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# to you under the Apache License, Version 2.0 (the
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#    http://www.apache.org/licenses/LICENSE-2.0
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# under the License.

#Apache OCW lib immports
import ocw.dataset_processor as dsp
import ocw.data_source.local as local
import ocw.data_source.rcmed as rcmed
import ocw.data_source.esgf as esgf
import ocw.plotter as plotter
import ocw.utils as utils
from ocw.dataset import Bounds

import matplotlib.pyplot as plt
from matplotlib import rcParams
import numpy as np
import numpy.ma as ma
import yaml
from glob import glob
import operator
from dateutil import parser
from datetime import datetime
import os
import sys
from getpass import getpass

from metrics_and_plots import *

import ssl 

if hasattr(ssl, '_create_unverified_context'):
  ssl._create_default_https_context = ssl._create_unverified_context

config_file = str(sys.argv[1])

print 'Reading the configuration file ', config_file
config = yaml.load(open(config_file))
time_info = config['time']
temporal_resolution = time_info['temporal_resolution']

start_time = datetime.strptime(time_info['start_time'].strftime('%Y%m%d'),'%Y%m%d')
end_time = datetime.strptime(time_info['end_time'].strftime('%Y%m%d'),'%Y%m%d')

space_info = config['space']
min_lat = space_info['min_lat']
max_lat = space_info['max_lat']
min_lon = space_info['min_lon']
max_lon = space_info['max_lon']

ref_data_info = config['datasets']['reference']
model_data_info = config['datasets']['targets']
if ref_data_info['data_source'] == 'ESGF' or model_data_info['data_source'] == 'ESGF':
    username=raw_input('Enter your ESGF OpenID:\n')
    password=getpass(prompt='Enter your ESGF password:\n')

""" Step 1: Load the reference data """
ref_lat_name = None
ref_lon_name = None
if 'latitude_name' in ref_data_info.keys():
    ref_lat_name = ref_data_info['latitude_name']
if 'longitude_name' in ref_data_info.keys():
    ref_lon_name = ref_data_info['longitude_name']
print 'Loading observation dataset:\n',ref_data_info
ref_name = ref_data_info['data_name']
if ref_data_info['data_source'] == 'local':
    ref_dataset = local.load_file(ref_data_info['path'],
                                  ref_data_info['variable'], name=ref_name,
                                  lat_name=ref_lat_name, lon_name=ref_lon_name)
elif ref_data_info['data_source'] == 'rcmed':
      ref_dataset = rcmed.parameter_dataset(ref_data_info['dataset_id'],
                                            ref_data_info['parameter_id'],
                                            min_lat, max_lat, min_lon, max_lon,
                                            start_time, end_time)
elif ref_data_info['data_source'] == 'ESGF':
      ds = esgf.load_dataset(dataset_id = ref_data_info['dataset_id'],
                             variable = ref_data_info['variable'],
                             esgf_username=username,
                             esgf_password=password)
      ref_dataset = ds[0]
else:
    print ' '
if temporal_resolution == 'daily' or temporal_resolution == 'monthly':
    ref_dataset =  dsp.normalize_dataset_datetimes(ref_dataset, temporal_resolution)
if 'multiplying_factor' in ref_data_info.keys():
    ref_dataset.values = ref_dataset.values*ref_data_info['multiplying_factor']

""" Step 2: Load model NetCDF Files into OCW Dataset Objects """
model_lat_name = None
model_lon_name = None
if 'latitude_name' in model_data_info.keys():
    model_lat_name = model_data_info['latitude_name']
if 'longitude_name' in model_data_info.keys():
    model_lon_name = model_data_info['longitude_name']
boundary_check_model = True
if 'GCM_data' in model_data_info.keys():
    if model_data_info['GCM_data']:
        boundary_check_model = False                                           
print 'Loading model datasets:\n',model_data_info
if model_data_info['data_source'] == 'local':
    model_datasets, model_names = local.load_multiple_files(file_path = model_data_info['path'],
                                                            variable_name =model_data_info['variable'], 
                                                            lat_name=model_lat_name, lon_name=model_lon_name)
elif model_data_info['data_source'] == 'ESGF':
      md = esgf.load_dataset(dataset_id=model_data_info['dataset_id'],
                             variable=model_data_info['variable'],
                             esgf_username=username,
                             esgf_password=password)
      model_datasets = []
      model_names = []
      model_datasets.append(md[0])
      model_names.append(model_data_info['data_name'])
else:
    print ' '
    # TO DO: support RCMED
if temporal_resolution == 'daily' or temporal_resolution == 'monthly':
    for idata,dataset in enumerate(model_datasets):
        model_datasets[idata] = dsp.normalize_dataset_datetimes(dataset, temporal_resolution)

