats.date_format(statistic.in_end_date, "SHORT_DATE_FORMAT")),
               (statistic.in_end_date-statistic.in_start_date).days+1,
               nb_patients, acts.count())])
           data_tables.append(data)
           data = []
           data.append(['Féminin', 'Masculin'])
           data.append([(patients.filter(gender='2').count(),
               patients.filter(gender='1').count())])
           data_tables.append(data)
           data = []
           data.append(['Durée totale de la PEC',
               'Durée moyenne de la PEC par patient'])
           pec_total = sum([p.care_duration_since_last_contact_or_first_act for p in patients])
           data.append([(pec_total, pec_total/nb_patients)])
           data_tables.append(data)
           data = []
           data.append(["Etat du dossier à ce jour (%s)" % formats.date_format(datetime.today(), "SHORT_DATE_FORMAT"), 'Nombre de patients'])
           states = dict()
           for patient in patients:
               states.setdefault(patient.get_current_state().status, []).append(patient)
           values = []
           for state, ps in states.iteritems():
               values.append((state.name, len(ps)))
           data.append(values)
           data_tables.append(data)
           data = []
           data.append(["Etat du dossier en date de fin (%s)" % formats.date_format(statistic.in_end_date, "SHORT_DATE_FORMAT"), 'Nombre de patients'])
           states = dict()
           for patient in patients:
               states.setdefault(patient.get_state_at_day(statistic.in_end_date).status, []).append(patient)
           values = []
           for state, ps in states.iteritems():
               values.append((state.name, len(ps)))
           data.append(values)
           data_tables.append(data)
           data = []
           data.append(["Inscriptions (premier acte suivant le début d'une phase de traitement dans la période)", "Durée moyenne de l'attente"])
           data.append([(len(inscriptions), sum([wd for _, _, _, _, wd in inscriptions])/len(inscriptions) if len(inscriptions) else 0)])
           data_tables.append(data)
           if inscriptions:
               data = []
               data.append(['Nom', 'Prénom', "Numéro papier", "Date premier acte", "Date passage en traitement", "Date de contact", "Attente"])
               values = []
               for patient, contact, first_ts, first_act, waiting_duration in sorted(inscriptions, key=lambda p: (p[0].last_name, p[0].first_name)):
                   values.append((patient.last_name, patient.first_name, patient.paper_id, first_act, first_ts, contact, waiting_duration))
               data.append(values)
               data_tables.append(data)
           closed_records = FileState.objects.filter(status__type='CLOS',
               date_selected__gte=statistic.in_start_date,
               date_selected__lte=statistic.in_end_date, patient__in=patients). \
               order_by('patient').distinct('patient').\
               values_list('patient')
           closed_records = PatientRecord.objects.filter(service=statistic.in_service, id__in=[patient[0]
               for patient in closed_records])
           total_pec = 0
           not_closed_now = 0
           for record in closed_records:
               if record.get_current_state().status.type != 'CLOS':
                   not_closed_now += 1
               total_pec += record.care_duration_since_last_contact_or_first_act
           avg_pec = 0
           if closed_records.count() and total_pec:
               avg_pec = total_pec/closed_records.count()
           if closed_records.count():
               data = []
               data.append(['Clos dans la période', 'Durée totale de la PEC', 'Durée moyenne de la PEC', "Qui ne sont plus clos à ce jour"])
               data.append([(closed_records.count(), total_pec, avg_pec, not_closed_now)])
               data_tables.append(data)
           mdph_requests = 0
           mdph_requests_before = 0
           for patient in patients:
               if patient.mdph_requests.exists():
                   mdph_requests += 1
                   # We only look to the last one
                   if patient.mdph_requests.order_by('-start_date')[0].start_date < statistic.in_start_date.date():
                       mdph_requests_before +=1
           data = []
           data.append(['Dossier avec une demande MDPH', "Dont la dernière demande a été faite avant la période"])
           data.append([(mdph_requests, mdph_requests_before)])
           data_tables.append(data)
           birth_years = dict()
           patients_without_birthyear = []
           for patient in patients:
               try:
                   birth_years.setdefault(patient.birthdate.year, []).append(patient)
               except:
                   patients_without_birthyear.append(patient)
           data = []
           data.append(['Année de naissance', "Nombre de dossiers", "%"])
           values = []
           for birth_year, pts in birth_years.iteritems():
               values.append((birth_year, len(pts), "%.2f" % (len(pts) / float(len(patients)) * 100)))
           values.append(('Non renseignée',  len(patients_without_birthyear), "%.2f" % (len(patients_without_birthyear) / float(len(patients)) * 100)))
           data.append(values)
           data_tables.append(data)
           if patients_without_birthyear:
