def dashboard(request):
context = {"category": 'dashboard', 'user': request.user}
# le nombre total d'enfant
patients = Patient.by_uren.all().all_uren()
nbr_total_patient = len(patients)
# Taux guerison
nbr_healing = len(ProgramIO.healing.all().all_uren())
healing_rates = percentage_calculation(nbr_healing,
nbr_total_patient)
# Taux abandon
nbr_abandonment = len(ProgramIO.abandon.all().all_uren())
abandonment_rates = percentage_calculation(nbr_abandonment,
nbr_total_patient)
# Taux déces
nbr_deaths = len(ProgramIO.death.all().all_uren())
deaths_rates = percentage_calculation(nbr_deaths, nbr_total_patient)
# Taux non repondant
nbr_non_response = len(ProgramIO.nonresp.all().all_uren())
non_response_rates = percentage_calculation(nbr_non_response,
nbr_total_patient)
context.update({"nbr_total_patient": nbr_total_patient,
"nbr_healing": nbr_healing,
"healing_rates": healing_rates,
"nbr_abandonment": nbr_abandonment,
"abandonment_rates": abandonment_rates,
"nbr_deaths": nbr_deaths,
"deaths_rates": deaths_rates,
"nbr_non_response": nbr_non_response,
"non_response_rates": non_response_rates})
# Durées de tous les programmes puis moyenne
context.update({"avg_days": ProgramIO.out.avg_days()})
# Gain de poids moyen
context.update({"avg_weight": "%.2f" % Patient.avg_weight_gain()})
# graphic
total_patient = []
graph_date = []
diagnose_samp = []
diagnose_sam = []
# Ordonne les programio par order décroissante
programio = ProgramIO.by_uren.order_by('-date').all_uren()
nbr_date_graph = len(programio)
if len(programio) > 100:
nbr_date_graph = 100
try:
week_dates = week_range(programio[nbr_date_graph].date)
except IndexError:
week_dates = []
for dat in week_dates:
active_patients = []
for p in patients:
try:
if not p.programios.filter(date__lte=dat).latest().is_output:
active_patients.append(p)
except ProgramIO.DoesNotExist:
pass
total_patient.append(len(active_patients))
graph_date.append(dat.strftime('%d/%m'))
l_diagnose = []
for patient in active_patients:
try:
l_diagnose.append(patient.uren)
except NutritionalData.DoesNotExist:
pass
diagnose_samp.append(l_diagnose.count(NutritionalData.SAMP))
diagnose_sam.append(l_diagnose.count(NutritionalData.SAM))
graph_data = [{'name': "Total", 'data': total_patient},
{'name': "MAS+", 'data': diagnose_samp},
{'name': "MAS", 'data': diagnose_sam}]
context.update({"graph_date": graph_date, "graph_data": graph_data})
# Diagnose
SAMP_count = extract(diagnose_samp, -1, default=0)
SAM_count = extract(diagnose_sam, -1, default=0)
# Nbre d'enfant dans le programme
children_in_program = extract(total_patient, -1, default=0)
# Nbre d'enfant en retard de consultation
patients_late = []
for patient in patients:
if not patient.last_data_event().is_output and patient.is_late:
patients_late.append(patient)
patients_late = len(patients_late)
# message
received = Inbox.objects.count()
sent = SentItems.objects.count()
context.update({"children_in_program": children_in_program, \
"sent": sent, "received": received, \
"SAMP_count": SAMP_count, "SAM_count": SAM_count, \
"patients_late": patients_late})
return render(request, 'dashboard.html', context)
def details_health_center(request, *args, **kwargs):
""" Details of a health center """
context = {"category": "health_center", "user": request.user}
num = kwargs["id"]
health_center = HealthCenter.objects.get(id=num)
def avg_(list_):
''' calcule la moyenne de jours'''
if list_ != []:
return sum(list_) / len(list_)
else:
return None
# Les patients de ce centre
patients = Patient.by_uren.all().filter(health_center=health_center) \
.all_uren()
consumption_reports = ConsumptionReport.objects \
.filter(health_center=health_center)
nbr_total_patient = len(patients)
# Taux guerison
nbr_healing = len(
ProgramIO.healing.filter(
patient__health_center=health_center).all_uren())
healing_rates = percentage_calculation(nbr_healing, nbr_total_patient)
# Taux abandon
nbr_abandonment = len(
ProgramIO.abandon.filter(
patient__health_center=health_center).all_uren())
abandonment_rates = percentage_calculation(nbr_abandonment, \
nbr_total_patient)
# Taux déces
nbr_deaths = len(
ProgramIO.death.filter(
patient__health_center=health_center).all_uren())
deaths_rates = percentage_calculation(nbr_deaths, nbr_total_patient)
# Taux non repondant
nbr_non_response = len(
ProgramIO.nonresp.filter(
patient__health_center=health_center).all_uren())