""" Step 3: Subset the data for temporal and spatial domain """
# Create a Bounds object to use for subsetting
if time_info['maximum_overlap_period']:
    start_time, end_time = utils.get_temporal_overlap([ref_dataset]+model_datasets)
    print 'Maximum overlap period'
    print 'start_time:', start_time
    print 'end_time:', end_time

if temporal_resolution == 'monthly' and end_time.day !=1:
    end_time = end_time.replace(day=1)
if ref_data_info['data_source'] == 'rcmed':
    min_lat = np.max([min_lat, ref_dataset.lats.min()])
    max_lat = np.min([max_lat, ref_dataset.lats.max()])
    min_lon = np.max([min_lon, ref_dataset.lons.min()])
    max_lon = np.min([max_lon, ref_dataset.lons.max()])
bounds = Bounds(min_lat, max_lat, min_lon, max_lon, start_time, end_time)

ref_dataset = dsp.subset(ref_dataset, bounds)
if ref_dataset.temporal_resolution() != temporal_resolution:
    ref_dataset = dsp.temporal_rebin(ref_dataset, temporal_resolution)
for idata,dataset in enumerate(model_datasets):
    model_datasets[idata] = dsp.subset(dataset, bounds)
    if dataset.temporal_resolution() != temporal_resolution:
        model_datasets[idata] = dsp.temporal_rebin(dataset, temporal_resolution)

# Temporaly subset both observation and model datasets for the user specified season
month_start = time_info['month_start']
month_end = time_info['month_end']
average_each_year = time_info['average_each_year']

ref_dataset = dsp.temporal_subset(ref_dataset,month_start, month_end,average_each_year)
for idata,dataset in enumerate(model_datasets):
    model_datasets[idata] = dsp.temporal_subset(dataset,month_start, month_end,average_each_year)

# generate grid points for regridding
if config['regrid']['regrid_on_reference']:
    new_lat = ref_dataset.lats
    new_lon = ref_dataset.lons 
else:
    delta_lat = config['regrid']['regrid_dlat']
    delta_lon = config['regrid']['regrid_dlon']
    nlat = (max_lat - min_lat)/delta_lat+1
    nlon = (max_lon - min_lon)/delta_lon+1
    new_lat = np.linspace(min_lat, max_lat, nlat)
    new_lon = np.linspace(min_lon, max_lon, nlon)

# number of models
nmodel = len(model_datasets)
print 'Dataset loading completed'
print 'Observation data:', ref_name 
print 'Number of model datasets:',nmodel
for model_name in model_names:
    print model_name

""" Step 4: Spatial regriding of the reference datasets """
print 'Regridding datasets: ', config['regrid']
if not config['regrid']['regrid_on_reference']:
    ref_dataset = dsp.spatial_regrid(ref_dataset, new_lat, new_lon)
    print 'Reference dataset has been regridded'
for idata,dataset in enumerate(model_datasets):
    model_datasets[idata] = dsp.spatial_regrid(dataset, new_lat, new_lon, boundary_check = boundary_check_model)
    print model_names[idata]+' has been regridded'
print 'Propagating missing data information'
ref_dataset = dsp.mask_missing_data([ref_dataset]+model_datasets)[0]
model_datasets = dsp.mask_missing_data([ref_dataset]+model_datasets)[1:]

""" Step 5: Checking and converting variable units """
print 'Checking and converting variable units'
ref_dataset = dsp.variable_unit_conversion(ref_dataset)
for idata,dataset in enumerate(model_datasets):
    model_datasets[idata] = dsp.variable_unit_conversion(dataset)
    

print 'Generating multi-model ensemble'
if len(model_datasets) >= 2.:
    model_datasets.append(dsp.ensemble(model_datasets))
    model_names.append('ENS')

