global_funcs.py

# -*- coding: utf-8 -*-
"""
Created on Thu Aug 10 10:00:00 2023

@author: Derrick Muheki
"""

import os
import xarray as xr
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
import matplotlib.transforms as transforms
import matplotlib.gridspec as gridspec
import matplotlib.ticker as ticker
import matplotlib.colors as mcolors
import matplotlib as mpl
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from matplotlib import gridspec
from scipy import stats
from scipy.special import logsumexp
from scipy.stats import spearmanr
import seaborn as sns
from numpy.linalg import inv, det
from statistics import mean
from fractions import Fraction
import itertools




#%% SETTING UP THE CURRENT WORKING DIRECTORY; for both the input and output folders
cwd = os.getcwd()


#%% SETTING MAP EXTENT

# Entire globe
map_extent = [-180, 180, -60, 90] # (longitude min, longitude max, latitude min, latitude max)


#%% Function to extract starting year from file name to use in the next function for the start period of the data
def read_start_year(file):
    """ Read the starting year of the data from the file name


    Parameters
    ----------
    file : files in data directory (string)

    Returns
    -------
    start_year (integer)

    """
    # find the first consecutive 4 digits in the file name; which in our case represents the starting year of the data
    years = re.findall('\\d{4}', file) 
    start_year = int(years.pop(0))
    return start_year  #returns start year that is used in decoding the times for the xarray data


#%% Function for reading NetCDF4 data 
def nc_read(file, start_year, type_of_extreme_climate_event, time_dim):
    
    """ Reading netcdfs based on occurrence variable & Clip out data for the study area: The East African Region 
    
    Parameters
    ----------
    file : files in data directory (string)
    occurrence_variable : target variable name (string)
    
    Returns
    ------- 
    Xarray data array
    """
    # initiate as dataset 
    ds = xr.open_dataset(file,decode_times=False)
    
    # convert to data array for target variable
    da = ds['exposure']
    
    if time_dim:
        # manually decode times from integer to datetime series 
        new_dates = pd.date_range(start=str(start_year)+'-1-1', periods=da.sizes['time'], freq='YS')
        da['time'] = new_dates
    
    
    # Clip out data for Specific Region that lies between LATITUDES ##N and ##S & LONGITUDES ##E and ##E
    latitude_bounds, longitude_bounds =[90, -60], [-180, 180]
    clipped_da = da.sel(lat=slice(*latitude_bounds), lon=slice(*longitude_bounds)) #Clipped dataset
       
    return clipped_da


#%% Function for returning array showing the occurence of an extreme climate event at a location per year
def extreme_event(extreme_event_data):
    """ Investigates the occurrence of an extreme event in a grid (location) per year
    
    Parameters
    ----------
    extreme_event_data : Xarray data array

    Returns
    Xarray data array (boolean where 1 means the extreme event was recorded in that location during that year)

    """
    
    extreme_event = xr.where(extreme_event_data>0.005, 1, (xr.where(np.isnan(extreme_event_data), np.nan, 0))) #returns 1 where extreme event was recorded in that location during that year and O where false, as well as NaN values e.g., over the ocean
    
    return extreme_event


#%% Function for changing event names from the original Lange et. al (2020) dataset folder names

def event_name(type_of_extreme_climate_event_selected):
    """ changing event names from the original Lange et. al (2020) dataset folder names
    
    Parameters
    ----------
    type_of_extreme_climate_event_selected : String

    Returns
    -------
    String (Extreme Event name)

    """

    if type_of_extreme_climate_event_selected == 'floodedarea':
        event_name = 'River Floods'
    if type_of_extreme_climate_event_selected == 'driedarea':
        event_name = 'Droughts'
    if type_of_extreme_climate_event_selected == 'heatwavedarea':
        event_name = 'Heatwaves'
    if type_of_extreme_climate_event_selected == 'cropfailedarea':
        event_name = 'Crop Failures'
    if type_of_extreme_climate_event_selected =='burntarea':
        event_name = 'Wildfires'
    if type_of_extreme_climate_event_selected == 'tropicalcyclonedarea':
        event_name ='Tropical Cyclones'
    
    return event_name


#%%


#%% Function for plotting map showing the length of spell with impacts (years with consecutive occurrence of an extreme event) in the same location over the entire dataset period
def length_of_spell_with_occurrence_of_extreme_event(occurrence_of_extreme_event):
    
    """Determine the length of spell with impacts (years with consecutive occurrence of an extreme event) in the same location over the entire dataset period
    
    Parameters
    ----------
    occurrence of compound extreme event : Xarray data array (boolean with true for locations with the occurrence of an extreme event within the same year)
    
    Returns
    -------
    Xarray with the length of spell with impacts (years with consecutive occurrence of an extreme event) in the same location over the entire dataset period
              
    """
    
    # Calculates the cumulative sum and whenever a false is met, it resets the sum to zero. thus the .max() returns the maximum cummumlative sum along the entire time dimension
    length_of_spell_with_occurrence_of_extreme_event = (occurrence_of_extreme_event.cumsum('time',skipna=False) - occurrence_of_extreme_event.cumsum('time',skipna=False).where(occurrence_of_extreme_event.values == False).ffill('time').fillna(0))
       
    return length_of_spell_with_occurrence_of_extreme_event


#%% Function for plotting map showing the maximum number of years with consecutive occurrence of an extreme event in the same location during the entire dataset period
def maximum_no_of_years_with_consecutive_ocuurence_of_an_extreme_event(length_of_spell_with_occurrence_of_extreme_event):
    
    """Determine the maximum number of years with consecutive occurrence of an extreme event in the same location over the entire dataset period
    
    Parameters
    ----------
    occurrence of compound extreme event : Xarray data array (boolean with true for locations with the occurrence of an extreme event within the same year)
    
    Returns
    -------
    Xarray with the maximum number of years with consecutive occurrence of an extreme event in the same location over the entire dataset period
              
    """
    
    # Calculates the cumulative sum and whenever a false is met, it resets the sum to zero. thus the .max() returns the maximum cummumlative sum along the entire time dimension
    maximum_no_of_years_with_consecutive_ocuurence_of_an_extreme_event = length_of_spell_with_occurrence_of_extreme_event.max('time')
          
    return maximum_no_of_years_with_consecutive_ocuurence_of_an_extreme_event


#%% Select from a list of Global Impact Models e.g. GHMs, GGCMS, GVMs etc. for which the Global Climate Models are based on
def impact_model(extreme_event, gcm):
    """ Select from a list of Global Impact Models e.g. GHMs, GGCMS, GVMs etc. driven by the same Global Climate Models (GCMs) 
    
    Parameters
    ----------
    extreme_event: String
    gcm: String    

    Returns
    -------
    filtered_dataset: Global impact model' files driven by the same Global Climate Model and directories for the files (dataset).

    """
    
    # List of Global Impact Models e.g. GHMs, GGCMS, GVMs etc.
    list_of_gms = os.path.join(cwd, extreme_event)
    
    all_available_of_impact_model_data_files_under_gcm = []  # List of available impact models per extreme event category
    
    
    for gm in os.listdir(list_of_gms):       
        gms = os.path.join(list_of_gms, gm)
        for file in os.listdir(gms):
            impact_model_files_directory = os.path.join(gms, file)
            all_available_of_impact_model_data_files_under_gcm.append(impact_model_files_directory)
        
    # Filter out datasets to include only impact models driven by the same GCM 
    filter_data_with_gcm = re.compile('.*{}*'.format(gcm))
    filtered_dataset = list(filter(filter_data_with_gcm.match,all_available_of_impact_model_data_files_under_gcm))
    
    #print(filtered_dataset)  
    
    return filtered_dataset


#%% Function for returning extreme event occurrence per grid for a given scenario and given time period considering an ensemble of GCMs
def extreme_event_occurrence(type_of_extreme_climate_event, list_of_impact_models_driven_by_same_gcm, scenario_of_dataset):
    """ Function for returning extreme event occurrence per grid for a given scenario and given time period considering an ensemble of GCMs
    
    Parameters
    ----------
    type_of_extreme_climate_event : String
    list_of_impact_models_driven_by_same_gcm : List
    scenario_of dataset : String

    Returns
    -------
    Tuple (with [0] & [1] Xarray (Extreme event occurrences for respective time period))

    """

    # Creating list for all available data files according to selected criteria: Extreme Event Type, Impact Model driven by the same Global Climate Model and Time Period/Scenario/RCP
    extreme_event_datasets_in_the_scenario =[] 
    for impact_model in list_of_impact_models_driven_by_same_gcm:
        if('landarea' in impact_model): #Selecting only the land area datafiles (exposure on land), thus excluding the available population datafiles within the dataset
            if(scenario_of_dataset in impact_model): #Selecting data for only the selected time period/scenario/RCP
                #data_files = os.path.join(impact_model_list_of_gcms[1], gcm)
                extreme_event_datasets_in_the_scenario.append(impact_model)
            
    print('*********PROCESSING DATA************PLEASE WAIT*********** \n')

    # Filtering the extreme event datasets in the scenario for the different time periods (1661-1860, 1861-2005, 2006-2099 and 2100-2299)