non_response_rates = percentage_calculation(nbr_non_response, \
nbr_total_patient)
dict_ = {}
dict_["health_center"] = health_center.name
dict_["code"] = health_center.code
dict_["abandon"] = nbr_abandonment
dict_["taux_abandon"] = abandonment_rates
dict_["guerison"] = nbr_healing
dict_["taux_guerison"] = healing_rates
dict_["deces"] = nbr_deaths
dict_["taux_deces"] = deaths_rates
dict_["non_repondant"] = nbr_non_response
dict_["taux_non_repondant"] = non_response_rates
# graphic
total_patient = []
graph_date = []
diagnose_samp = []
diagnose_sam = []
programio = ProgramIO.by_uren.order_by('-date').all_uren()
nbr_date_graph = len(programio)
if len(programio) > 100:
nbr_date_graph = 100
try:
week_dates = week_range(programio[nbr_date_graph].date)
except IndexError:
week_dates = []
for dat in week_dates:
active_patients = []
for p in patients:
try:
if not p.programios.filter(date__lte=dat).latest().is_output:
active_patients.append(p)
except ProgramIO.DoesNotExist:
pass
total_patient.append(len(active_patients))
graph_date.append(dat.strftime('%d/%m'))
l_diagnose = []
for patient in active_patients:
try:
l_diagnose.append(patient.uren)
except NutritionalData.DoesNotExist:
pass
diagnose_samp.append(l_diagnose.count(NutritionalData.SAMP))
diagnose_sam.append(l_diagnose.count(NutritionalData.SAM))
graph_data = [{
'name': "Total",
'data': total_patient
}, {
'name': "MAS+",
'data': diagnose_samp
}, {
'name': "MAS",
'data': diagnose_sam
}]
context.update({"graph_date": graph_date, "graph_data": graph_data})
# Diagnose
dict_["SAMP_count"] = extract(diagnose_samp, -1, default=0)
dict_["SAM_count"] = extract(diagnose_sam, -1, default=0)
# Nbre d'enfants dans le programme
dict_["actif"] = extract(total_patient, -1, default=0)
# Nbre d'enfants enregistres
dict_["total"] = nbr_total_patient or 0
try:
dict_["avg_days"] = "%.0f" % ProgramIO.out\
.filter(patient__health_center=health_center)\
.avg_days()
except:
dict_["avg_days"] = 0
dict_["avg_diff_weight"] = "%.2f" % Patient.avg_weight_gain(patients)
context.update({'dict_': dict_, \
'consumption_reports': consumption_reports})
return render(request, 'details_health_center.html', context)
def dashboard(request):
context = {"category": 'dashboard', 'user': request.user}
# le nombre total d'enfant
patients = Patient.by_uren.all().all_uren()
nbr_total_patient = len(patients)
# Taux guerison
nbr_healing = len(ProgramIO.healing.all().all_uren())
healing_rates = percentage_calculation(nbr_healing, nbr_total_patient)
# Taux abandon
nbr_abandonment = len(ProgramIO.abandon.all().all_uren())
abandonment_rates = percentage_calculation(nbr_abandonment,
nbr_total_patient)
# Taux déces
nbr_deaths = len(ProgramIO.death.all().all_uren())
deaths_rates = percentage_calculation(nbr_deaths, nbr_total_patient)
# Taux non repondant
nbr_non_response = len(ProgramIO.nonresp.all().all_uren())
non_response_rates = percentage_calculation(nbr_non_response,
nbr_total_patient)
context.update({
"nbr_total_patient": nbr_total_patient,
"nbr_healing": nbr_healing,
"healing_rates": healing_rates,
"nbr_abandonment": nbr_abandonment,
"abandonment_rates": abandonment_rates,
"nbr_deaths": nbr_deaths,
"deaths_rates": deaths_rates,
"nbr_non_response": nbr_non_response,
"non_response_rates": non_response_rates
})
# Durées de tous les programmes puis moyenne
context.update({"avg_days": ProgramIO.out.avg_days()})
# Gain de poids moyen
context.update({"avg_weight": "%.2f" % Patient.avg_weight_gain()})
# graphic
total_patient = []
graph_date = []
diagnose_samp = []
diagnose_sam = []
# Ordonne les programio par order décroissante
programio = ProgramIO.by_uren.order_by('-date').all_uren()
nbr_date_graph = len(programio)
if len(programio) > 100:
nbr_date_graph = 100
try:
week_dates = week_range(programio[nbr_date_graph].date)
except IndexError:
week_dates = []
for dat in week_dates:
active_patients = []
for p in patients:
try:
if not p.programios.filter(date__lte=dat).latest().is_output:
active_patients.append(p)
except ProgramIO.DoesNotExist:
pass
total_patient.append(len(active_patients))
graph_date.append(dat.strftime('%d/%m'))
l_diagnose = []
for patient in active_patients:
try:
l_diagnose.append(patient.uren)
except NutritionalData.DoesNotExist:
pass
diagnose_samp.append(l_diagnose.count(NutritionalData.SAMP))
diagnose_sam.append(l_diagnose.count(NutritionalData.SAM))