               data = []
               data.append(["Patients sans date de naissance"])
               values = [[patients_without_birthyear]]
               data.append(values)
               data_tables.append(data)
           lower_bounds = [0, 3, 5, 7, 11, 16, 20, 25, 30, 35, 40, 45, 50, 55, 60, 75, 85, 96]
           anap_code = 198
           data = []
           data.append(["Code ANAP", "Tranche d'âge (au %s)" \
               % formats.date_format(statistic.in_end_date, "SHORT_DATE_FORMAT"),
               "Nombre de dossiers", "%"])
           values = []
           for i in range(len(lower_bounds)):
               lower_bound = lower_bounds[i]
               if i == len(lower_bounds) - 1:
                   values.append([anap_code, "De %d ans et plus" % lower_bound, 0, None])
               else:
                   values.append([anap_code, "De %d à %d ans" % (lower_bound, lower_bounds[i + 1] - 1), 0, None])
               anap_code += 1
           unknown = 0
           for patient in patients:
               try:
                   age = statistic.in_end_date.date() - patient.birthdate
                   age = age.days / 365
                   i = 0
                   while age >= lower_bounds[i+1]:
                       i += 1
                       if i == len(lower_bounds) - 1:
                           break
                   values[i][2] += 1
               except:
                   unknown += 1
           for value in values:
               value[3] = "%.2f" % (value[2] / float(len(patients)) * 100)
           values.append(['', "Non renseignée", unknown, "%.2f" % (unknown / float(len(patients)) * 100)])
           data.append(values)
           data_tables.append(data)
           jobs = dict()
           no_job = 0
           for patient in patients:
               job = False
               if patient.job_mother:
                   jobs.setdefault(patient.job_mother, []).append(patient)
                   job = True
               else:
                   for contact in patient.contacts.all():
                       if contact.parente and contact.parente.name == 'Mère':
                           if contact.job:
                               jobs.setdefault(contact.job, []).append(patient)
                               job = True
                           break
               if not job:
                   no_job += 1
           data = []
           data.append(["Profession de la mère", "Nombre de dossiers", "%"])
           values = []
           for job, pts in jobs.iteritems():
               values.append((job, len(pts), "%.2f" % (len(pts) / float(len(patients)) * 100)))
           values.append(("Non renseignée", no_job, "%.2f" % (no_job / float(len(patients)) * 100)))
           data.append(values)
           data_tables.append(data)
           jobs = dict()
           no_job = 0
           for patient in patients:
               job = False
               if patient.job_father:
                   jobs.setdefault(patient.job_father, []).append(patient)
                   job = True
               else:
                   for contact in patient.contacts.all():
                       if contact.parente and contact.parente.name == 'Père':
                           if contact.job:
                               jobs.setdefault(contact.job, []).append(patient)
                               job = True
                           break
               if not job:
                   no_job += 1
           data = []
           data.append(["Profession du père", "Nombre de dossiers", "%"])
           values = []
           for job, pts in jobs.iteritems():
               values.append((job, len(pts), "%.2f" % (len(pts) / float(len(patients)) * 100)))
           values.append(("Non renseignée", no_job, "%.2f" % (no_job / float(len(patients)) * 100)))
           data.append(values)
           data_tables.append(data)
           provenances = dict()
           unknown = 0
           for patient in patients:
               if patient.provenance:
                   provenances.setdefault(patient.provenance, []).append(patient)
               else:
                   unknown += 1
           data = []
           data.append(["Provenances", "Nombre de dossiers", "%"])
           values = []
           for provenance, pts in provenances.iteritems():
               values.append((provenance, len(pts), "%.2f" % (len(pts) / float(len(patients)) * 100)))
           values.append(('Non renseignée', unknown, "%.2f" % (unknown / float(len(patients)) * 100)))
           data.append(values)
           data_tables.append(data)
           outmotives = dict()
           unknown = 0
           for patient in patients:
               if patient.outmotive:
                   outmotives.setdefault(patient.outmotive, []).append(patient)
               else:
                   unknown += 1
           data = []
           data.append(["Motifs de sortie", "Nombre de dossiers", "%"])
           values = []
           for outmotive, pts in outmotives.iteritems():
               values.append((outmotive, len(pts), "%.2f" % (len(pts) / float(len(patients)) * 100)))
           values.append(('Non renseigné', unknown, "%.2f" % (unknown / float(len(patients)) * 100)))
           data.append(values)
           data_tables.append(data)
           outtos = dict()
           unknown = 0
           for patient in patients:
               if patient.outto:
                   outtos.setdefault(patient.outto, []).append(patient)
               else:
                   unknown += 1
           data = []
           data.append(["Orientations", "Nombre de dossiers", "%"])
           values = []
           for outto, pts in outtos.iteritems():
               values.append((outto, len(pts), "%.2f" % (len(pts) / float(len(patients)) * 100)))
           values.append(('Non renseigné', unknown, "%.2f" % (unknown / float(len(patients)) * 100)))
           data.append(values)
           data_tables.append(data)
           provenance_places = dict()
           unknown = 0
           for patient in patients:
               if patient.provenanceplace:
                   provenance_places.setdefault(patient.provenanceplace, []).append(patient)
               else:
                   unknown += 1
           data = []
           data.append(["Lieux de provenance", "Nombre de dossiers", "%"])
           values = []
           for provenance_place, pts in provenance_places.iteritems():
               values.append((provenance_place, len(pts), "%.2f" % (len(pts) / float(len(patients)) * 100)))
           values.append(('Non renseigné', unknown, "%.2f" % (unknown / float(len(patients)) * 100)))
           data.append(values)
           data_tables.append(data)
           return [data_tables]
       def acts_synthesis(statistic):
           if not statistic.in_service:
               return None
           if not statistic.in_end_date:
               statistic.in_end_date = datetime.today()
           if not statistic.in_start_date:
               statistic.in_start_date = datetime(statistic.in_end_date.year, 1, 1)
           data_tables_set = []
           data_tables = []
           data = []
           data.append(['Période', 'Jours',
               "Nombre d'actes proposés sur la période",
               "Dossiers concernés",
               "Nombre d'actes réalisés sur la période",
               "Dossiers concernés"])
           acts = Act.objects.filter(date__gte=statistic.in_start_date,
               date__lte=statistic.in_end_date,
               patient__service=statistic.in_service)
           len_patients = len(set([a.patient.id for a in acts]))
           acts_present = [a for a in acts if a.is_present()]
           len_patients_present = len(set([a.patient.id for a in acts_present]))
           len_acts_present = len(acts_present)
           data.append([("%s - %s"
               % (formats.date_format(statistic.in_start_date, "SHORT_DATE_FORMAT"),
               formats.date_format(statistic.in_end_date, "SHORT_DATE_FORMAT")),
               (statistic.in_end_date-statistic.in_start_date).days+1,
               acts.count(), len_patients, len_acts_present, len_patients_present)])
           data_tables.append(data)
           data_tables_set.append(data_tables)
           data_tables=[]
           acts_types = dict()
           for act in acts:
               acts_types.setdefault(act.act_type, []).append(act)
           data = []
           data.append(["Types des actes", "Nombre d'actes proposés", "Nombre de dossiers", "Nombre d'actes réalisés", "Nombre de dossiers"])
           values = []
           for act_type, acts in sorted(acts_types.items(), key=lambda t: t[0].name):
               values.append((act_type, len(acts), len(set([a.patient.id for a in acts])), len([a for a in acts if a.is_present()]), len(set([a.patient.id for a in acts if a.is_present()]))))
           data.append(values)
           data_tables.append(data)
           data_tables_set.append(data_tables)
           data_tables=[]
           acts_count_participants = dict()
           for act in acts_present:
               acts_count_participants.setdefault(act.doctors.count(), []).append(act)
           data = []
           data.append(["Nombre d'intervenants des actes réalisés", "Nombre d'actes", "Nombre de dossiers concernés"])
           values = []
           for number, acts_counted in acts_count_participants.iteritems():
               values.append((number, len(acts_counted), len(set([a.patient.id for a in acts_counted]))))
           data.append(values)
           data_tables.append(data)
           data_tables_set.append(data_tables)
           for act_type, acts in sorted(acts_types.items(), key=lambda t: t[0].name):
               data_tables=[]
               analysis = {'Non pointés': 0,
                   'Reportés': 0, 'Absents': 0, 'Présents': 0}
               for a in acts:
                   if a.is_new():
                       analysis['Non pointés'] += 1
                   elif a.is_absent():
                       state = a.get_state()
                       if state.state_name == 'REPORTE':
                           analysis['Reportés'] += 1
                       else:
                           analysis['Absents'] += 1
                   else:
                       analysis['Présents'] += 1
               data = []
               data.append(["Type d'acte", act_type])
               values = []
               for status, number in analysis.iteritems():
                   values.append((status, number))
               data.append(values)
               data_tables.append(data)
               acts_type_patients = {}
               for act in acts:
                   acts_type_patients.setdefault(act.patient, []).append(act)
               data = []