    # Filter out datasets for the period from 1661 until 1860    
    #filter_data_from_1661_until_1860 = re.compile('.*_1661_*') # start year of the period = 1661
    #extreme_event_data_from_1661_until_1860 =list(filter(filter_data_from_1661_until_1860.match,extreme_event_datasets_in_the_scenario))

    # Filter out datasets for the period from 1861 until 2005  
    filter_data_from_1861_until_2005 = re.compile('.*_1861_*') # start year of the period = 1861
    extreme_event_data_from_1861_until_2005 =list(filter(filter_data_from_1861_until_2005.match,extreme_event_datasets_in_the_scenario))

    # Filter out datasets for the period from 2006 until 2099
    filter_data_from_2006_until_2099 = re.compile('.*_2006_*') # start year of the period = 2006
    extreme_event_data_from_2006_until_2099 =list(filter(filter_data_from_2006_until_2099.match,extreme_event_datasets_in_the_scenario))

    # Filter out datasets for the period from 2100 until 2299
    # filter_data_from_2100_until_2299 = re.compile('.*_2100_*') # start year of the period = 2100
    # extreme_event_data_from_2100_until_2299 =list(filter(filter_data_from_2100_until_2299.match,extreme_event_datasets_in_the_scenario))


    # =============================================================================
    # OCCURRENCE OF EXTREME EVENT WITHIN SCENARIO CONSIDERING THE MAXIMUM/EXTREME VALUES PER GRID FROM AN ENSEMBLE OF GCMS
    # =============================================================================

    # OCCURRENCE OF EXTREME EVENT FROM 1861 UNTIL 2005

    occurrence_of_extreme_event_datasets_from_1861_until_2005 =[]
    for file in extreme_event_data_from_1861_until_2005:
        start_year_of_the_data = read_start_year(file) # function to get the starting year of the data from file name
        extreme_event_from_1861_until_2005 = nc_read(file, start_year_of_the_data,type_of_extreme_climate_event, time_dim=True) # function to read the NetCDF4 files based on occurrence variable
    
        # occurence of an extreme event...as a boolean...true or false
        occurrence_of_extreme_event_from_1861_until_2005 = extreme_event(extreme_event_from_1861_until_2005) #returns 1 where an extreme event was recorded in that location during that year
    
        # add the array with occurrences per GCM for same time period to list (that shall be iterated to get the extremes from the ENSEMBLE of these different GCMs)
        occurrence_of_extreme_event_datasets_from_1861_until_2005.append(occurrence_of_extreme_event_from_1861_until_2005)

    if len(occurrence_of_extreme_event_datasets_from_1861_until_2005) == 0: # check for empty data set list
        print('No data available for the selected extreme events for the selected GCM scenario during the period from 1861 until 2015 \n *********PROCESSING DATA************PLEASE WAIT*********** \n')
        occurrence_of_extreme_event_considering_ensemble_of_gcms_from_1861_until_2005 = xr.DataArray([]) #create empty array
    else: # occurrence of extreme event in arrays for each available impact model driven by the same GCM
        occurrence_of_extreme_event_considering_ensemble_of_gcms_from_1861_until_2005 = occurrence_of_extreme_event_datasets_from_1861_until_2005


    # OCCURRENCE OF EXTREME EVENT FROM 2006 UNTIL 2099

    occurrence_of_extreme_event_datasets_from_2006_until_2099 =[]
    for file in extreme_event_data_from_2006_until_2099:
        start_year_of_the_data = read_start_year(file) # function to get the starting year of the data from file name
        extreme_event_from_2006_until_2099 = nc_read(file, start_year_of_the_data, type_of_extreme_climate_event, time_dim=True) # function to read the NetCDF4 files based on occurrence variable
    
        # occurence of an extreme event...as a boolean...true or false
        occurrence_of_extreme_event_from_2006_until_2099 = extreme_event(extreme_event_from_2006_until_2099) #returns 1 where an extreme event was recorded in that location during that year
    
        # add the array with occurrences per GCM for ame time period to list (that shall be iterated to get the extremes from the ENSEMBLE of these different GCMs)
        occurrence_of_extreme_event_datasets_from_2006_until_2099.append(occurrence_of_extreme_event_from_2006_until_2099)

    if len(occurrence_of_extreme_event_datasets_from_2006_until_2099) == 0: # check for empty data set list
        print('No data available for the selected extreme events for the selected GCM scenario during the period from 2006 until 2099 \n *********PROCESSING DATA************PLEASE WAIT*********** \n')
        occurrence_of_extreme_event_considering_ensemble_of_gcms_from_2006_until_2099 = xr.DataArray([]) #create empty array
    else: # occurrence of extreme event in arrays for each available impact model driven by the same GCM
        occurrence_of_extreme_event_considering_ensemble_of_gcms_from_2006_until_2099 = occurrence_of_extreme_event_datasets_from_2006_until_2099

    
    print('\n *******************PROCESSING************************ \n')
    
    return occurrence_of_extreme_event_considering_ensemble_of_gcms_from_1861_until_2005, occurrence_of_extreme_event_considering_ensemble_of_gcms_from_2006_until_2099


#%% Function for calculating the total number of years per location that experienced extreme events. 
def total_no_of_years_with_occurrence_of_extreme_event(occurrence_of_extreme_event):
    
    """ Determine the total number of years throught the data period per location for which an extreme event was experienced
    
    Parameters
    ----------
    occurrence of compound extreme event : Xarray data array (boolean with true for locations with the occurrence of an extreme event within the same year)
    
    Returns
    -------
    Xarray with the total number of years throught the data period per location for which an extreme event was experienced   
    """
    
    # Number of years per region that experienced compound events
    total_no_of_years_with_occurrence_of_extreme_event = (occurrence_of_extreme_event).sum('time',skipna=False)
   
    return total_no_of_years_with_occurrence_of_extreme_event  



#%% Function for returning array showing locations with the occurrence of compound events within the same location in the same year
def compound_event_occurrence(occurrence_of_event_1, occurrence_of_event_2):
    
    """ Compare two arrays with occurrence of extreme climate events at the same locations and during the same years
    
    Parameters
    ----------
    occurrence_of_event_1 & occurrence_of_event_2 : Xarray data arrays for occurrence of extreme events
    
    Returns
    -------
    Xarray data array (boolean with true for locations with the occurrence of both events within the same year)
    """
       
    compound_event = np.logical_and(occurrence_of_event_1==1, occurrence_of_event_2==1)
    compound_bin  = xr.where(compound_event == True, 1, 0)#returns True for locations with occurence of compound events within same location in same year
    nanq = np.logical_and(np.isnan(occurrence_of_event_1),np.isnan(occurrence_of_event_2)) #returns array where nan is true for both event 1 and event 2 
    compound_event_bin = xr.where(nanq==True, np.nan, compound_bin) #returns 1 where extreme event was recorded in that location during that year
    
       
    return compound_event_bin




#%% Function for calculating the total number of years per location that experienced compound events. 
def total_no_of_years_with_compound_event_occurrence(occurrence_of_compound_event):
    
    """ Determine the total number of years throught the data period per location for which a compound extreme event was experienced
    
    Parameters
    ----------
    occurrence of compound extreme event : Xarray data array (boolean with true for locations with the occurrence of both events within the same year)
    
    Returns
    -------
    Xarray with the total number of years throught the data period per location for which a compound extreme event was experienced   
    """
    
    # Number of years per region that experienced compound events
    no_of_years_with_compound_events = (occurrence_of_compound_event).sum('time',skipna=False)
   
    return no_of_years_with_compound_events  


#%% Function for plotting map showing the total number of years per location that experienced compound events. FOR VISUALIZATION
def plot_total_no_of_years_with_compound_event_occurrence(no_of_years_with_compound_events, event_1_name, event_2_name, time_period, gcm, scenario):
    
    """ Plot a map showing the total number of years throught the data period per location for which a compound extreme event was experienced
    
    Parameters
    ----------
    occurrence of compound extreme event : Xarray data array (boolean with true for locations with the occurrence of both events within the same year)
    event_1_name, event_2_name : String (Extreme Events)
    gcm: String (Driving GCM)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the total number of years throught the data period per location for which a compound extreme event was experienced   
    """
       
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
          
    # Set the extent of the plotn
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
    
    # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) # 20E up to 50E
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree()) # 20N up to 10S
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)
   
    
    # Plot the gridlines for the coordinate system on the map
    #grid= ax.gridlines(draw_labels = False, dms = True )
    #grid.top_labels = False #Removes the grid labels from the top of the plot
    #grid.right_labels= False #Removes the grid labels from the right of the plot
    
    
    # Plot number of years with occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(no_of_years_with_compound_events, origin = 'upper' , extent= map_extent, cmap = plt.cm.get_cmap('viridis', 12))
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.65,0.85,'{}, {}'.format(time_period, scenario), fontsize = 8)
    plt.gcf().text(0.25,0.03,'{}'.format(gcm), fontsize= 6)
    
    # Add the title and legend to the figure and show the figure
    plt.title('Occurrence of Compound {} and {} \n'.format(event_1_name, event_2_name),fontsize=10) #Plot title  
    
