#! /usr/bin/env python
"""

"""

# region // imports
import sys
import os
from SDToolBox import outputmessages as outputmessages
from SDToolBox import data_acquisition
from netCDF4 import Dataset
from netCDF4.utils import ncinfo 
import datetime
import numpy as np

# endregion

# region // variables

# endregion

class ExtractSeaLevelPressureEARTH:

    __lon_key = 'lon'
    __lat_key = 'lat'
    __input_data = None

    def __init__(self, input_data: data_acquisition.InputData):
        if not input_data or not input_data.coord_list or len(input_data.coord_list) < 1:
            raise IOError('No valid input data.')

        input_data.lon_key = self.__lon_key
        input_data.lat_key = self.__lat_key
        self.__input_data = input_data
        self.__input_lat =  input_data.coord_list[0]
        self.__input_lon = input_data.coord_list[1]

    def subset_earth_v2(self, directory_path: str, year_from: int, year_to: int) :
        ds_format = 'netCDF4'
        case_dataset_list = []
        years = generate_years_array_slp(year_from, year_to)
        months_list = ['01', '02', '03', '04', '05', '06', '07', '08','09','10','11', '12']
        
        for yearidx, year in enumerate(years) :
            for month in months_list :
                case_file_path = directory_path + 'RCP45_\\' + 'EC-Earth_RCP4.5_MSLP_' + str(year) + month + '.nc'
                with Dataset(case_file_path, 'r', ds_format) as case_dataset:
                    # Find nearest point
                    # (considering we are only selecting a point)
                    lat_list = case_dataset.variables[self.__lat_key][:]
                    lon_list = case_dataset.variables[self.__lon_key][:]

                    lon_list = rescale_input_area(lon_list, 180)  # longitude has to be rescaled to -180:180
                    corr_lon = [data_acquisition.get_nearest_neighbor(input_lon, lon_list) for input_lon in self.__input_lon]
                    corr_lat = [data_acquisition.get_nearest_neighbor(input_lat, lat_list) for input_lat in self.__input_lat]
                    
                    pos_lon1 = corr_lon[0]
                    pos_lon2 = corr_lon[-1]
                    pos_lat1 = corr_lat[0]
                    pos_lat2 = corr_lat[-1]
                    
                    # concatenates the data. Time has to be formatted accordingly

                    if(pos_lon1 < pos_lon2) & (pos_lat1 < pos_lat2) :
                        if (yearidx == 0) :
                            variable_subset = case_dataset.variables['var151'][:,corr_lat,corr_lon]
                            variable_subset = np.squeeze(variable_subset)
                            reftime = case_dataset['time'].units.split(' ')
                            datetime.strptime(reftime[2]+' '+reftime[3], '%Y-%m-%d %H:%M:%S.%f')
                          #  reftime.append([datetime.strptime(reftime[2]+' '+reftime[3], '%Y-%m-%d %H:%M:%S.%f')+timedelta(hours=int(ti)) for ti in case_dataset['time'][:]])
                        else :
                            variable_sub = np.concatenate(variable_sub,
                                case_dataset['var151'][:,corr_lat,corr_lon])
                            #format the time 
                            variable_sub = np.concatenate(variable_sub,
                                case_dataset['time'][:,corr_lat,corr_lon])
                    else :
                        if (yearidx == 0) :
                            variable_subset = case_dataset.variables['var151'][:,corr_lat,corr_lon]
                            variable_subset = np.squeeze(variable_subset)
                         #   variable_subset = np.concatenate(case_dataset.variables['time'][:,corr_lat,corr_lon])
                        else :
                            variable_subset = case_dataset.variables['var151'][:,corr_lat,corr_lon]
                            variable_subset = np.squeeze(variable_subset)

                    # Get variable subset
    
                case_dataset_list.append(variable_subset)

        return case_dataset_list



    # this method could be moved in a generic utils stript outside here
  
def generate_years_array_slp(year_from, year_to):
    years = []
    for i in range(year_to - year_from):
        years.append(year_from + i) # fills an array of years
    years.append(year_to)
    return years

def rescale_input_area(input_list : list, scale_factor: int):
    for idx,element in enumerate(input_list):
        input_list[idx] = input_list[idx] - 180
    return input_list