def insights_forecast():
from airqo_etl_utils.app_insights_utils import (
create_insights_data,
get_forecast_data,
transform_old_forecast,
)
from airqo_etl_utils.date import date_to_str, first_day_of_week, first_day_of_month
now = datetime.now()
start_date_time = date_to_str(
first_day_of_week(first_day_of_month(date_time=now)))
end_date_time = date_to_str(now)
old_forecast = transform_old_forecast(start_date_time=start_date_time,
end_date_time=end_date_time)
pd.DataFrame(old_forecast).to_csv(path_or_buf="old_forecast_data.csv",
index=False)
forecast_data = get_forecast_data("airqo")
pd.DataFrame(forecast_data).to_csv(path_or_buf="forecast_data.csv",
index=False)
insights_data = create_insights_data(data=forecast_data)
pd.DataFrame(insights_data).to_csv(
path_or_buf="insights_forecast_data.csv", index=False)
def load(data: dict):
from airqo_etl_utils.app_insights_utils import (
save_insights_data,
create_insights_data,
)
from airqo_etl_utils.commons import un_fill_nan
insights_list = un_fill_nan(data.get("data"))
insights_data = create_insights_data(data=insights_list)
save_insights_data(insights_data=insights_data, action="save")
def extract_api_forecast_data():
from airqo_etl_utils.app_insights_utils import (
create_insights_data,
get_forecast_data,
)
from airqo_etl_utils.commons import fill_nan
forecast_data = get_forecast_data("airqo")
insights_data = create_insights_data(data=forecast_data)
return dict({"data": fill_nan(data=insights_data)})
def extract_airqo_data(**kwargs):
from airqo_etl_utils.app_insights_utils import (
create_insights_data,
get_airqo_data,
)
from airqo_etl_utils.commons import get_date_time_values
start_time, end_time = get_date_time_values(**kwargs)
measurements_data = get_airqo_data(freq="hourly",
start_time=start_time,
end_time=end_time)
insights_data = create_insights_data(data=measurements_data)
return {"data": insights_data}
def extract_insights_forecast_data():
from airqo_etl_utils.app_insights_utils import (
create_insights_data,
transform_old_forecast,
)
from airqo_etl_utils.date import (
date_to_str,
first_day_of_week,
first_day_of_month,
)
from airqo_etl_utils.commons import fill_nan
now = datetime.now()
start_date_time = date_to_str(
first_day_of_week(first_day_of_month(date_time=now)))
end_date_time = date_to_str(now)
forecast_data = transform_old_forecast(start_date_time=start_date_time,
end_date_time=end_date_time)
insights_data = create_insights_data(data=forecast_data)
return dict({"data": fill_nan(data=insights_data)})