    # discrete color bar legend
    #bounds = [0,5,10,15,20,25,30]
    plt.clim(0,30)
    plt.colorbar(orientation = 'horizontal', extend = 'max', shrink = 0.5).set_label(label = 'Number of years', size = 9) #Plots the legend color bar
    plt.xticks(fontsize=8, color = 'dimgrey') # color and size of longitude labels
    plt.yticks(fontsize=8, color = 'dimgrey') # color and size of latitude labels
    plt.show()
    
    #plt.close()
    
    return no_of_years_with_compound_events
    

#%% Function for plotting map showing the total number of years per location that experienced compound events. FOR VISUALIZATION
def plot_total_no_of_years_with_compound_event_occurrence_considering_all_gcms(average_no_of_years_with_compound_events_per_scenario_and_gcm, event_1_name, event_2_name, gcms):
    
    """ Plot a map showing the total number of years throught the data period per location for which a compound extreme event was experienced
    
    Parameters
    ----------
    average_no_of_years_with_compound_events_per_scenario_and_gcm : Xarray data array 
    event_1_name, event_2_name : String (Extreme Events)
    gcms: List of gcms (Driving GCM)
    time_period: String
    scenarios: List of gcms
    
    Returns
    -------
    Plot (Figure) showing the total number of years throught the data period per location for which a compound extreme event was experienced   
    """
    
    #average_no_of_years_with_compound_events_per_scenario_and_gcm = [[],[],[],[],[]] #  Where order of list is early industrial, present day, rcp 2.6, rcp6.0 and rcp 8.5
    
    for scenario in range(len(average_no_of_years_with_compound_events_per_scenario_and_gcm)):
        
        if scenario == 0:
            scenario_name = 'Early-industrial'
        if scenario == 1:
            scenario_name = 'Present-Day'
        if scenario == 2:
            scenario_name = 'RCP2.6'
        if scenario == 3:
            scenario_name = 'RCP6.0'
        if scenario == 4:
            scenario_name = 'RCP8.5'
        
        average_no_of_years_with_compound_events_per_gcm = average_no_of_years_with_compound_events_per_scenario_and_gcm[scenario]
        
        
        # Setting the projection of the map to cylindrical / Mercator
        fig, axs = plt.subplots(2,2, figsize=(10, 6.5), subplot_kw = {'projection': ccrs.PlateCarree()})  # , constrained_layout=True
        
        # since axs is a 2 dimensional array of geozaxes, we have to flatten it into 1D; as explained on a similar example on this page: https://kpegion.github.io/Pangeo-at-AOES/examples/multi-panel-cartopy.html
        axs=axs.flatten()
        
        # Add the background map to the plot
        #ax.stock_img()
        
        if scenario == 4 and event_1_name == 'Crop Failures' or scenario == 4 and event_2_name == 'Crop Failures':
            
            print('No data available for the crop failures for the selected GCM scenario under RCP8.5')
            plt.close()
        
        else:
            
            for gcm in range(len(average_no_of_years_with_compound_events_per_gcm)):           
                
                # Plot per GCM in a subplot
                plot = axs[gcm].imshow(average_no_of_years_with_compound_events_per_gcm[gcm], origin = 'upper' , extent= map_extent, cmap = plt.cm.get_cmap('viridis', 12), vmin = 0, vmax =30)
                        
                # Add the background map to the plot
                #ax.stock_img()
                      
                # Set the extent of the plotn
                axs[gcm].set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                
                # Plot the coastlines along the continents on the map
                axs[gcm].coastlines(color='dimgrey', linewidth=0.7)
                
                # Plot features: lakes, rivers and boarders
                axs[gcm].add_feature(cfeature.LAKES, alpha =0.5)
                axs[gcm].add_feature(cfeature.RIVERS)
                axs[gcm].add_feature(cfeature.OCEAN)
                axs[gcm].add_feature(cfeature.LAND, facecolor ='lightgrey')
                #ax.add_feature(cfeature.BORDERS, linestyle=':')
                
                
                # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
                xticks = [-180, -120, -60, 0, 60, 120, 180] # Longitudes
                axs[gcm].set_xticks(xticks, crs=ccrs.PlateCarree()) # 20E up to 50E
                axs[gcm].set_xticklabels(xticks, fontsize = 8)
                
                yticks = [90, 60, 30, 0, -30, -60]
                axs[gcm].set_yticks(yticks, crs=ccrs.PlateCarree()) # 20N up to 10S
                axs[gcm].set_yticklabels(yticks, fontsize = 8)
                
                lon_formatter = LongitudeFormatter()
                lat_formatter = LatitudeFormatter()
                axs[gcm].xaxis.set_major_formatter(lon_formatter)
                axs[gcm].yaxis.set_major_formatter(lat_formatter)
                
                
                # Subplot labels
                # label
                subplot_labels = ['a.','b.','c.','d.']
                axs[gcm].text(0, 1.06, subplot_labels[gcm], transform=axs[gcm].transAxes, fontsize=9, ha='left')
                # GCM
                gcm_name = gcms[gcm] # GCM 
        
                if gcm_name == 'gfdl-esm2m':
                    gcm_title = 'GFDL-ESM2M'
                if gcm_name == 'hadgem2-es':
                    gcm_title =  'HadGEM2-ES'
                if gcm_name == 'ipsl-cm5a-lr':
                    gcm_title = 'IPSL-CM5A-LR'
                if gcm_name == 'miroc5':
                    gcm_title = 'MIROC5'
               
                axs[gcm].set_title(gcm_title, fontsize = 9, loc = 'right')
                   

            # Add the title and legend to the figure and show the figure
            fig.suptitle('Occurrence of Compound {} and {} \n'.format(event_1_name, event_2_name),fontsize=10) #Plot title     
            
            # Discrete color bar legend
            fig.subplots_adjust(bottom=0.1, top=1.1, left=0.1, right=0.9, wspace=0.2, hspace=-0.5)
            cbar_ax = fig.add_axes([0.35, 0.2, 0.3, 0.02])
            cbar = fig.colorbar(plot, cax=cbar_ax, orientation='horizontal', extend='max', shrink = 0.5)  #Plots the legend color bar   
            cbar.ax.tick_params(labelsize=9) # Text size on legend color bar 
            cbar.set_label(label = 'Number of years', size = 9)
        
            # Text outside the plot to display the scenario (top-center)
            fig.text(0.5,0.935,'{}'.format(scenario_name), fontsize = 8, ha = 'center', va = 'center')
                
            # Change this directory to save the plots to your desired directory
            plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/Occurrence of Compound {} and {} under the {} scenario.pdf'.format(event_1_name, event_2_name, scenario_name), dpi = 300)
        
            plt.show()
            
        #plt.close()
        
    return average_no_of_years_with_compound_events_per_scenario_and_gcm
    


#%% Function for calculating the Probability of joint occurrence of the extreme events in one grid cell over the entire dataset period
def probability_of_occurrence_of_compound_events(no_of_years_with_compound_events, occurrence_of_compound_event):
    
    """ Determine the probability of occurrence of a compound extreme climate event over the entire dataset time period
    
    Parameters
    ----------
    occurrence of compound extreme event : Xarray data array (boolean with true for locations with the occurrence of both events within the same year)
    
    Returns
    -------
    Xarray with the probability of joint occurrence of two extreme climate events over the entire dataset time period
    
    """
    # Total number of years in the dataset
    total_no_of_years_in_dataset = len(occurrence_of_compound_event)
    
    # Probability of occurrence
    probability_of_occurrence_of_the_compound_event = no_of_years_with_compound_events/total_no_of_years_in_dataset
    
    return probability_of_occurrence_of_the_compound_event


#%% Function for plotting map showing the probability of compound events per location. FOR VISUALIZATION
def plot_probability_of_occurrence_of_compound_events_considering_all_gcms(average_probability_of_occurrence_of_the_compound_events_considering_all_gcms_and_impact_models, event_1_name, event_2_name, gcms):
    
    """ Plot a map showing the average probability of compound events per location
    
    Parameters
    ----------
    average_probability_of_occurrence_of_the_compound_events_considering_all_gcms_and_impact_models : Xarray data array 
    event_1_name, event_2_name : String (Extreme Events)
    gcms: List of gcms (Driving GCM)
    time_period: String
    scenarios: List of gcms
    
    Returns
    -------
    Plot (Figure) showing the probability per location for which a compound extreme event was experienced   
    """
    
    #average_no_of_years_with_compound_events_per_scenario_and_gcm = [[],[],[],[],[]] #  Where order of list is early industrial, present day, rcp 2.6, rcp6.0 and rcp 8.5
    
    for scenario in range(len(average_probability_of_occurrence_of_the_compound_events_considering_all_gcms_and_impact_models)):
        
        if scenario == 0:
            scenario_name = 'Early-industrial'
        if scenario == 1:
            scenario_name = 'Present-Day'
        if scenario == 2:
            scenario_name = 'RCP2.6'
        if scenario == 3:
            scenario_name = 'RCP6.0'
        if scenario == 4:
            scenario_name = 'RCP8.5'
        
        average_probability_of_occurrence_of_the_compound_events_per_gcm = average_probability_of_occurrence_of_the_compound_events_considering_all_gcms_and_impact_models[scenario]
        
