def now(config):
df = get_health.now(config,
"br")[config["br"]["simulacovid"]["columns"]["cnes"]]
# merge cases
cases = get_cities_cases.now(config, "br")
cases = cases[cases["is_last"] == True].drop(config["br"]["cases"]["drop"],
1)
df = df.merge(cases, on="city_id", how="left", suffixes=("", "_y"))
df = df[[c for c in df.columns if not c.endswith("_y")]]
health_region_rate = (df[[
"health_region_id", "health_region_notification_rate"
]].dropna().groupby("health_region_id").mean())
# get notification for cities without cases
df["health_region_notification_rate"] = df.apply(
lambda row: _recover_notification_rate(row, health_region_rate),
axis=1)
df["notification_rate"] = np.where(
df["notification_rate"].isnull(),
df["health_region_notification_rate"],
df["notification_rate"],
)
df["last_updated"] = pd.to_datetime(df["last_updated"])
return df
def now(config=None):
# Import cases
df = get_cities_cases.now(config, "br")
df["last_updated"] = pd.to_datetime(df["last_updated"])
return get_rt(df, "city_id", config)
def now(config):
cols = [
"health_region_id",
"health_region_name",
"state_num_id",
"state_id",
"state_name",
"last_updated",
]
# Pega casos já tratados para cidades (+ taxa de notificação da regional)
df = get_cities_cases.now(config)
logger.info("FINISH LOAD DATA")
# Agrega colunas calculadas em cidades para regionais de saúde
grouped = df.groupby(cols, sort=False)
df = grouped.agg({
"confirmed_cases": "sum",
"daily_cases": "sum",
"deaths": "sum",
"new_deaths": "sum",
"is_last":
"max", # todas as cidades são atualizadas numa mesma tabela diária no Brasil.io
"estimated_cases": "mean", # mesmo valor para todas cidades
"expected_mortality": "mean",
"notification_rate": "mean",
"total_estimated_cases": "mean",
}).reset_index()
# Converte data e ordena tabela
df["last_updated"] = pd.to_datetime(df["last_updated"])
df = df.sort_values(by=["health_region_id", "last_updated"])
# Padroniza ids e nomes + populacao
df = get_default_ids(df, config, place_type="health_region")
logger.info("FINISH DATA TREATMENT")
# Gera métricas de média móvel e tendência
groups = df.groupby("health_region_id", as_index=False)
df = groups.apply(lambda x: get_rolling_indicators(
x, config, cols=["daily_cases", "new_deaths"]))
df = df.reset_index(drop=True)
logger.info("FINISH DATA GROW CALCULATION")
# Calcula casos ativos
df["active_cases"] = np.nan
df.loc[~df["notification_rate"].isnull(), "active_cases"] = round(
df["infectious_period_cases"] / df["notification_rate"], 0)
return df
def now(config):
# Get resource data
data_cases = get_cities_cases.now(config)
data_rt = get_cities_rt.now(config)
data_rt_region = get_health_region_rt.now(config)
return get_place_indicators(
place_id="city_id",
data_cases=data_cases,
data_rt=data_rt,
data_rt_region=data_rt_region,
config=config,
)
def now(config):
# Get resource data
df = (get_health.now(config, "br")[[
"country_iso",
"country_name",
"state_num_id",
"state_id",
"state_name",
"health_region_id",
"health_region_name",
"city_name",
"city_id",
"last_updated_number_beds",
"author_number_beds",
"last_updated_number_icu_beds",
"author_number_icu_beds",
"population",
]].sort_values("city_id").set_index("city_id"))
df = get_situation_indicators(
df,
data=get_cities_cases.now(config),
place_id="city_id",
rules=config["br"]["farolcovid"]["rules"],
classify="situation_classification",
)
df = get_control_indicators(
df,
data=get_cities_rt.now(config),
place_id="city_id",
rules=config["br"]["farolcovid"]["rules"],
classify="control_classification",
config=config,
region_data=get_health_region_farolcovid_main.now(config),
)
df = get_trust_indicators(
df,
data=get_cities_cases.now(config),
place_id="city_id",
rules=config["br"]["farolcovid"]["rules"],
classify="trust_classification",
)
df = get_capacity_indicators(
df,
place_id="city_id",
config=config,
rules=config["br"]["farolcovid"]["rules"],
classify="capacity_classification",
data=get_health_region_farolcovid_main.now(config),
)
cols = [col for col in df.columns if "classification" in col]
df["overall_alert"] = df.apply(
lambda row: get_overall_alert(row[cols]),
axis=1) # .replace(config["br"]["farolcovid"]["categories"])
return df.reset_index()
def now(config=None):
return get_rt(get_cities_cases.now(), place_id="city_id")