""" Step 6: Generate subregion average and standard deviation """
if config['use_subregions']:
    # sort the subregion by region names and make a list
    subregions= sorted(config['subregions'].items(),key=operator.itemgetter(0))

    # number of subregions
    nsubregion = len(subregions)

    print 'Calculating spatial averages and standard deviations of ',str(nsubregion),' subregions'

    ref_subregion_mean, ref_subregion_std, subregion_array = utils.calc_subregion_area_mean_and_std([ref_dataset], subregions) 
    model_subregion_mean, model_subregion_std, subregion_array = utils.calc_subregion_area_mean_and_std(model_datasets, subregions) 

""" Step 7: Write a netCDF file """
workdir = config['workdir']
if workdir[-1] != '/':
    workdir = workdir+'/'
print 'Writing a netcdf file: ',workdir+config['output_netcdf_filename']
if not os.path.exists(workdir):
    os.system("mkdir -p "+workdir)

if config['use_subregions']:
    dsp.write_netcdf_multiple_datasets_with_subregions(ref_dataset, ref_name, model_datasets, model_names,
                                                       path=workdir+config['output_netcdf_filename'],
                                                       subregions=subregions, subregion_array = subregion_array, 
                                                       ref_subregion_mean=ref_subregion_mean, ref_subregion_std=ref_subregion_std,
                                                       model_subregion_mean=model_subregion_mean, model_subregion_std=model_subregion_std)
else:
    dsp.write_netcdf_multiple_datasets_with_subregions(ref_dataset, ref_name, model_datasets, model_names,
                                                       path=workdir+config['output_netcdf_filename'])

""" Step 8: Calculate metrics and draw plots """
nmetrics = config['number_of_metrics_and_plots']
if config['use_subregions']:
    Map_plot_subregion(subregions, ref_dataset, workdir)

if nmetrics > 0:
    print 'Calculating metrics and generating plots'
    for imetric in np.arange(nmetrics)+1:
        metrics_name = config['metrics'+'%1d' %imetric]
        plot_info = config['plots'+'%1d' %imetric]
        file_name = workdir+plot_info['file_name']

        print 'metrics '+str(imetric)+'/'+str(nmetrics)+': ', metrics_name
        if metrics_name == 'Map_plot_bias_of_multiyear_climatology':
            row, column = plot_info['subplots_array']
            if 'map_projection' in plot_info.keys():
                Map_plot_bias_of_multiyear_climatology(ref_dataset, ref_name, model_datasets, model_names,
                                          file_name, row, column, map_projection=plot_info['map_projection'])
            else:
                Map_plot_bias_of_multiyear_climatology(ref_dataset, ref_name, model_datasets, model_names,
                                          file_name, row, column)
        elif metrics_name == 'Taylor_diagram_spatial_pattern_of_multiyear_climatology':
            Taylor_diagram_spatial_pattern_of_multiyear_climatology(ref_dataset, ref_name, model_datasets, model_names,
                                      file_name)
        elif config['use_subregions']:
            if metrics_name == 'Timeseries_plot_subregion_interannual_variability' and average_each_year:
                row, column = plot_info['subplots_array']
                Time_series_subregion(ref_subregion_mean, ref_name, model_subregion_mean, model_names, False,
                                      file_name, row, column, x_tick=['Y'+str(i+1) for i in np.arange(model_subregion_mean.shape[1])])
            if metrics_name == 'Timeseries_plot_subregion_annual_cycle' and not average_each_year and month_start==1 and month_end==12:
                row, column = plot_info['subplots_array']
                Time_series_subregion(ref_subregion_mean, ref_name, model_subregion_mean, model_names, True,
                                      file_name, row, column, x_tick=['J','F','M','A','M','J','J','A','S','O','N','D'])
            if metrics_name == 'Portrait_diagram_subregion_interannual_variability' and average_each_year:
                Portrait_diagram_subregion(ref_subregion_mean, ref_name, model_subregion_mean, model_names, False,
                                      file_name)
            if metrics_name == 'Portrait_diagram_subregion_annual_cycle' and not average_each_year and month_start==1 and month_end==12:
                Portrait_diagram_subregion(ref_subregion_mean, ref_name, model_subregion_mean, model_names, True,
                                      file_name)
        else:
            print 'please check the currently supported metrics'