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