        
        # Setting the projection of the map to cylindrical / Mercator
        fig, axs = plt.subplots(2,2, figsize=(10, 6.5), subplot_kw = {'projection': ccrs.PlateCarree()})  # , constrained_layout=True
        
        # since axs is a 2 dimensional array of geozaxes, we have to flatten it into 1D; as explained on a similar example on this page: https://kpegion.github.io/Pangeo-at-AOES/examples/multi-panel-cartopy.html
        axs=axs.flatten()
        
        # Add the background map to the plot
        #ax.stock_img()
        
        if scenario == 4 and event_1_name == 'Crop Failures' or scenario == 4 and event_2_name == 'Crop Failures':
            
            print('No data available for the crop failures for the selected GCM scenario under RCP8.5')
            plt.close()
        
        else:
        
            for gcm in range(len(average_probability_of_occurrence_of_the_compound_events_per_gcm)):           
                
                
                # Plot per GCM in a subplot
                plot = axs[gcm].imshow(average_probability_of_occurrence_of_the_compound_events_per_gcm[gcm], origin = 'upper' , extent= map_extent, cmap = plt.cm.get_cmap('viridis', 12), vmin = 0, vmax =0.6)
                        
                # Add the background map to the plot
                #ax.stock_img()
                      
                # Set the extent of the plotn
                axs[gcm].set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                
                # Plot the coastlines along the continents on the map
                axs[gcm].coastlines(color='dimgrey', linewidth=0.7)
                
                # Plot features: lakes, rivers and boarders
                axs[gcm].add_feature(cfeature.LAKES, alpha =0.5)
                axs[gcm].add_feature(cfeature.RIVERS)
                axs[gcm].add_feature(cfeature.OCEAN)
                axs[gcm].add_feature(cfeature.LAND, facecolor ='lightgrey')
                #ax.add_feature(cfeature.BORDERS, linestyle=':')
                
                
                # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
                xticks = [-180, -120, -60, 0, 60, 120, 180] # Longitudes
                axs[gcm].set_xticks(xticks, crs=ccrs.PlateCarree()) # 20E up to 50E
                axs[gcm].set_xticklabels(xticks, fontsize = 8)
                
                yticks = [90, 60, 30, 0, -30, -60]
                axs[gcm].set_yticks(yticks, crs=ccrs.PlateCarree()) # 20N up to 10S
                axs[gcm].set_yticklabels(yticks, fontsize = 8)
                
                lon_formatter = LongitudeFormatter()
                lat_formatter = LatitudeFormatter()
                axs[gcm].xaxis.set_major_formatter(lon_formatter)
                axs[gcm].yaxis.set_major_formatter(lat_formatter)
                
                
                # Subplot labels
                # label
                subplot_labels = ['a.','b.','c.','d.']
                axs[gcm].text(0, 1.06, subplot_labels[gcm], transform=axs[gcm].transAxes, fontsize=9, ha='left')
                # GCM
                gcm_name = gcms[gcm] # GCM 
        
                if gcm_name == 'gfdl-esm2m':
                    gcm_title = 'GFDL-ESM2M'
                if gcm_name == 'hadgem2-es':
                    gcm_title =  'HadGEM2-ES'
                if gcm_name == 'ipsl-cm5a-lr':
                    gcm_title = 'IPSL-CM5A-LR'
                if gcm_name == 'miroc5':
                    gcm_title = 'MIROC5'
               
                axs[gcm].set_title(gcm_title, fontsize = 9, loc = 'right')
          
                        
            # Add the title and legend to the figure and show the figure
            fig.suptitle('Average Probability of Occurrence of Compound {} and {} \n'.format(event_1_name, event_2_name),fontsize=10) #Plot title     
            
            # Discrete color bar legend
            fig.subplots_adjust(bottom=0.1, top=1.1, left=0.1, right=0.9, wspace=0.2, hspace=-0.5)
            cbar_ax = fig.add_axes([0.35, 0.2, 0.3, 0.02])
            cbar = fig.colorbar(plot, cax=cbar_ax, orientation='horizontal', extend='max', shrink = 0.5)  #Plots the legend color bar   
            cbar.ax.tick_params(labelsize=9) # Text size on legend color bar 
            cbar.set_label(label = 'Probability of occurrence', size = 9)
        
            # Text outside the plot to display the scenario (top-center)
            fig.text(0.5,0.935,'{}'.format(scenario_name), fontsize = 8, ha = 'center', va = 'center')
                
            # Change this directory to save the plots to your desired directory
            plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/Probability of Occurrence of Compound {} and {} under the {} scenario.pdf'.format(event_1_name, event_2_name, scenario_name), dpi = 300)
            
            plt.show()
            
        #plt.close()
        
    return average_probability_of_occurrence_of_the_compound_events_considering_all_gcms_and_impact_models
 
#%%
def plot_average_frequency_of_compound_events_considering_all_gcms_and_showing_all_compound_events(
    average_frequency_of_all_compound_events, 
    compound_events_names, 
    selected_indices):
    """
    Plot a map showing the average frequency of compound events for selected scenarios.
    
    Parameters
    ----------
    average_frequency_of_all_compound_events : List of Xarray data arrays 
        The average frequency of all possible combinations of extreme events considering 
        all the gcms and scenarios.
    compound_events_names : List of Strings
        Names of the compound events.
    selected_indices : List of integers
        Indices of the selected compound events to plot.
    
    Returns
    -------
    Plot (Figure) showing the average frequency of compound events.
    """
    
    # Mapping event names to acronyms
    event_acronyms = {
        'floodedarea': 'RF',
        'driedarea': 'DR',
        'heatwavedarea': 'HW',
        'cropfailedarea': 'CF',
        'burntarea': 'WF',
        'tropicalcyclonedarea': 'TC'
    }
    
    list_of_compound_event_acronyms = []
    for compound_event in compound_events_names:
        event_1_name = event_acronyms.get(compound_event[0], compound_event[0])
        event_2_name = event_acronyms.get(compound_event[1], compound_event[1])
        compound_event_acronyms = '{} & {}'.format(event_1_name, event_2_name)
        list_of_compound_event_acronyms.append(compound_event_acronyms)
    
    selected_compound_events_names = [list_of_compound_event_acronyms[i] for i in selected_indices]
    
    # Scenarios for the main figure:
    scenarios_main = ['Present day', 'RCP6.0', 'Difference']

    # Calculate the difference between Present day and RCP6.0
    difference = calculate_difference(
        average_frequency_of_all_compound_events[1], 
        average_frequency_of_all_compound_events[3]
    )
    
    main_scenarios_data = [
        average_frequency_of_all_compound_events[1],  # Present day
        average_frequency_of_all_compound_events[3],  # RCP6.0
        difference  # Difference
    ]
    
    # Supplementary scenarios
    scenarios_supplementary = ['Early-industrial', 'RCP2.6', 'RCP8.5']
    
    supplementary_scenarios_data = [
        average_frequency_of_all_compound_events[0],  # Early-industrial
        average_frequency_of_all_compound_events[2],  # RCP2.6
        average_frequency_of_all_compound_events[4]   # RCP8.5
    ]
    
    subplot_labels = 'abcdefghijklmnopqrstuvwxyz'
    
    # Setting the projection of the map to cylindrical / Mercator
    fig_main, axs_main = plt.subplots(len(selected_indices), 3, figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})
    
    # Flatten the 2D array of axes into 1D
    axs_main = axs_main.flatten()
    
    # Setting up the discrete color bar scheme
    cmap = plt.cm.get_cmap('viridis', 12)
    norm = mpl.colors.BoundaryNorm([0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.3], cmap.N, extend='both')
    
    # Diverging color map for the difference plot
    cmap_diff = plt.cm.get_cmap('bwr')
    norm_diff = mpl.colors.TwoSlopeNorm(vmin=-0.2, vcenter=0, vmax=0.2)
    
    # Add maps to the main figure
    for col in range(3):
        for row in range(len(selected_indices)):
            index = row * 3 + col
            ax = axs_main[index]
            ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            ax.coastlines(color='dimgrey', linewidth=0.7)
            ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            ax.spines['geo'].set_visible(False)  # Remove border
            
            if col == 2:  # For the difference column
                plot = ax.imshow(
                    main_scenarios_data[col][selected_indices[row]], 
                    origin='lower', 
                    extent=[-180, 180, 90, -60], 
                    cmap=cmap_diff, 
                    norm=norm_diff
                )
            else:
                plot = ax.imshow(
                    main_scenarios_data[col][selected_indices[row]], 
                    origin='lower', 
                    extent=[-180, 180, 90, -60], 
                    cmap=cmap, 
                    norm=norm
                )
           
            if row == 0:
                ax.set_title(scenarios_main[col], fontsize=10)
            if col == 0:
                ax.text(-0.2, 0.5, selected_compound_events_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
            # Add subplot label
            ax.text(0.02, 1.05, subplot_labels[index], transform=ax.transAxes, fontsize=9, va='top', ha='left')
        
        
    fig_main.subplots_adjust(wspace=0.1, hspace=-0.5)
    cbar_ax_main = fig_main.add_axes([0.3, 0.18, 0.2, 0.015])
    cbar_main = fig_main.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax_main, orientation='horizontal', extend='max')
    cbar_main.ax.tick_params(labelsize=8)
    cbar_main.set_label(label='Probability of joint occurrence', size=8)
    