               data.append(["Patient", "Actes proposés", "Actes réalisés"])
               values = []
               for patient, acts_type in acts_type_patients.iteritems():
                   values.append((patient, len(acts_type), len([a for a in acts_type if a.is_present()])))
               data.append(values)
               data_tables.append(data)
               data_tables_set.append(data_tables)
           return data_tables_set
       def acts_synthesis_cmpp(statistic):
           data_tables_set = []
           if not statistic.in_service:
               return None
           if not statistic.in_end_date:
               statistic.in_end_date = datetime.today()
           if not statistic.in_start_date:
               statistic.in_start_date = datetime(statistic.in_end_date.year, 1, 1)
           acts = Act.objects.filter(date__gte=statistic.in_start_date,
               date__lte=statistic.in_end_date,
               patient__service=statistic.in_service)
           acts_billed = acts.filter(is_billed=True)
           patients_billed = dict()
           for act in acts_billed:
               patients_billed.setdefault(act.patient, [False, False])
               if act.get_hc_tag() and act.get_hc_tag()[0] == 'D':
                   patients_billed[act.patient][0] = True
               elif act.get_hc_tag() and act.get_hc_tag()[0] == 'T':
                   patients_billed[act.patient][1] = True
           values1, values2, values3 = [], [], []
           for patient, vals in patients_billed.iteritems():
               pfields = [patient.last_name, patient.first_name, patient.paper_id]
               if vals == [True, False]:
                   values1.append(pfields)
               elif vals == [False, True]:
                   values2.append(pfields)
               elif vals == [True, True]:
                   values3.append(pfields)
           cols = ['Nom', 'Prénom', 'Numéro de dossier']
           data_tables_set.append([[['Seulement facturé en diagnostic'], [[len(values1)]]], [cols, sorted(values1, key=lambda t: t[0])]])
           data_tables_set.append([[['Seulement facturé en traitement'], [[len(values2)]]], [cols, sorted(values2, key=lambda t: t[0])]])
           data_tables_set.append([[['Facturé en diagnostic et en traitement'], [[len(values3)]]], [cols, sorted(values3, key=lambda t: t[0])]])
           return data_tables_set
       def mises(statistic):
           if not statistic.in_service:
               return None
           if not statistic.in_end_date:
               statistic.in_end_date = datetime.today()
           if not statistic.in_start_date:
               statistic.in_start_date = datetime(statistic.in_end_date.year, 1, 1)
           acts = Act.objects.filter(valide='True',
               date__gte=statistic.in_start_date,
               date__lte=statistic.in_end_date,
               patient__service=statistic.in_service)
           patients = acts.order_by('patient').distinct('patient').\
               values_list('patient')
           patients = PatientRecord.objects.filter(id__in=[patient[0]
               for patient in patients])
           pathologies = dict()
           for patient in patients:
               for pathology in patient.mises_1.all():
                   pathologies.setdefault(pathology, 0)
                   pathologies[pathology] += 1
               for pathology in patient.mises_2.all():
                   pathologies.setdefault(pathology, 0)
                   pathologies[pathology] += 1
               for pathology in patient.mises_3.all():
                   pathologies.setdefault(pathology, 0)
                   pathologies[pathology] += 1
           data = [['Pathologies MISES', 'Nombre de patients concernés']]
           data.append(OrderedDict(sorted(pathologies.items(), key=lambda t: t[0].ordering_code)).items())
           return [[data]]
       def deficiencies(statistic):
           if not statistic.in_service:
               return None
           if not statistic.in_end_date:
               statistic.in_end_date = datetime.today()
           if not statistic.in_start_date:
               statistic.in_start_date = datetime(statistic.in_end_date.year, 1, 1)
           acts = Act.objects.filter(valide='True',
               date__gte=statistic.in_start_date,
               date__lte=statistic.in_end_date,
               patient__service=statistic.in_service)
           patients = acts.order_by('patient').distinct('patient').\
               values_list('patient')
           patients = PatientRecord.objects.filter(id__in=[patient[0]
               for patient in patients])
           deficiencies_three = ('deficiency_intellectual',
                   'deficiency_autism_and_other_ted',
                   'deficiency_mental_disorder', 'deficiency_learning_disorder',
                   'deficiency_auditory', 'deficiency_visual', 'deficiency_motor',
                   'deficiency_metabolic_disorder', 'deficiency_brain_damage',
                   'deficiency_behavioral_disorder', 'deficiency_other_disorder')
           data = [['Déficiences', 'Nombre de patients concernés'], []]
           for deficiency in deficiencies_three:
               name = PatientRecord._meta.get_field_by_name(deficiency)[0].verbose_name
               filter_dict = {deficiency: 1}
               data[1].append((name + ' à titre principal', patients.filter(**filter_dict).count()))
               filter_dict = {deficiency: 2}
