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# **************************** LICENSE START ***********************************
#
# Copyright 2022 ECMWF. This software is distributed under the terms
# of the Apache License version 2.0. In applying this license, ECMWF does not
# waive the privileges and immunities granted to it by virtue of its status as
# an Intergovernmental Organization or submit itself to any jurisdiction.
#
# ***************************** LICENSE END ************************************
import cdsapi

c = cdsapi.Client()

c.retrieve('reanalysis-era5-complete', {

    'class': 'ea',

    'date': '2021-01-01',

    'expver': '1',

    'levelist':'1/to/137',

    'levtype': 'ml',

    'param': '130/152',

    'step': '0',

    'stream': 'oper',

    'time': '00/to/06/by/1',

    'type': 'an',

    'grid': '1.0/1.0'

}, 'output_00_06_130_152_1x1.grib')

# **************************** LICENSE START ***********************************
#
# Copyright 2022 ECMWF. This software is distributed under the terms
# of the Apache License version 2.0. In applying this license, ECMWF does not
# waive the privileges and immunities granted to it by virtue of its status as
# an Intergovernmental Organization or submit itself to any jurisdiction.
#
# ***************************** LICENSE END ************************************


import cfgrib
import xarray as xr
import numpy as np
from eccodes import *
import matplotlib.pyplot as plt
import argparse
import sys
import os

def parse_args():
    ''' Parse program arguments using ArgumentParser'''
    parser = argparse.ArgumentParser(description ="Python tool to calculate the model level variable at a given pressure level and write data to a netCDF file")         
    parser.add_argument('-p', '--pressure', required=True, nargs='+',type=float,
                        help='Pressure levels (Pa) to calculate the variable')
    parser.add_argument('-o', '--output', required=False, help='name of the output file (default "output.nc"')
    parser.add_argument('-i', '--input', required=True, metavar='input.grib', type=str,
                        help=('grib file with required variable(s) on model level and surface pressure fields',
                              'the model levels'))
    args = parser.parse_args()
    if not args.output:
        args.output = 'output.nc' 
    return args

def get_input_variable_list(fin):
    f = open(fin)
    var_list = []
    while 1:
        gid = codes_grib_new_from_file(f)
        if gid is None:
            break
        keys = ('dataDate', 'dataTime', 'shortName')
        for key in keys:
            if key == 'shortName':
              var_list.append(codes_get(gid, key))
        codes_release(gid)
    var_list_unique = list(set(var_list))
    f.close()
    if 'lnsp' not in var_list_unique:
      print("Error - lnsp variable missing from input file -exiting")
      sys.exit()
    if len(var_list_unique) < 2:
      print("Error - Data variable missing from input file -exiting")
      sys.exit()
    return var_list_unique

def check_requested_levels(plevs):
    check_lev = True
    if len(plevs) > 1:
        error_msg = "Error - only specify 1 input pressure level to interpolate to"
    else:
        for lev in plevs:
           if lev < 0 or lev > 110000 :
              check_lev = False
              error_msg = "Error - negative values and large positive values for pressure are not allowed -exiting"
    if check_lev == False:
        print(error_msg)  
        sys.exit()    
    return check_lev

def check_in_range(data_array,requested_levels):
    amin = data_array.minimum()
    amax = data_array.maximum()
    print("min max ",amin,amax)          


def vertical_interpolate(vcoord_data, interp_var, interp_levels):
    """A function to interpolate sounding data from each station to
    every millibar. Assumes a log-linear relationship.

