# Linux and Unix Users
import irradpy
# Run Downloader
irradpy.downloader.run(auth={"uid":"USERNAME", "password": "PASSWORD"})
# Run Model
irradpy.model.ClearSkyREST2v5(latitudes, longitudes, elevations, time, dataset_dir).REST2v5()
irradpy.model.ClearSkyMAC2(latitudes, longitudes, elevations, time, dataset_dir).MAC2()


# Windows Users Only:
import irradpy
import multiprocessing
# Important Note: If you're using windows, make sure to wrap the function.
if __name__ == "__main__":
    multiprocessing.freeze_support()
    irradpy.downloader.run(auth={"uid":"USERNAME", "password": "PASSWORD"})
    
# More Examples

# Download All Data From 2018-01-01 To 2018-01-02
irradpy.downloader.run(auth={"uid":"USERNAME", "password": "PASSWORD"},
    initial_year=2018, final_year=2018,
    initial_month=1, final_month=1,
    initial_day=1, final_day=2,
    lat_1=-90, lat_2=90,
    lon_1=-180, lon_2=180,
    verbose=True,
    thread_num=20, connection_num=2
    )

# Run clear sky model from 2018-01-01 To 2018-01-02
time_delta = 10  # minute
timedef = [('2018-01-01T00:00:00', '2018-01-02T0:00:00')]
time = irradpy.model.timeseries_builder(timedef, time_delta, np.size(latitudes))
irradpy.model.ClearSkyREST2v5(latitudes, longitudes, elevations, time, dataset_dir).REST2v5()

    Parameters
    #downloader:
    collection_names : List[str]
        Variable short names, must be defined in variables.py
        if merra2_var_dict is not provided. If more than one variable,
        they are assumed to have the same original files and those will only
        be downloaded once.
    initial_year : int
        Initial year for the data to be downloaded.
        Select from [1980, Now]
    final_year : int
        Final year for the data to be downloaded.
        Select from [1980, Now]
    initial_month : int
        Initial month for the data to be downloaded.
        Select from [1, 12]
    final_month : int
        Final month for the data to be downloaded.
        Select from [1, 12]
    initial_day : int
        Initial day for the data to be downloaded.
        Select from [1, 12]
    final_day : Optional[int]
        Final day for the data to be downloaded.
    lat_1 : Optional[float]
        Define the latitude of the left bottom corner of the rectangle region of interest.
        Select a value from [-90, +90]
    lon_1 : Optional[float]
        Define the longitude of the left bottom corner of the rectangle region of interest.
        Select a value from [-180, +180]
    lat_2 : Optional[float]
        Define the latitude of the right top corner of the rectangle region of interest.
        Select a value from [-90, +90]
    lon_2 : Optional[float]
        Define the longitude of the right top corner of the rectangle region of interest.
        Select a value from [-180, +180]
    merra2_var_dicts : Optional[List[dict]]
        Dictionary containing the following keys:
        esdt_dir, collection, var_name, standard_name,
        see the Bosilovich paper for details. Same order as collection_names.
    output_dir : Union[str, Path]
    auth : dict
        Dictionary contains login information.
        {"uid": "USERNAME", "password": "PASSWORD"}
    verbose : bool
    thread_num : Optional[int]
        Number of Files to be downloaded simutanously.
    connection_num : Optional[int]
        Number of Connections for each file to be downloaded simutanously.
        
    #irradpy.model.timeseries_builder:
    timedef: list [(start time , end time)], optional — specify the start 
        time(s) and end time(s) of the location(s) of interest.
    time_delta: integer, optional — specify the temporal resolution of the 
        time series in minutes
    num_station: integer, compulsory - specify the station number of interest.
        if timedef less than num_station, timeseries_builder will expand it 
        for every station.
    
    #irradpy.model.clearSkyRadiation_MAC2.py && irradpy.model.clearSkyRadiation_REST2v5.py:
    
    latitudes: numpy.ndarray, float, compulsory — Define the latitude(s) of the 
        location(s) of interest, size must match longitudes. 
    longitudes: numpy.ndarray, float, compulsory — Define the longitude(s) of 
        the location(s) of interest, size must match latitudes. 
    elevations: numpy.ndarray, float, compulsory — Define the elevation(s) of 
        the location(s) of interest, size must match lats.
    time: numpy.ndarry of dtype ̄‘datetime64[m]’, compulsory — Define the time 
        series desired.
    dataset_dir: Union[string, Path], optional — Define the location of the 
        dataset of downloaded and merged data.