    # Color bar for the difference column
    cbar_ax_diff = fig_main.add_axes([0.6, 0.18, 0.2, 0.015])
    cbar_diff = fig_main.colorbar(mpl.cm.ScalarMappable(norm=norm_diff, cmap=cmap_diff), cax=cbar_ax_diff, orientation='horizontal', extend='both')
    cbar_diff.ax.tick_params(labelsize=8)
    cbar_diff.set_label(label='Difference in Probability of joint occurrence', size=8)

    # Save and show the main plot
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/average_frequency_of_selected_compound_extremes_under_the_present_day_and_rcp60.pdf', dpi=300)
    plt.show()
    
    # Supplementary figure
    fig_supp, axs_supp = plt.subplots(len(selected_indices), 3, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})
    axs_supp = axs_supp.flatten()
    
    # Add maps to the supplementary figure
    for col in range(3):
        for row in range(len(selected_indices)):
            index = row * 3 + col
            ax = axs_supp[index]
            ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            ax.coastlines(color='dimgrey', linewidth=0.7)
            # Check if CF is in the event name and if it's the RCP8.5 column (last column)
            if "CF" in selected_compound_events_names[row] and col == 2:
                # Apply hatching for RCP8.5 scenario for CF
                ax.add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
            else:
                ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            ax.spines['geo'].set_visible(False)  # Remove border

            plot = ax.imshow(
                supplementary_scenarios_data[col][selected_indices[row]], 
                origin='lower', 
                extent=[-180, 180, 90, -60], 
                cmap=cmap, 
                norm=norm
            )
           
            if row == 0:
                ax.set_title(scenarios_supplementary[col], fontsize=10)
            if col == 0:
                ax.text(-0.2, 0.5, selected_compound_events_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
            # Add subplot label
            ax.text(0.02, 1.05, subplot_labels[index], transform=ax.transAxes, fontsize=9, va='top', ha='left')
        
        
    fig_supp.subplots_adjust(wspace=0.1, hspace=-0.5)
    cbar_ax_supp = fig_supp.add_axes([0.4, 0.18, 0.25, 0.015])
    cbar_supp = fig_supp.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax_supp, orientation='horizontal', extend='max')
    cbar_supp.ax.tick_params(labelsize=9)
    cbar_supp.set_label(label='Probability of joint occurrence', size=8)

    # Save and show the supplementary plot
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/average_frequency_of_selected_compound_extremes_under_the_early_industrial_rcp26_and_rcp85_SUPPLEMENT.pdf', dpi=300)
    plt.show()

    return main_scenarios_data, supplementary_scenarios_data
   

#%% Function for plotting map showing the total number of years per location that experienced compound events. FOR VISUALIZATION
def plot_average_95th_quantile_of_length_of_spell_with_occurrence_of_compound_events_considering_all_gcms(average_95th_quantile_of_length_of_spell_with_occurrence_of_compound_event_per_scenario_and_gcm, event_1_name, event_2_name, gcms):
    
    """ Plot a map showing the average 95th percentile of length of spells of compound events throught the data period per location  
    
    Parameters
    ----------
    average_95th_quantile_of_length_of_spell_with_occurrence_of_compound_event_per_scenario_and_gcm : Xarray data array 
    event_1_name, event_2_name : String (Extreme Events)
    gcms: List of gcms (Driving GCM)
    time_period: String
    scenarios: List of gcms
    
    Returns
    -------
    Plot (Figure) showing the 95th percentile of length of spells of compound events throught the data period per location  
    """
    
    #average_95th_quantile_of_length_of_spell_with_occurrence_of_compound_event_per_scenario_and_gcm = [[],[],[],[],[]] #  Where order of list is early industrial, present day, rcp 2.6, rcp6.0 and rcp 8.5
    
    for scenario in range(len(average_95th_quantile_of_length_of_spell_with_occurrence_of_compound_event_per_scenario_and_gcm)):
        
        if scenario == 0:
            scenario_name = 'Early-industrial'
        if scenario == 1:
            scenario_name = 'Present-Day'
        if scenario == 2:
            scenario_name = 'RCP2.6'
        if scenario == 3:
            scenario_name = 'RCP6.0'
        if scenario == 4:
            scenario_name = 'RCP8.5'
        
        average_95th_quantile_of_length_of_spell_with_occurrence_of_compound_event_per_gcm = average_95th_quantile_of_length_of_spell_with_occurrence_of_compound_event_per_scenario_and_gcm[scenario]
        
        
        # Setting the projection of the map to cylindrical / Mercator
        fig, axs = plt.subplots(2,2, figsize=(10, 6.5), subplot_kw = {'projection': ccrs.PlateCarree()})  # , constrained_layout=True
        
        # since axs is a 2 dimensional array of geozaxes, we have to flatten it into 1D; as explained on a similar example on this page: https://kpegion.github.io/Pangeo-at-AOES/examples/multi-panel-cartopy.html
        axs=axs.flatten()
        
        # Add the background map to the plot
        #ax.stock_img()
        if scenario == 4 and event_1_name == 'Crop Failures' or scenario == 4 and event_2_name == 'Crop Failures':
            
            print('No data available for the crop failures for the selected GCM scenario under RCP8.5')
            plt.close()
                
        else: 
            
            for gcm in range(len(average_95th_quantile_of_length_of_spell_with_occurrence_of_compound_event_per_gcm)):           
                
                # Plot per GCM in a subplot
                plot = axs[gcm].imshow(average_95th_quantile_of_length_of_spell_with_occurrence_of_compound_event_per_gcm[gcm], origin = 'upper' , extent= map_extent, cmap = plt.cm.get_cmap('YlOrRd'), vmin = 1, vmax =30)
                        
                # Add the background map to the plot
                #ax.stock_img()
                      
                # Set the extent of the plotn
                axs[gcm].set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                
                # Plot the coastlines along the continents on the map
                axs[gcm].coastlines(color='dimgrey', linewidth=0.7)
                
                # Plot features: lakes, rivers and boarders
                axs[gcm].add_feature(cfeature.LAKES, alpha =0.5)
                axs[gcm].add_feature(cfeature.RIVERS)
                axs[gcm].add_feature(cfeature.OCEAN)
                axs[gcm].add_feature(cfeature.LAND, facecolor ='lightgrey')
                #ax.add_feature(cfeature.BORDERS, linestyle=':')
                
                
                # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
                xticks = [-180, -120, -60, 0, 60, 120, 180] # Longitudes
                axs[gcm].set_xticks(xticks, crs=ccrs.PlateCarree()) # 20E up to 50E
                axs[gcm].set_xticklabels(xticks, fontsize = 8)
                
                yticks = [90, 60, 30, 0, -30, -60]
                axs[gcm].set_yticks(yticks, crs=ccrs.PlateCarree()) # 20N up to 10S
                axs[gcm].set_yticklabels(yticks, fontsize = 8)
                
                lon_formatter = LongitudeFormatter()
                lat_formatter = LatitudeFormatter()
                axs[gcm].xaxis.set_major_formatter(lon_formatter)
                axs[gcm].yaxis.set_major_formatter(lat_formatter)
                
                
                # Subplot labels
                # label
                subplot_labels = ['a.','b.','c.','d.']
                axs[gcm].text(0, 1.06, subplot_labels[gcm], transform=axs[gcm].transAxes, fontsize=9, ha='left')
                # GCM
                gcm_name = gcms[gcm] # GCM 
        
                if gcm_name == 'gfdl-esm2m':
                    gcm_title = 'GFDL-ESM2M'
                if gcm_name == 'hadgem2-es':
                    gcm_title =  'HadGEM2-ES'
                if gcm_name == 'ipsl-cm5a-lr':
                    gcm_title = 'IPSL-CM5A-LR'
                if gcm_name == 'miroc5':
                    gcm_title = 'MIROC5'
               
                axs[gcm].set_title(gcm_title, fontsize = 9, loc = 'right')
               
            
            # Add the title and legend to the figure and show the figure
            fig.suptitle('95th percentile of length of spell with Compound {} and {} \n'.format(event_1_name, event_2_name),fontsize=10) #Plot title     
            
            # Discrete color bar legend
            fig.subplots_adjust(bottom=0.1, top=1.1, left=0.1, right=0.9, wspace=0.2, hspace=-0.5)
            cbar_ax = fig.add_axes([0.35, 0.2, 0.3, 0.02])
            cbar = fig.colorbar(plot, cax=cbar_ax, orientation='horizontal', extend='max', boundaries=[1, 5, 10, 15, 20, 25, 30], spacing = 'uniform', shrink = 0.5)  #Plots the legend color bar   
            cbar.ax.tick_params(labelsize=9) # Text size on legend color bar 
            cbar.set_label(label = 'Length of spell', size = 9)
        
            # Text outside the plot to display the scenario (top-center)
            fig.text(0.5,0.935,'{}'.format(scenario_name), fontsize = 8, ha = 'center', va = 'center')
                
            # Change this directory to save the plots to your desired directory
            plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/95th percentile of length of spell with occurrence of compound {} and {} under the {} scenario.pdf'.format(event_1_name, event_2_name, scenario_name), dpi = 300)
            
            plt.show()
        
        #plt.close()
        
    return average_95th_quantile_of_length_of_spell_with_occurrence_of_compound_event_per_scenario_and_gcm

#%% Function for calculating the propensity of an extreme event
def propensity(average_number_of_years_with_occurrence_of_an_extreme_event, average_number_of_years_with_occurrence_of_all_extreme_event_categories_per_scenario):
    
    """ Calculate the propensity of an extreme event such that events more frequent than the average of the other extremes have a propensity > 1 and those less frequent than the average f the other extremes have a propensity < 1, 
    and propensity = 1 means that the chosen event class perfectly matches the average extreme event frequency at that gridpoint.
    