    Input
    -----
    vcoord_data : A 1D array of vertical level values (e.g., pressure from a radiosonde)
    interp_var : A 1D array of the variable to be interpolated to all pressure levels
    vcoord_interp_levels : A 1D array containing veritcal levels to interpolate to

    Return
    ------
    interp_data : A 1D array that contains the interpolated variable on the interp_levels
    """
    l_count = 0
    for l in interp_levels:
      if l < np.min(vcoord_data) or l > np.max(vcoord_data):
          interp_data[l_count] = np.NAN           
    # Make vertical coordinate data and grid level log variables
    lnp = np.log(vcoord_data)
    lnp_intervals = [np.log(x) for x in interp_levels]
    # Use numpy to interpolate from observed levels to grid levels
    interp_data = np.interp(lnp_intervals, lnp, interp_var)
    return interp_data[0]

def calculate_pressure_on_model_levels(ds_var,ds_lnsp):
    # Get the number of model levels in the input variable 
    nlevs=ds_var.sizes['hybrid']
    # Get the a and b coefficients from the pv array to calculate the model level pressure 
    pv_coeff = np.array(ds_var.GRIB_pv)
    pv_coeff=pv_coeff.reshape(2,nlevs+1)
    a_coeff=pv_coeff[0,:]
    b_coeff=pv_coeff[1,:]
    # get the surface pressure in hPa
    sp = np.exp(ds_lnsp)
    p_half=[]
    for i in range(len(a_coeff)):  
        p_half.append(a_coeff[i] + b_coeff[i] * sp)
    p_ml=[]
    for hybrid in range(len(p_half) - 1):
        p_ml.append((p_half[hybrid + 1] + p_half[hybrid]) / 2.0)   
    ds_p_ml = xr.concat(p_ml, 'hybrid')
    return ds_p_ml

def plot_profile(var_ml,press_ml, var_int_press,var_int_plevs,tstep,lat,lon):

    var_v= var_ml.sel(time = var_ml.time[tstep],longitude=lon, latitude=lat, method='nearest')
    var_v_values = var_v.values
    var_p= press_ml.sel(time = var_ml.time[tstep],longitude=lon, latitude=lat, method='nearest')
    var_p_values = var_p.values
    var_ip= var_int_press.sel(time = var_ml.time[tstep],longitude=lon, latitude=lat, method='nearest')
    var_ip_values = var_ip.values 
    var_ip_p = var_ip.pressure
    var_ip_p_values = var_ip_p.values
    plt.axis([min(var_v_values), max(var_v_values), max(var_p_values), min(var_p_values)])
    plt.plot(var_v_values,var_p_values, 'o', color = 'black')
    plt.plot(var_ip_values,var_ip_p_values,'o', color = 'red')
    plt.show()
    return

def calculate_interpolated_pressure_field(data_var_on_ml, data_p_on_ml,plevs):
    nlevs = len(data_var_on_ml.hybrid)
    p_array = np.stack(data_p_on_ml, axis=2).flatten()
    # Flatten the data array to enable faster processing
    var_array = np.stack(data_var_on_ml, axis=2).flatten()
    no_grid_points =  int(len(var_array)/nlevs)
    interpolated_var = np.empty((len(plevs), no_grid_points))
    ds_shape = data_var_on_ml.shape
    nlats_values = data_var_on_ml.coords['latitude']
    nlons_values = data_var_on_ml.coords['longitude']
    nlats = len(nlats_values)
    nlons = len(nlons_values)
 
#     Iterate over the data, selecting one vertical profile at a time
    count = 0
    profile_count = 0
    interpolated_values=[]
    for point in range(no_grid_points):
        offset =  count*nlevs
        var_profile = var_array[offset:offset+nlevs]
        p_profile = p_array[offset:offset+nlevs]
        interpolated_values.append(vertical_interpolate(p_profile, var_profile, plevs))
        profile_count += len(p_profile)
        count = count + 1
    interpolated_field=np.asarray(interpolated_values).reshape(len(plevs),nlats,nlons)    
    return interpolated_field  

