def get_context_data(self, **kwargs):
context = super(AlertaPageViewMunicipio, self).get_context_data(**kwargs)
municipio_gc = context['geocodigo']
city_info = get_city_info(municipio_gc)
alert, SE, case_series, last_year, observed_cases, min_max_est, dia = dbdata.get_city_alert(municipio_gc)
casos_ap = {municipio_gc: int(case_series[-1])}
bairros = {municipio_gc: city_info['nome']}
total_series = case_series[-12:]
total_observed_series = observed_cases[-12:]
context.update({
'nome': city_info['nome'],
'casos_por_ap': json.dumps(casos_ap),
'alerta': {municipio_gc: alert},
'novos_casos': case_series[-1],
'bairros': bairros,
'min_est': min_max_est[0],
'max_est': min_max_est[1],
'series_casos': {municipio_gc: case_series[-12:]},
'SE': SE,
'data1': (dia - datetime.timedelta(2)).strftime("%d de %B de %Y"),
'data2': (dia + datetime.timedelta(4)).strftime("%d de %B de %Y"),
'last_year': last_year,
'look_back': len(total_series),
'total_series': ', '.join(map(str, total_series)),
'total_observed': total_observed_series[-1],
'total_observed_series': ', '.join(map(str, total_observed_series)),
'geocodigo': municipio_gc,
})
return context
def test_get_city_alert(self):
alert, SE, case_series, last_year, obs_case_series, min_max_est, dia = dbdata.get_city_alert(330330)
self.assertIsInstance(alert, pd.np.int64)
self.assertIsInstance(SE, pd.np.int64, SE)
self.assertIsInstance(case_series, list)
self.assertIsInstance(last_year, pd.np.int64)
self.assertIsInstance(obs_case_series, list)
self.assertIsInstance(min_max_est, tuple)
self.assertIsInstance(dia, datetime.date)
def test_get_city_alert(self):
alert, SE, case_series, last_year, obs_case_series, min_max_est, dia = dbdata.get_city_alert(
330330)
self.assertIsInstance(alert, pd.np.int64)
self.assertIsInstance(SE, pd.np.int64, SE)
self.assertIsInstance(case_series, list)
self.assertIsInstance(last_year, pd.np.int64)
self.assertIsInstance(obs_case_series, list)
self.assertIsInstance(min_max_est, tuple)
self.assertIsInstance(dia, datetime.date)
def test_get_city_alert(self):
(alert, SE, case_series, last_year, obs_case_series, min_max_est, dia,
ptr1) = dbdata.get_city_alert(3303302)
self.assertIsInstance(alert, int)
self.assertIsInstance(SE, int, SE)
self.assertIsInstance(case_series, list)
self.assertIsInstance(last_year, int)
self.assertIsInstance(obs_case_series, list)
self.assertIsInstance(min_max_est, tuple)
self.assertIsInstance(dia, datetime.date)
def get_context_data(self, **kwargs):
context = super(AlertaPageViewMunicipio,
self).get_context_data(**kwargs)
municipio_gc = context['geocodigo']
city_info = get_city_info(municipio_gc)
alert, SE, case_series, last_year, observed_cases, min_max_est, dia, prt1 = dbdata.get_city_alert(
municipio_gc)
casos_ap = {municipio_gc: int(case_series[-1])}
bairros = {municipio_gc: city_info['nome']}
total_series = case_series[-12:]
total_observed_series = observed_cases[-12:]
context.update({
'nome':
city_info['nome'],
'populacao':
city_info['populacao'],
'incidencia':
(case_series[-1] / city_info['populacao']) * 100000, #casos/100000
'casos_por_ap':
json.dumps(casos_ap),
'alerta': {
municipio_gc: alert
},
'prt1':
prt1 * 100,
'novos_casos':
case_series[-1],
'bairros':
bairros,
'min_est':
min_max_est[0],
'max_est':
min_max_est[1],
'series_casos': {
municipio_gc: case_series[-12:]
},
'SE':
SE,
'data1':
