## === retrieve EFAS Medium-Range Climatology === 
import cdsapi


if __name__ == '__main__':

	c = cdsapi.Client()


	VARIABLES = [
			'river_discharge_in_the_last_6_hours', 'snow_depth_water_equivalent',
	]


	YEARS = ['%02d'%(mn) for mn in range(1991,2022)]

	MONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']
	DAYS = ['%02d'%(mn) for mn in range(1,32)]


	for variable in VARIABLES:
		for year in YEARS:
			c.retrieve(
				'efas-historical',
				{
					'system_version': 'version_4_0',
					'variable': variable,
					'model_levels': 'surface_level',
					'hyear': '1991',
					'hmonth': MONTHS,
					'hday': DAYS,
					'time': '00:00',
					'format': 'grib',
				},
					f'efas_historical_{variable}_{year}.grib')

## === retrieve EFAS Medium-Range Forecast === 

import cdsapi
import datetime



def compute_dates_range(start_date,end_date,loop_days=True):


    start_date = datetime.date(*[int(x) for x in start_date.split('-')])
    
    end_date = datetime.date(*[int(x) for x in end_date.split('-')])
    
    ndays =  (end_date - start_date).days + 1
    
    dates = []
    for d in range(ndays):
        dates.append(start_date + datetime.timedelta(d))
    
    if not loop_days:
        dates = [i for i in dates if i.day == 1]
    else:
        pass
    return dates



if __name__ == '__main__':


    # start the client
    c = cdsapi.Client()


    # user inputs
    START_DATE = '2020-10-14' # first date with available data

    END_DATE = '2021-02-28' 

    LEADTIMES =  [str(lt) for lt in range(0,372,6)]


    # loop over dates and save to disk

    dates = compute_dates_range(START_DATE,END_DATE)

    for date in dates:

        year  = date.strftime('%Y')
        month = date.strftime('%m')
        day   = date.strftime('%d')

        print(f"RETRIEVING: {year}-{month}-{day}")

        c.retrieve('efas-forecast',
            {
                'format': 'grib',
                'originating_centre':'ecmwf',
                'product_type':'ensemble_perturbed_forecasts',
                'variable': 'river_discharge_in_the_last_6_hours',
                'model_levels': 'surface_level',
                'year': year,
                'month': month,
                'day': day,
                'leadtime_hour':LEADTIMES,
                'time': '12:00',
            },
            f'efas_forecast_{year}_{month}_{day}.grib')

## === retrieve GloFAS Medium-Range Climatology === 

import cdsapi


if __name__ == '__main__':
    c = cdsapi.Client()


    YEARS  = ['%02d'%(mn) for mn in range(1979,2021)]

    MONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']
    DAYS   = ['%02d'%(mn) for mn in range(1,32)]


    for year in YEARS:
        c.retrieve(
            'cems-glofas-historical',
            {
                'system_version':'version_2_1',
                'product_type': 'consolidated',
                'hydrological_model': 'htessel_lisflood',
                'variable': 'river_discharge_in_the_last_24_hours',
                'hyear': year,,
                'hmonth': MONTHS,
                'hday': DAYS,,
                'format': 'grib',
            },
            f'glofas_historical_{year}.grib')

## === retrieve GloFAS Medium-Range Forecast === 

import cdsapi
import datetime
import warnings



def compute_dates_range(start_date,end_date,loop_days=True):


    start_date = datetime.date(*[int(x) for x in start_date.split('-')])
    
    end_date = datetime.date(*[int(x) for x in end_date.split('-')])
    
    ndays =  (end_date - start_date).days + 1
    
    dates = []
    for d in range(ndays):
        dates.append(start_date + datetime.timedelta(d))
    
    if not loop_days:
        dates = [i for i in dates if i.day == 1]
    else:
        pass
    return dates



if __name__ == '__main__':


    # start the client
    c = cdsapi.Client()


    # user inputs
    START_DATE = '2019-11-05' # first date with available data

    END_DATE = '2021-03-15' 

    LEADTIMES =  [str(lt) for lt in range(24,744,24)]


    # loop over dates and save to disk

    dates = compute_dates_range(START_DATE,END_DATE)

    for date in dates:

        year  = date.strftime('%Y')
        month = date.strftime('%m')
        day   = date.strftime('%d')

        print(f"RETRIEVING: {year}-{month}-{day}")

        c.retrieve(
            'cems-glofas-forecast',
            {
                'format': 'grib',
                'system_version':'operational',
                'hydrological_model': 'htessel_lisflood',
                'product_type':'ensemble_perturbed_forecasts',
                'variable': 'river_discharge_in_the_last_24_hours',
                'year': year,
                'month': month,
                'day': day,
                'leadtime_hour':LEADTIMES
            },
            f'glofas_forecast_{year}_{month}_{day}.grib')

## === retrieve GloFAS Medium-Range Reforecast ===

## === subset India, Pakistan, Nepal and Bangladesh region === 


import cdsapi
from datetime import datetime, timedelta


def get_monthsdays():

    start, end = datetime(2019, 1, 1), datetime(2019, 12, 31)
    days = [start + timedelta(days=i) for i in range((end - start).days + 1)]
    monthday = [d.strftime("%B-%d").split("-")  for d in days if d.weekday() in [0,3] ]   

    return monthday

MONTHSDAYS = get_monthsdays()

if __name__ == '__main__':
    c = cdsapi.Client()
    
    # user inputs
	BBOX = [40.05 ,59.95, 4.95, 95.05] # North West South East
    YEARS  = ['%d'%(y) for y in range(1999,2019)]
    LEADTIMES = ['%d'%(l) for l in range(24,1128,24)]
    
    # submit request
    for md in MONTHSDAYS:

        month = md[0].lower()
        day = md[1]

        c.retrieve(
            'cems-glofas-reforecast',
            {
                'system_version': 'version_2_2',
                'variable': 'river_discharge_in_the_last_24_hours',
                'format': 'grib',
                'hydrological_model': 'htessel_lisflood',
                'product_type': 'control_reforecast',
				'area': BBOX,# < - subset
                'hyear': YEARS,
                'hmonth': month ,
                'hday': day ,
                'leadtime_hour': LEADTIMES,
            },
            f'glofas_reforecast_{month}_{day}.grib')