    Parameters
    ----------
    average_number_of_years_with_occurrence_of_an_extreme_event : Xarray data array of average number of years with occurrence of a particular extreme event
    average_number_of_years_with_occurrence_of_all_extreme_event_categories_per_scenario: Xarray data array of average number of years with occurrence of all extreme events in the dataset. In this case: 6 extremes
    
    Returns
    -------
    Propensity of an extreme event per location per scenario
    """
    propensity_of_an_extreme_event = 1 + np.log(average_number_of_years_with_occurrence_of_an_extreme_event/average_number_of_years_with_occurrence_of_all_extreme_event_categories_per_scenario)  
    
    
    return propensity_of_an_extreme_event


#%% Function for calculating the cooccurrence ratio of extreme events
def cooccurrence_ratio(average_total_number_of_extreme_events_not_occurring_in_isolation_considering_all_GCMs_and_scenarios, average_total_number_of_extreme_events_occurring_in_isolation_considering_all_GCMs_and_scenarios):
    
    """ Calculate the cooccurrence_ratio of extreme events whereby "occurring in isolation" means that there is a single class of extremes occurring at a given gridbox in a given year, while "occuring not in isolation" means that there are two or more extremes occurring at a given gridbox in a given year. 
    Like propensity, the cutoff value is 1. A co-occurrence ratio > 1 means that there are more co-occurring extremes than isolated extremes, and vice-versa for a ratio < 1.
    
    Parameters
    ----------
    average_total_number_of_extreme_events_not_occurring_in_isolation_considering_all_GCMs_and_scenarios: Xarray
    average_total_number_of_extreme_events_occurring_in_isolation_considering_all_GCMs_and_scenarios: Xarray

    
    Returns
    -------
    cooccurrence_ratio of extreme events
    
    """
    cooccurrence_ratio = 1 + np.log(average_total_number_of_extreme_events_not_occurring_in_isolation_considering_all_GCMs_and_scenarios/average_total_number_of_extreme_events_occurring_in_isolation_considering_all_GCMs_and_scenarios)  
    
    
    return cooccurrence_ratio



#%% Function for plotting the propensity of an extreme event
def plot_propensity_considering_all_gcms(propensity_of_an_extreme_event_considering_different_gcms_per_scenario, event_name, gcms, scenario):
    
    """ Plot a map showing the propensity of an extreme event such that events more frequent than the average of the other extremes have a propensity > 1 and those less frequent than the average f the other extremes have a propensity < 1, 
    and propensity = 1 means that the chosen event class perfectly matches the average extreme event frequency at that gridpoint.
    
    Parameters
    ----------
    propensity_of_an_extreme_event_considering_different_gcms_per_scenario : Xarray data array of propensity of a particular extreme event considering all the gcms
    event_name : String (Extreme Event whose propensity is to be calculated)
    gcms: List (Driving GCMs)
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the propensity of an extreme event per location per scenario, with subplots for respective GCMs
    """
        
    if scenario == 0:
        scenario_name = 'Early-industrial'
    if scenario == 1:
        scenario_name = 'Present-Day'
    if scenario == 2:
        scenario_name = 'RCP2.6'
    if scenario == 3:
        scenario_name = 'RCP6.0'
    if scenario == 4:
        scenario_name = 'RCP8.5'
    
    # Setting the projection of the map to cylindrical / Mercator
    fig, axs = plt.subplots(2,2, figsize=(10, 6.5), subplot_kw = {'projection': ccrs.PlateCarree()})  # , constrained_layout=True
    
    # since axs is a 2 dimensional array of geozaxes, we have to flatten it into 1D; as explained on a similar example on this page: https://kpegion.github.io/Pangeo-at-AOES/examples/multi-panel-cartopy.html
    axs=axs.flatten()
    
    if scenario == 4:
        
        print('No data available for the crop failures for the selected GCM scenario under RCP8.5') # this is done such that to avoid propensity that would have been calculated excluding crop failures under RCP8.5
        plt.close()
    
    else:        
        # Subplots for each GCM
        for gcm in range(len(propensity_of_an_extreme_event_considering_different_gcms_per_scenario)): 
            
            # Plot the propensity of an extreme event per GCM in a subplot
            plot = axs[gcm].imshow(propensity_of_an_extreme_event_considering_different_gcms_per_scenario[gcm], origin = 'upper' , extent= map_extent, cmap = plt.cm.get_cmap('bwr'), vmin = 0, vmax =2)
                    
            # Add the background map to the plot
            #ax.stock_img()
            
            # Set the extent of the plotn
            axs[gcm].set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            
            # Plot the coastlines along the continents on the map
            axs[gcm].coastlines(color='dimgrey', linewidth=0.7)
            
            # Plot features: lakes, rivers and boarders
            axs[gcm].add_feature(cfeature.LAKES, alpha =0.5)
            axs[gcm].add_feature(cfeature.RIVERS)
            axs[gcm].add_feature(cfeature.OCEAN)
            axs[gcm].add_feature(cfeature.LAND, facecolor ='lightgrey')
            #ax.add_feature(cfeature.BORDERS, linestyle=':')
            
            
            # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
            xticks = [-180, -120, -60, 0, 60, 120, 180] # Longitudes
            axs[gcm].set_xticks(xticks, crs=ccrs.PlateCarree()) 
            axs[gcm].set_xticklabels(xticks, fontsize = 8)
            
            yticks = [90, 60, 30, 0, -30, -60]
            axs[gcm].set_yticks(yticks, crs=ccrs.PlateCarree()) 
            axs[gcm].set_yticklabels(yticks, fontsize = 8)
            
            lon_formatter = LongitudeFormatter()
            lat_formatter = LatitudeFormatter()
            axs[gcm].xaxis.set_major_formatter(lon_formatter)
            axs[gcm].yaxis.set_major_formatter(lat_formatter)
            
            
            # Subplot labels
            # label
            subplot_labels = ['a.','b.','c.','d.']
            axs[gcm].text(0, 1.06, subplot_labels[gcm], transform=axs[gcm].transAxes, fontsize=9, ha='left')
            # GCM
            gcm_name = gcms[gcm] # GCM 
    
            if gcm_name == 'gfdl-esm2m':
                gcm_title = 'GFDL-ESM2M'
            if gcm_name == 'hadgem2-es':
                gcm_title =  'HadGEM2-ES'
            if gcm_name == 'ipsl-cm5a-lr':
                gcm_title = 'IPSL-CM5A-LR'
            if gcm_name == 'miroc5':
                gcm_title = 'MIROC5'
           
            axs[gcm].set_title(gcm_title, fontsize = 9, loc = 'right')
           
       
        # Add the title and legend to the figure and show the figure
        fig.suptitle('Propensity of {} \n'.format(event_name),fontsize=10) #Plot title     
        
        # Discrete color bar legend
        fig.subplots_adjust(bottom=0.1, top=1.1, left=0.1, right=0.9, wspace=0.2, hspace=-0.5)
        cbar_ax = fig.add_axes([0.35, 0.2, 0.3, 0.02])
        cbar = fig.colorbar(plot, cax=cbar_ax, orientation='horizontal', extend='both', ticks=[0, 0.5, 1, 1.5, 2], shrink = 0.5)  #Plots the legend color bar   
        cbar.ax.tick_params(labelsize=9) # Text size on legend color bar 
        cbar.set_label(label = 'Propensity', size = 9)
    
        # Text outside the plot to display the scenario (top-center)
        fig.text(0.5,0.935,'{}'.format(scenario_name), fontsize = 8, ha = 'center', va = 'center')
    
        #plt.tight_layout()
        
        # Change this directory to save the plots to your desired directory
        plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Propensity of {} under the {} scenario considering all impact models and their respective driving GCMs.pdf'.format(event_name, scenario_name), dpi = 300)
        
        plt.show()
    
    #plt.close()
    
    return propensity_of_an_extreme_event_considering_different_gcms_per_scenario


#%% Function for plotting the propensity of an extreme event
def plot_propensity_considering_all_gcms_in_one_plot(propensity_of_an_extreme_event_considering_all_GCMs, event_name, scenario):
    
    """ Plot a map showing the propensity of an extreme event such that events more frequent than the average of the other extremes have a propensity > 1 and those less frequent than the average f the other extremes have a propensity < 1, 
    and propensity = 1 means that the chosen event class perfectly matches the average extreme event frequency at that gridpoint.
    