def check_data_cube(dc):
    checks = True
    for var_name in dc.variables:
        if var_name in ['time','step','hybrid','latitude','longitude','valid_time']:
            continue               
        if var_name == 'lnsp':
            lnsp_dims = ['time','latitude','longitude']
            if all(value in lnsp_dims for value in dc.variables[var_name].dims):
                continue
            else:
                print("Not all required lnsp dimensions found -exiting ", dc.variables[var_name].dims)
                checks = False
        else:
            var_dims = ['time','hybrid','latitude','longitude']
            if all(value in var_dims for value in dc.variables[var_name].dims):
                continue
            else:
                print("Not all required variable dimensions found -exiting ",dc.variables[var_name].dims)
                checks = False
            continue
    return checks

def main():
    '''Main function'''
    print("-p <pressure level (Pa) > -o <output_file> -i <input grib file>")
    print("e.g. to process a grib file containing 6 hours of lnsp and temperature data to the 500 hPa level:")
    print("python3 script.py -o output_press.nc -p 50000  -i output_00_06_130_152_1x1.grib`n")
    args = parse_args()
 
    print('Arguments: %s' % ", ".join(
        ['%s: %s' % (k, v) for k, v in vars(args).items()]))

    plevels = args.pressure
    plevels.sort(reverse = True) 

    check_requested_levels(plevels)

    input_fname = args.input 
    output_fname = args.output
    if not os.path.isfile(input_fname):
        print("Input file does not exist - exiting")
        sys.exit()
    variable_list = get_input_variable_list(input_fname)
    # Create a data object to hold the input and derived data
    data_cube = xr.merge(cfgrib.open_datasets(input_fname, backend_kwargs={'read_keys': ['pv']}), combine_attrs='override')
    if not check_data_cube(data_cube):
        sys.exit()
   # Get the ln surface pressure
    lnsp = data_cube['lnsp']
    for var in variable_list:
      if var == 'lnsp':
          continue
      else:
          data_cube['pml']=data_cube[var].copy()
          break
    for var in variable_list:
        if var == 'lnsp' :
            continue
        data_pressure_on_model_levels_list =[]
        for time_step in range(len(data_cube[var].time)):
            data_slice_var=data_cube[var][time_step,:,:,:]
            data_slice_lnsp=data_cube['lnsp'][time_step,:,:]
#   Get the pressure field on model levels for each timestep            
            data_cube['pml'][time_step,:,:,:] = calculate_pressure_on_model_levels(data_slice_var,data_slice_lnsp)
    data_cube['pml'].attrs = {'units' : 'Pa','long_name':'pressure','standard_name':'air_pressure','positive':'down'}
    all_interpolated_var_fields_list = []
    for var in variable_list:
        if var == 'lnsp' or var == 'pml':
            continue
        interpolated_var_field = data_cube[var].copy()
        interpolated_var_field = interpolated_var_field[:,0:len(plevels),:,:]
        interpolated_var_field = interpolated_var_field.rename({'hybrid':'pressure'})
        interpolated_var_field['pressure'] = plevels
        for time_step in range(len(data_cube[var].time)):
            var_on_ml = data_cube[var][time_step,:,:,:]
            p_on_ml = data_cube['pml'][time_step,:,:,:]
            interpolated_var_field[time_step,:,:,:] = calculate_interpolated_pressure_field(var_on_ml,p_on_ml,plevels)
        all_interpolated_var_fields_list.append(interpolated_var_field)
    all_interpolated_var_fields = xr.merge(all_interpolated_var_fields_list)   
    all_interpolated_var_fields['pressure'].attrs = {'units' : 'Pa','long_name':'pressure','standard_name':'air_pressure','positive':'down'}
    all_interpolated_var_fields.to_netcdf(output_fname)
#   Write interpolated data variable to output filename    
    PLOT_DATA = False     
    if PLOT_DATA:
        latitude = 45.0
        longitude = 0
        tstep =0
        plot_profile(data_cube[var],data_cube['pml'],interpolated_var_field,plevels,tstep,latitude,longitude)
    print("Finished interpolation of variables to pressure level")

if __name__ == '__main__':
    main()