import cdsapi
from input.user_input import *

# start_lat = 78.22 Svalbard
# start_lon = 15.65

# start_lat = 67.887382 Kiruna
# start_lon = 21.081452

startdays = ['23', '24', '25', '26', '27', '28', '29', '30', '31']
days = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31']
endascent = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22']
endfloat = ['1']

def ERAsingle(year, month, days, north_lim, south_lim, east_lim, west_lim, name):
    single = cdsapi.Client().retrieve(
        'reanalysis-era5-single-levels',
        {
            'product_type': 'reanalysis',
            'format': 'netcdf',
            'variable': [
                'cloud_base_height', 'high_cloud_cover', 'low_cloud_cover', 'medium_cloud_cover',
                'skin_temperature', 'surface_net_solar_radiation', 'surface_net_thermal_radiation',
                'surface_pressure', 'surface_solar_radiation_downwards', 'surface_thermal_radiation_downward_clear_sky',
                'surface_thermal_radiation_downwards', 'toa_incident_solar_radiation',
                'top_net_solar_radiation', 'top_net_thermal_radiation', 'total_cloud_cover',
                'total_sky_direct_solar_radiation_at_surface',
            ],
            'time': [
                '00:00', '01:00', '02:00',
                '03:00', '04:00', '05:00',
                '06:00', '07:00', '08:00',
                '09:00', '10:00', '11:00',
                '12:00', '13:00', '14:00',
                '15:00', '16:00', '17:00',
                '18:00', '19:00', '20:00',
                '21:00', '22:00', '23:00',
            ],
            'year': str(year),
            'month': str(month),
            'day': days,
            'area': [
                north_lim, west_lim, south_lim,  # north_lim, west_lim, south_lim,  # North, West, South   72, -111, 67,
                east_lim,  # east_lim,                        # East                 22,
            ],
        })
    single.download(name)


def ERAlevelAscend(year, month, days, start_lat, start_lon, name):
    north_lim = start_lat + 10.0
    south_lim = start_lat - 10.0
    east_lim = start_lon - 10.0
    west_lim = start_lon + 10.0

    ascend = cdsapi.Client().retrieve(
        'reanalysis-era5-pressure-levels',
        {
            'product_type': 'reanalysis',
            'format': 'netcdf',
            'variable': [
                'geopotential', 'temperature', 'u_component_of_wind',
                'v_component_of_wind', 'vertical_velocity',
            ],
            'pressure_level': [
                '1', '2', '3',
                '5', '7', '10',
                '20', '30', '50',
                '70', '100', '125',
                '150', '175', '200',
                '225', '250', '300',
                '350', '400', '450',
                '500', '550', '600',
                '650', '700', '750',
                '775', '800', '825',
                '850', '875', '900',
                '925', '950', '975',
                '1000',
            ],
            'year': str(year),
            'month': str(month),
            'day': days,
            'time': [
                '00:00', '01:00', '02:00',
                '03:00', '04:00', '05:00',
                '06:00', '07:00', '08:00',
                '09:00', '10:00', '11:00',
                '12:00', '13:00', '14:00',
                '15:00', '16:00', '17:00',
                '18:00', '19:00', '20:00',
                '21:00', '22:00', '23:00',
            ],
            'area': [
                north_lim, west_lim, south_lim,
                east_lim,
            ],
        })
    ascend.download(name)


def ERAlevelFloat(year, month, days, north_lim, south_lim, east_lim, west_lim, name):
    floating = cdsapi.Client().retrieve(
        'reanalysis-era5-pressure-levels',
        {
            'product_type': 'reanalysis',
            'format': 'netcdf',
            'variable': [
                'geopotential', 'temperature', 'u_component_of_wind',
                'v_component_of_wind', 'vertical_velocity',
            ],
            'pressure_level': [
                '1', '2', '3',
                '5', '7', '10',
                '20',
            ],
            'year': str(year),
            'month': str(month),
            'day': days,
            'time': [
                '00:00', '01:00', '02:00',
                '03:00', '04:00', '05:00',
                '06:00', '07:00', '08:00',
                '09:00', '10:00', '11:00',
                '12:00', '13:00', '14:00',
                '15:00', '16:00', '17:00',
                '18:00', '19:00', '20:00',
                '21:00', '22:00', '23:00',
            ],
            'area': [
                north_lim, west_lim, south_lim,
                east_lim,
            ],
        })
    floating.download(name)


##ERAsingle(2018, 5, startdays, 90, 45, 180, -180, "single_2018_1.nc")
##ERAsingle(2018, 6, days, 90, 45, 180, -180, "single_2018_2.nc")
##ERAsingle(2018, 7, days, 90, 45, 180, -180, "single_2018_3.nc")
##ERAsingle(2018, 8, endfloat, 90, 45, 180, -180, "single_2018_4.nc")

##ERAlevelAscend(2018, 5, startdays, start_lat, start_lon, "ascend_2018_kiruna_1.nc")  # start_lat = 67.887382 Kiruna
ERAlevelAscend(2018, 6, endascent, start_lat, start_lon, "ascend_2018_kiruna_2.nc")  # start_lon = 21.081452

ERAlevelFloat(2018, 5, startdays, 90, 45, -180, 180, "float_2018_1.nc")
ERAlevelFloat(2018, 6, days, 90, 45, -180, 180, "float_2018_2.nc")
ERAlevelFloat(2018, 7, days, 90, 45, -180, 180, "float_2018_3.nc")
ERAlevelFloat(2018, 8, endfloat, 90, 45, -180, 180, "float_2018_4.nc")

#ERAlevelAscend(2016, 7, ['12', '13', '14'], start_lat, start_lon, "ascend_2016_kiruna_new.nc")
#ERAsingle(2016, 7, ['12', '13', '14', '15', '16', '17', '18', '19'], 90, 45, 180, -180, "single_2016_new.nc")
#ERAlevelFloat(2016, 7, ['12', '13', '14', '15', '16', '17', '18', '19'], 90, 45, -180, 180, "float_2016_new.nc")