    Parameters
    ----------
    propensity_of_an_extreme_event_considering_all_GCMs : Xarray data array of propensity of a particular extreme event considering all the gcms
    event_name : String (Extreme Event whose propensity is to be calculated)
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the propensity of an extreme event per location per scenario
    """
        
    if scenario == 0:
        scenario_name = 'Early-industrial'
    if scenario == 1:
        scenario_name = 'Present-Day'
    if scenario == 2:
        scenario_name = 'RCP2.6'
    if scenario == 3:
        scenario_name = 'RCP6.0'
    if scenario == 4:
        scenario_name = 'RCP8.5'
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    if scenario == 4:
             
        print('No data available for the crop failures for the selected GCM scenario under RCP8.5') # this is done such that to avoid propensity that would have been calculated excluding crop failures under RCP8.5
        plt.close()
        
    else:        
    
        # Add the background map to the plot
        #ax.stock_img()
        
        # Set the extent of the plot, in this case the East African Region
        ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
        
        # Plot the coastlines along the continents on the map
        ax.coastlines(color='dimgrey', linewidth=0.7)
        
        # Plot features: lakes, rivers and boarders
        ax.add_feature(cfeature.LAKES, alpha =0.5)
        ax.add_feature(cfeature.RIVERS)
        ax.add_feature(cfeature.OCEAN)
        ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
        #ax.add_feature(cfeature.BORDERS, linestyle=':')
        
         # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
        ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
        ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
        lon_formatter = LongitudeFormatter()
        lat_formatter = LatitudeFormatter()
        ax.xaxis.set_major_formatter(lon_formatter)
        ax.yaxis.set_major_formatter(lat_formatter)  
        
        
        # Plot the gridlines for the coordinate system on the map
        #grid= ax.gridlines(draw_labels = True, dms = True )
        #grid.top_labels = False #Removes the grid labels from the top of the plot
        #grid.right_labels= False #Removes the grid labels from the right of the plot
        
        # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
        plt.imshow(propensity_of_an_extreme_event_considering_all_GCMs, origin = 'upper' , extent=map_extent, cmap = plt.cm.get_cmap('bwr'), vmin = 0, vmax =2)
        
        # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left) 'Propensity of {} \n'.format(event_name),fontsize=10
        #plt.gcf().text(0.25,0.85,'Propensity of {} \n'.format(time_period, scenario), fontsize = 8)
        #plt.gcf().text(0.15,0.03,'{}'.format(gcm), fontsize= 8)
        
        # Add the title and legend to the figure and show the figure
        plt.title('Propensity of {} under {}\n'.format(event_name, scenario_name),fontsize=10) #Plot title
        
        # discrete color bar legend
        #bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
        plt.colorbar( orientation = 'horizontal', extend='both', ticks=[0, 0.5, 1, 1.5, 2], shrink = 0.5).set_label(label = 'Propensity', size = 9) #Plots the legend color bar
        plt.clim(0,2)
        plt.xticks(fontsize=8) # color and size of longitude labels
        plt.yticks(fontsize=8) # color and size of latitude labels
        
        
        plt.tight_layout()
                
        # Change this directory to save the plots to your desired directory
        plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Propensity of {} under the {} scenario considering all GCMs.pdf'.format(event_name, scenario_name), dpi = 300)
                 
        plt.show()
            
        plt.close()
    
    
    return propensity_of_an_extreme_event_considering_all_GCMs




#%% Function to calculate the difference between Present day and RCP6.0
def calculate_difference(present, rcp60):
    difference = [rcp60[i] - present[i] if present[i] is not None and rcp60[i] is not None else None for i in range(len(present))]
    return difference



#%% Function for plotting the propensity of extreme events considering all GCMs and showing all extremes
def plot_propensity_considering_all_gcms_and_showing_all_extremes(summary_of_propensity_of_an_extreme_event_considering_all_GCMs_and_scenarios, list_of_extreme_events):
    
    list_of_extreme_event_names = []
    for extreme_event in list_of_extreme_events:
        if extreme_event == 'floodedarea':
            event_name = 'River Floods'
        if extreme_event == 'driedarea':
            event_name = 'Droughts'
        if extreme_event == 'heatwavedarea':
            event_name = 'Heatwaves'
        if extreme_event == 'cropfailedarea':
            event_name = 'Crop Failures'
        if extreme_event == 'burntarea':
            event_name = 'Wildfires'
        if extreme_event == 'tropicalcyclonedarea':
            event_name = 'Tropical Cyclones'
        list_of_extreme_event_names.append(event_name)
   
    # Scenarios for the first figure:
    scenarios_main = ['Present day', 'RCP6.0', 'Difference']
    
    # Scenarios for the supplementary figure:
    scenarios_supplementary = ['Early-industrial', 'RCP2.6', 'RCP8.5']
    
    # Calculate the difference between Present day and RCP6.0
    difference = calculate_difference(summary_of_propensity_of_an_extreme_event_considering_all_GCMs_and_scenarios[1], summary_of_propensity_of_an_extreme_event_considering_all_GCMs_and_scenarios[3])
    
    # Add the difference to the main scenarios data
    main_scenarios_data = [summary_of_propensity_of_an_extreme_event_considering_all_GCMs_and_scenarios[1], summary_of_propensity_of_an_extreme_event_considering_all_GCMs_and_scenarios[3], difference]
    
    supplementary_scenarios_data = [summary_of_propensity_of_an_extreme_event_considering_all_GCMs_and_scenarios[0], summary_of_propensity_of_an_extreme_event_considering_all_GCMs_and_scenarios[2], summary_of_propensity_of_an_extreme_event_considering_all_GCMs_and_scenarios[4]]
    
    # Setting the projection of the map to cylindrical / Mercator
    fig_main, axs_main = plt.subplots(6, 3, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})
    
    # Flattening the 2D arrays of axes into 1D
    axs_main = axs_main.flatten()
    
    
    # Setting up the discrete color bar schemes
    cmap = plt.cm.get_cmap('bwr')
    norm = mpl.colors.Normalize(vmin=0, vmax=2)
    
    # Diverging color map for the difference plot
    cmap_diff = plt.cm.get_cmap('PRGn')
    norm_diff = mpl.colors.TwoSlopeNorm(vmin=-2, vcenter=0, vmax=2)
    
    # Function to add maps to a figure
    def add_maps_to_figure(fig, axs, scenarios, cmap, norm, data):
        subplot_labels = 'abcdefghijklmnopqrstuvwx'
        
        for col in range(len(scenarios)):
            for row in range(6):
                index = row * 3 + col
                ax = axs[index]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                ax.spines['geo'].set_visible(False)  # Remove border
                #plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap, norm=norm)
                if col == 2:  # For the difference column
                    plot = ax.imshow(
                        data[col][row], 
                        origin='lower', 
                        extent=[-180, 180, 90, -60], 
                        cmap=cmap_diff, 
                        norm=norm_diff
                    )
                else:
                    plot = ax.imshow(
                        data[col][row], 
                        origin='lower', 
                        extent=[-180, 180, 90, -60], 
                        cmap=cmap, 
                        norm=norm
                    )
        

                
                ax.text(0.02, 0.95, subplot_labels[index], transform=ax.transAxes, fontsize=9, ha='left')
                if row == 0:
                    ax.set_title(scenarios[col], fontsize=10)
                if col == 0:
                    ax.text(-0.2, 0.5, list_of_extreme_event_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
        
        fig.subplots_adjust(wspace=0.1, hspace=0.1)
        cbar_ax = fig.add_axes([0.3, 0.05, 0.2, 0.015])
        cbar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax, orientation='horizontal', extend='max')
        cbar.ax.tick_params(labelsize=9)
        cbar.set_label(label='Propensity', size=9)
        
        # Add a colorbar specifically for the difference plot
        cbar_ax_diff = fig.add_axes([0.58, 0.05, 0.2, 0.015])
        cbar_diff = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_diff, cmap=cmap_diff), cax=cbar_ax_diff, orientation='horizontal', extend='both')
        cbar_diff.ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'{x:.1f}'))
        cbar_diff.ax.tick_params(labelsize=9)
        cbar_diff.set_label(label='Difference in Propensity', size=9)    
        
    
    add_maps_to_figure(fig_main, axs_main, scenarios_main, cmap, norm, main_scenarios_data)
    fig_main.suptitle('Propensity of extreme events - Main Scenarios', fontsize=12)
    fig_main.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Propensity_of_extremes_main_scenarios.pdf', dpi=300)
    plt.show()
    plt.close(fig_main)  # Close the figure to free memory
    
    
    fig_supp, axs_supp = plt.subplots(6, 3, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})
    axs_supp = axs_supp.flatten()
    def add_maps_to_supplementary_figure(fig, axs, scenarios, cmap, norm, data):
        subplot_labels = 'abcdefghijklmnopqrstuvwx'
        
        for col in range(len(scenarios)):
            for row in range(6):
                index = row * 3 + col
                ax = axs[index]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                #ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                if row == 5 and col == 2:
                    # Apply hatching for RCP8.5 scenario for CF
                    ax.add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
                else:
                    ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                ax.spines['geo'].set_visible(False)  # Remove border
                if col == 2 and row == 5: # Avoid propensity for crop failures at RCP8.5 since there was no data for RCP8.5
                    # If data is None, leave the subplot empty
                    ax.axis('off')
                else:
                    plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap, norm=norm)
                ax.text(0.02, 0.95, subplot_labels[index], transform=ax.transAxes, fontsize=9, ha='left')
                if row == 0:
                    ax.set_title(scenarios[col], fontsize=10)
                if col == 0:
                    ax.text(-0.2, 0.5, list_of_extreme_event_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
        