               data[1].append((name + ' à titre associé', patients.filter(**filter_dict).count()))
           name = PatientRecord._meta.get_field_by_name('deficiency_polyhandicap')[0].verbose_name
           data[1].append((name, patients.filter(deficiency_polyhandicap=True).count()))
           name = PatientRecord._meta.get_field_by_name('deficiency_in_diagnostic')[0].verbose_name
           data[1].append((name, patients.filter(deficiency_in_diagnostic=True).count()))
           return [[data]]
       class Statistic(object):
           in_start_date = None
           in_end_date = None
           in_service = None
           in_participants = None
           in_patients = None
           in_year = None
           inscriptions = False
           def __init__(self, name=None, inputs=dict()):
               self.name = name
               params = STATISTICS.get(name, {})
               self.display_name = params['display_name']
               self.category = params['category']
               self.inputs = inputs
               self.in_participants = list()
               participants = inputs.get('participants')
               if participants:
                   p_str_ids = [p for p in participants.split('|') if p]
                   for str_id in p_str_ids:
                       try:
                           self.in_participants.append(Worker.objects.get(pk=int(str_id)))
                       except:
                           pass
               self.in_patients = list()
               patients = inputs.get('patients')
               if patients:
                   p_str_ids = [p for p in patients.split('|') if p]
                   for str_id in p_str_ids:
                       try:
                           self.in_patients.append(PatientRecord.objects.get(pk=int(str_id)))
                       except:
                           pass
               self.in_service = inputs.get('service')
               self.in_start_date = None
               try:
                   self.in_start_date = datetime.strptime(inputs.get('start_date'),
                       "%d/%m/%Y")
                   self.in_year = self.in_start_date.year
               except:
                   pass
               self.in_end_date = None
               try:
                   self.in_end_date = datetime.strptime(inputs.get('end_date'),
                       "%d/%m/%Y")
               except:
                   pass
               self.inscriptions = inputs.get('inscriptions')
           def get_data(self):
               func = globals()[self.name]
               data = func(self)
               self.data = [[[["Date du jour", "Service", "Nom statistique"],
                   [[formats.date_format(datetime.today(), "SHORT_DATE_FORMAT"),
                   self.in_service, STATISTICS[self.name]['display_name']]]]]] + data
               return self.data
           def render_to_csv(self):
               _delimiter = ';'
               _quotechar = '|'
               _doublequote = True
               _skipinitialspace = False
               _lineterminator = '\r\n'
               _quoting = csv.QUOTE_MINIMAL
               if getattr(settings, 'CSVPROFILE', None):
                   csv_profile = settings.CSVPROFILE
                   _delimiter = csv_profile.get('delimiter', ';')
                   _quotechar = csv_profile.get('quotechar', '|')
                   _doublequote = csv_profile.get('doublequote', True)
                   _skipinitialspace = csv_profile.get('skipinitialspace', False)
                   _lineterminator = csv_profile.get('lineterminator', '\r\n')
                   _quoting = csv_profile.get('quoting', csv.QUOTE_MINIMAL)
               class CSVProfile(csv.Dialect):
                   delimiter = _delimiter
                   quotechar = _quotechar
                   doublequote = _doublequote
                   skipinitialspace = _skipinitialspace
                   lineterminator = _lineterminator
                   quoting = _quoting
               csv.register_dialect('csv_profile', CSVProfile())
               encoding = getattr(settings, 'CSV_ENCODING', 'utf-8')
               import codecs
               filename = None
               with tempfile.NamedTemporaryFile(delete=False) as temp_out_csv:
                   filename = temp_out_csv.name
                   temp_out_csv.close()
               with codecs.open(filename, 'w+b', encoding=encoding) as encoded_f:
                   try:
                       writer = csv.writer(encoded_f, dialect='csv_profile')
                       for data_set in self.data:
                           for data in data_set:
                               writer.writerow(data[0])
                               if len(data) > 1:
                                   for d in data[1]:
                                       writer.writerow(d)
                               writer.writerow([])
                           writer.writerow([])
                       return filename
                   except:
                       try:
                           os.unlink(temp_out_pdf.name)
                       except:
                           pass
           def get_file(self):
               self.get_data()
               return self.render_to_csv()

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calebasse / calebasse / statistics / statistics.py @ fdb98b89