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"""
    GPM_WRF24_JPDF_comparison.py

    This is an example of calculating the joint probability distribution
    function of rainfall intensity and duration for the Northern Great
    Plains using GPM IMERG data for June/01/2015

    In this example:

    1. Load the GPM and WRF24 datasets with spatial filter.
    2. Load the spatial filter (Bukovsky region mask).
    3. Spatially subset the WRF data.
    4. Analyze the wet spells.
    5. Calculate the joint PDF(JPDF) of spell_duration and peak_rainfall.
    6. Visualize the JPDF.

    OCW modules demonstrated:

    1. datasource/local
    2. dataset
    3. dataset_processor
    4. metrics
    5. plotter

"""

from ocw.dataset import Bounds
import ocw.data_source.local as local
import ocw.dataset_processor as dsp
import ocw.metrics as metrics
import ocw.plotter as plotter

import numpy as np
import numpy.ma as ma
from pickle import load, dump

"""
This is an example of calculating the joint probability distribution function of rainfall intensity and duration for the Northern Great Plains using GPM IMERG data for June/01/2015
""" 

""" Step 1: Load the GPM and WRF24 datasets with spatial filter """

GPM_dataset_filtered = local.load_GPM_IMERG_files_with_spatial_filter(
                           file_path='./data/GPM_2015_summer/', 
                           filename_pattern=['*2015*.HDF5'],
                           user_mask_file='Bukovsky_regions.nc',
                           mask_variable_name='Bukovsky',
                           user_mask_values=[10],
                           longitude_name='lon',
                           latitude_name='lat')

WRF_dataset = local.load_WRF_2d_files_RAIN(
                  file_path='./data/WRF24_2010_summer/', 
                  filename_pattern=['wrf2dout*'])

""" Step 2: Load the spatial filter (Bukovsky region mask) """

Bukovsky_mask = Bounds(boundary_type='user',
                       user_mask_file='Bukovsky_regions.nc',
                       mask_variable_name='Bukovsky',
                       longitude_name='lon',
                       latitude_name='lat')  

""" Step 3: Spatial subset the WRF data (for Northern Great Plains, user_mask_values=[10]) """

WRF_dataset_filtered = dsp.subset(WRF_dataset, 
                           Bukovsky_mask, user_mask_values=[10])

""" Step 4: Analyze the wet spells """
duration1, peak1, total1 = metrics.wet_spell_analysis(GPM_dataset_filtered, 
                               threshold=0.1, nyear=1, dt=0.5)
duration2, peak2, total2 = metrics.wet_spell_analysis(WRF_dataset_filtered.values, 
                               threshold=0.1, nyear=1, dt=0.5)

""" Step 5: Calculate the joint PDF(JPDF) of spell_duration and peak_rainfall """

histo2d_GPM = metrics.calc_joint_histogram(data_array1=duration1, data_array2=peak1, 
                   bins_for_data1=np.append(np.arange(25)+0.5,[48.5,120.5]),
                   bins_for_data2=[0.1,0.2,0.5,1.0,2.0,5.0,10,20,30]) 
histo2d_GPM = histo2d_GPM/np.sum(histo2d_GPM)*100.

histo2d_WRF = metrics.calc_joint_histogram(data_array1=duration2, data_array2=peak2, 
                   bins_for_data1=np.append(np.arange(25)+0.5,[48.5,120.5]),
                   bins_for_data2=[0.1,0.2,0.5,1.0,2.0,5.0,10,20,30]) 
histo2d_WRF = histo2d_WRF/np.sum(histo2d_WRF)*100.

""" Step 6: Visualize the JPDF """


plot_level = np.array([0., 0.01,0.05, 0.1, 0.2, 0.5,1,2,3,5,10,25])
plotter.draw_precipitation_JPDF(plot_data=np.transpose(histo2d_GPM), 
          plot_level=plot_level, title='', 
          x_ticks=[0.5, 4.5, 9.5, 14.5, 19.5, 23.5], x_names=['1','5','10','15','20','24'],
          y_ticks=np.arange(9), y_names=['0.1','0.2','0.5','1.0','2.0','5.0','10','20','30'], 
          output_file='GPM_JPDF_example')
plotter.draw_precipitation_JPDF(plot_data=np.transpose(histo2d_WRF), 
          plot_level=plot_level, title='', 
          x_ticks=[0.5, 4.5, 9.5, 14.5, 19.5, 23.5], x_names=['1','5','10','15','20','24'],
          y_ticks=np.arange(9), y_names=['0.1','0.2','0.5','1.0','2.0','5.0','10','20','30'], 
          output_file='WRF24_JPDF_example')

overlap = metrics.calc_histogram_overlap(histo2d_GPM, histo2d_WRF)
plot_level = np.array([-21, -3, -1, -0.5, -0.2, -0.1, -0.05, 0, 0.05, 0.1, 0.2, 0.5, 1, 3, 21])
cbar_ticks = [-2, -0.5, -0.1, 0.1, 0.5, 2]
cbar_label = [str(i) for i in cbar_ticks]
plotter.draw_precipitation_JPDF(plot_data=np.transpose(histo2d_WRF - histo2d_GPM), 
          plot_level=plot_level, title='overlap %d ' %overlap +r'%', diff_plot=True,
          x_ticks=[0.5, 4.5, 9.5, 14.5, 19.5, 23.5], x_names=['1','5','10','15','20','24'],
          y_ticks=np.arange(9), y_names=['0.1','0.2','0.5','1.0','2.0','5.0','10','20','30'],
          output_file='GPM_WRF24_JPDF_comparison',  
          cbar_ticks=cbar_ticks, cbar_label=cbar_label)