dia.strftime("%d de %B de %Y"),
'data2': (dia + datetime.timedelta(6)).strftime("%d de %B de %Y"),
'last_year':
last_year,
'look_back':
len(total_series),
'total_series':
', '.join(map(str, total_series)),
'total_observed':
total_observed_series[-1],
'total_observed_series':
', '.join(map(str, total_observed_series)),
'geocodigo':
municipio_gc,
})
return context
def get_context_data(self, **kwargs):
context = super(AlertaMainView, self).get_context_data(**kwargs)
mundict = dict(dbdata.get_all_active_cities())
municipios, geocodigos = list(mundict.values()), list(mundict.keys())
alerta = {}
case_series = {}
total = np.zeros(52, dtype=int)
for gc in geocodigos:
dados = dbdata.get_city_alert(gc, 'dengue')
alerta[gc] = int(dados[0])
case_series[str(gc)] = list(map(int, dados[2][-12:]))
total += dados[2][-52:]
context.update({
'mundict': json.dumps(mundict),
'municipios': municipios,
'geocodigos': geocodigos,
'alerta': json.dumps(alerta),
'case_series': json.dumps(case_series),
'total': json.dumps(total.tolist()),
})
return context
def get_context_data(self, **kwargs):
context = super(AlertaMainView, self).get_context_data(**kwargs)
mundict = dict(dbdata.get_all_active_cities())
municipios, geocodigos = list(mundict.values()), list(mundict.keys())
alerta = {}
case_series = {}
total = np.zeros(52, dtype=int)
for gc in geocodigos:
dados = dbdata.get_city_alert(gc, 'dengue')
alerta[gc] = int(dados[0])
case_series[str(gc)] = list(map(int, dados[2][-12:]))
total += dados[2][-52:]
context.update({
'mundict': json.dumps(mundict),
'num_mun': len(mundict),
'municipios': municipios,
'geocodigos': geocodigos,
'alerta': json.dumps(alerta),
'case_series': json.dumps(case_series),
'total': json.dumps(total.tolist()),
})
return context
def get_context_data(self, **kwargs):
context = super(AlertaPageViewMunicipio, self) \
.get_context_data(**kwargs)
disease_code = context['disease']
disease_label = (
'Dengue' if disease_code == 'dengue' else
'Chikungunya' if disease_code == 'chikungunya' else None)
municipio_gc = context['geocodigo']
city_info = get_city_info(municipio_gc)
(alert, SE, case_series, last_year, observed_cases, min_max_est, dia,
prt1) = dbdata.get_city_alert(municipio_gc, disease_code)
if alert is not None:
casos_ap = {municipio_gc: int(case_series[-1])}
bairros = {municipio_gc: city_info['nome']}
total_series = case_series[-12:]
total_observed_series = observed_cases[-12:]
else:
casos_ap = {}
bairros = {}
total_series = [0]
total_observed_series = [0]
context.update({
'nome':
city_info['nome'],
'populacao':
city_info['populacao'],
'incidencia': (case_series[-1] / city_info['populacao']) *
100000, # casos/100000
'casos_por_ap':
json.dumps(casos_ap),
'alerta': {
municipio_gc: alert
},
'prt1':
prt1 * 100,
'novos_casos':
case_series[-1],
'bairros':
bairros,
'min_est':
min_max_est[0],
'max_est':
min_max_est[1],
'series_casos': {
municipio_gc: case_series[-12:]
},
'SE':
SE,
'data1':
dia.strftime("%d de %B de %Y"),
# .strftime("%d de %B de %Y")
'data2': (dia + datetime.timedelta(6)),
'last_year':
last_year,
'look_back':
len(total_series),
'total_series':
', '.join(map(str, total_series)),
'total_observed':
total_observed_series[-1],
'total_observed_series':
', '.join(map(str, total_observed_series)),
'geocodigo':
municipio_gc,
'disease_label':
disease_label,
'disease_code':
disease_code
})
return context