graph_data = [{
'name': "Total",
'data': total_patient
}, {
'name': "MAS+",
'data': diagnose_samp
}, {
'name': "MAS",
'data': diagnose_sam
}]
context.update({"graph_date": graph_date, "graph_data": graph_data})
# Diagnose
SAMP_count = extract(diagnose_samp, -1, default=0)
SAM_count = extract(diagnose_sam, -1, default=0)
# Nbre d'enfant dans le programme
children_in_program = extract(total_patient, -1, default=0)
# Nbre d'enfant en retard de consultation
patients_late = []
for patient in patients:
if not patient.last_data_event().is_output and patient.is_late:
patients_late.append(patient)
patients_late = len(patients_late)
# message
received = Inbox.objects.count()
sent = SentItems.objects.count()
context.update({"children_in_program": children_in_program, \
"sent": sent, "received": received, \
"SAMP_count": SAMP_count, "SAM_count": SAM_count, \
"patients_late": patients_late})
return render(request, 'dashboard.html', context)
def details_health_center(request, *args, **kwargs):
""" Details of a health center """
context = {"category": "health_center", "user": request.user}
num = kwargs["id"]
health_center = HealthCenter.objects.get(id=num)
def avg_(list_):
""" calcule la moyenne de jours"""
if list_ != []:
return sum(list_) / len(list_)
else:
return None
# Les patients de ce centre
patients = Patient.by_uren.all().filter(health_center=health_center).all_uren()
consumption_reports = ConsumptionReport.objects.filter(health_center=health_center)
nbr_total_patient = len(patients)
# Taux guerison
nbr_healing = len(ProgramIO.healing.filter(patient__health_center=health_center).all_uren())
healing_rates = percentage_calculation(nbr_healing, nbr_total_patient)
# Taux abandon
nbr_abandonment = len(ProgramIO.abandon.filter(patient__health_center=health_center).all_uren())
abandonment_rates = percentage_calculation(nbr_abandonment, nbr_total_patient)
# Taux déces
nbr_deaths = len(ProgramIO.death.filter(patient__health_center=health_center).all_uren())
deaths_rates = percentage_calculation(nbr_deaths, nbr_total_patient)
# Taux non repondant
nbr_non_response = len(ProgramIO.nonresp.filter(patient__health_center=health_center).all_uren())
non_response_rates = percentage_calculation(nbr_non_response, nbr_total_patient)
dict_ = {}
dict_["health_center"] = health_center.name
dict_["code"] = health_center.code
dict_["abandon"] = nbr_abandonment
dict_["taux_abandon"] = abandonment_rates
dict_["guerison"] = nbr_healing
dict_["taux_guerison"] = healing_rates
dict_["deces"] = nbr_deaths
dict_["taux_deces"] = deaths_rates
dict_["non_repondant"] = nbr_non_response
dict_["taux_non_repondant"] = non_response_rates
# graphic
total_patient = []
graph_date = []
diagnose_samp = []
diagnose_sam = []
programio = ProgramIO.by_uren.order_by("-date").all_uren()
nbr_date_graph = len(programio)
if len(programio) > 100:
nbr_date_graph = 100
try:
week_dates = week_range(programio[nbr_date_graph].date)
except IndexError:
week_dates = []
for dat in week_dates:
active_patients = []
for p in patients:
try:
if not p.programios.filter(date__lte=dat).latest().is_output:
active_patients.append(p)
except ProgramIO.DoesNotExist:
pass
total_patient.append(len(active_patients))
graph_date.append(dat.strftime("%d/%m"))
l_diagnose = []
for patient in active_patients:
try:
l_diagnose.append(patient.uren)
except NutritionalData.DoesNotExist:
pass
diagnose_samp.append(l_diagnose.count(NutritionalData.SAMP))
diagnose_sam.append(l_diagnose.count(NutritionalData.SAM))
graph_data = [
{"name": "Total", "data": total_patient},
{"name": "MAS+", "data": diagnose_samp},
{"name": "MAS", "data": diagnose_sam},
]
context.update({"graph_date": graph_date, "graph_data": graph_data})
# Diagnose
dict_["SAMP_count"] = extract(diagnose_samp, -1, default=0)
dict_["SAM_count"] = extract(diagnose_sam, -1, default=0)
# Nbre d'enfants dans le programme
dict_["actif"] = extract(total_patient, -1, default=0)
# Nbre d'enfants enregistres
dict_["total"] = nbr_total_patient or 0
try:
dict_["avg_days"] = "%.0f" % ProgramIO.out.filter(patient__health_center=health_center).avg_days()
except:
dict_["avg_days"] = 0
dict_["avg_diff_weight"] = "%.2f" % Patient.avg_weight_gain(patients)
context.update({"dict_": dict_, "consumption_reports": consumption_reports})
return render(request, "details_health_center.html", context)