        fig.subplots_adjust(wspace=0.1, hspace=0.1)
        cbar_ax = fig.add_axes([0.4, 0.05, 0.25, 0.015])
        cbar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax, orientation='horizontal', extend='max')
        cbar.ax.tick_params(labelsize=9)
        cbar.set_label(label='Propensity', size=9)
    add_maps_to_supplementary_figure(fig_supp, axs_supp, scenarios_supplementary, cmap, norm, supplementary_scenarios_data)
    fig_supp.suptitle('Propensity of extreme events - Supplementary Scenarios', fontsize=12)
    fig_supp.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Propensity_of_extremes_supplementary_scenarios.pdf', dpi=300)
    plt.show()
    plt.close(fig_supp)  # Close the figure to free memory

    return main_scenarios_data




#%% Function for plotting the length of spell of an extreme event
def calculate_difference_length_of_spell_individual_extremes(present, rcp60):
    difference = [
        np.where(np.isnan(present[i]) & ~np.isnan(rcp60[i]), rcp60[i], rcp60[i] - present[i])
        if present[i] is not None and rcp60[i] is not None
        else None
        for i in range(len(present))
    ]
    return difference

def plot_quantile_95th_of_length_of_spell_with_occurrence_considering_all_gcms_and_showing_all_extremes(average_length_of_spells_for_all_extreme_events, list_of_extreme_events):
        

    list_of_extreme_event_names = []
    for extreme_event in list_of_extreme_events:
        
        if extreme_event == 'floodedarea':
            event_name = 'River Floods'
        if extreme_event == 'driedarea':
            event_name = 'Droughts'
        if extreme_event == 'heatwavedarea':
            event_name = 'Heatwaves'
        if extreme_event == 'cropfailedarea':
            event_name = 'Crop Failures'
        if extreme_event =='burntarea':
            event_name = 'Wildfires'
        if extreme_event == 'tropicalcyclonedarea':
            event_name ='Tropical Cyclones'
        list_of_extreme_event_names.append(event_name)

   
    # Scenarios:
    #scenarios = ['Early-industrial', 'Present day', 'RCP2.6', 'RCP6.0', 'RCP8.5']
    
    # Scenarios for the main figure:
    scenarios_main = ['Present day', 'RCP6.0', 'Difference']
    # Scenarios for the supplementary figure:
    scenarios_supplementary = ['Early-industrial', 'RCP2.6', 'RCP8.5']
    
    # Calculate the difference between Present day and RCP6.0
    difference = calculate_difference_length_of_spell_individual_extremes(
        average_length_of_spells_for_all_extreme_events[1], 
        average_length_of_spells_for_all_extreme_events[3]
    )
    
    # Data for the main scenarios
    main_scenarios_data = [
        average_length_of_spells_for_all_extreme_events[1],  # Present day
        average_length_of_spells_for_all_extreme_events[3],  # RCP6.0
        difference  # Difference
    ]
    
    # Data for the supplementary scenarios
    supplementary_scenarios_data = [
        average_length_of_spells_for_all_extreme_events[0],  # Early-industrial
        average_length_of_spells_for_all_extreme_events[2],  # RCP2.6
        average_length_of_spells_for_all_extreme_events[4]   # RCP8.5
    ]
    
    # Subplot labels
    subplot_labels = [
        'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',
        'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x'
    ]

    # Plot the main figure for main scenarios
    fig_main, axs_main = plt.subplots(6, 3, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})
    
    # Flatten the 2D array of axes into 1D
    axs_main = axs_main.flatten()
    
    # Setting up the discrete color bar scheme
    cmap = plt.cm.get_cmap('YlOrRd')
    norm = mpl.colors.BoundaryNorm([1, 5, 10, 15, 20, 25, 30], cmap.N, extend='both')
    
    # Diverging color map for the difference plot
    cmap_diff = plt.cm.get_cmap('bwr')
    norm_diff = mpl.colors.TwoSlopeNorm(vmin=-10, vcenter=0, vmax=10)
    
    # Add maps to the main figure
    for col in range(3):
        for row in range(6):
            index = row * 3 + col
            ax = axs_main[index]
            ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            ax.coastlines(color='dimgrey', linewidth=0.7)
            ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            ax.spines['geo'].set_visible(False)  # Remove border

            if col == 2:  # For the difference column
                plot = ax.imshow(
                    main_scenarios_data[col][row], 
                    origin='lower', 
                    extent=[-180, 180, 90, -60], 
                    cmap=cmap_diff, 
                    norm=norm_diff
                )
            else:
                plot = ax.imshow(
                    main_scenarios_data[col][row], 
                    origin='lower', 
                    extent=[-180, 180, 90, -60], 
                    cmap=cmap, 
                    norm=norm
                )

            # Add subplot labels
            ax.text(0.02, 0.95, subplot_labels[index], transform=ax.transAxes, fontsize=9, ha='left')
            
            # Add row and column labels
            if row == 0:
                ax.set_title(scenarios_main[col], fontsize=10)
            if col == 0:
                ax.text(-0.2, 0.5, list_of_extreme_event_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
    
    fig_main.subplots_adjust(wspace=0.1, hspace=0.1)
    cbar_ax_main = fig_main.add_axes([0.25, 0.05, 0.25, 0.015])
    cbar_main = fig_main.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax_main, orientation='horizontal', extend='max')
    cbar_main.ax.tick_params(labelsize=9)
    cbar_main.set_label(label='Years', size=9)

    # Add a colorbar specifically for the difference plot
    cbar_ax_diff = fig_main.add_axes([0.60, 0.05, 0.25, 0.015])
    cbar_diff = fig_main.colorbar(mpl.cm.ScalarMappable(norm=norm_diff, cmap=cmap_diff), cax=cbar_ax_diff, orientation='horizontal', extend='both')
    cbar_diff.ax.tick_params(labelsize=9)
    cbar_diff.set_label(label='Difference (Years)', size=9)
    
    # Save and show the main plot
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/95th_percentile_of_length_of_spell_for_all_extreme_events_main.pdf', dpi=300)
    plt.show()
    
    # Plot the supplementary figure for supplementary scenarios
    fig_supp, axs_supp = plt.subplots(6, 3, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})
    axs_supp = axs_supp.flatten()
    
    # Add maps to the supplementary figure
    for col in range(3):
        for row in range(6):
            index = row * 3 + col
            ax = axs_supp[index]
            ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            ax.coastlines(color='dimgrey', linewidth=0.7)
            #ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            if row == 5 and col == 2:
                # Apply hatching for RCP8.5 scenario for CF
                ax.add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
            else:
                ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            ax.spines['geo'].set_visible(False)  # Remove border

            plot = ax.imshow(
                supplementary_scenarios_data[col][row], 
                origin='lower', 
                extent=[-180, 180, 90, -60], 
                cmap=cmap, 
                norm=norm
            )
            
            # Add subplot labels
            ax.text(0.02, 0.95, subplot_labels[index], transform=ax.transAxes, fontsize=9, ha='left')
            
            # Add row and column labels
            if row == 0:
                ax.set_title(scenarios_supplementary[col], fontsize=10)
            if col == 0:
                ax.text(-0.2, 0.5, list_of_extreme_event_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
    
    fig_supp.subplots_adjust(wspace=0.1, hspace=0.1)
    cbar_ax_supp = fig_supp.add_axes([0.4, 0.05, 0.25, 0.015])
    cbar_supp = fig_supp.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax_supp, orientation='horizontal', extend='max')
    cbar_supp.ax.tick_params(labelsize=9)
    cbar_supp.set_label(label='Years', size=9)

    # Save and show the supplementary plot
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/95th_percentile_of_length_of_spell_for_all_extreme_events_supplementary.pdf', dpi=300)
    plt.show()

    return main_scenarios_data, supplementary_scenarios_data
    

#%% Function for plotting the length of spell of compound evennts
def plot_quantile_95th_of_length_of_spell_with_compound_event_occurrence_considering_all_gcms_and_showing_all_extremes(average_length_of_spells_for_all_compound_extreme_events, compound_events_names):
    
    """ Plot a map showing the 95th quantile for length of spell of compound events such that events that gridpoint.
    
    Parameters
    ----------
    average_length_of_spells_for_all_compound_extreme_events : List of Xarray data arrays of the 95th quantile for length of spell of all the possible comnbinations of six extreme events considering all the gcms and scenarios
    compound_events_names: the order of the pairs
    
    Returns
    -------
    Plot (Figure) showing the the 95th quantile for length of spell of compound events
    """
    

    list_of_compound_event_acronyms = []  
    for compound_event in compound_events_names:
        # renaming event 1 with an acronym for plotting purposes
        if compound_event[0] == 'floodedarea':
            event_1_name = 'RF'
        if compound_event[0] == 'driedarea':
            event_1_name = 'DR'
        if compound_event[0] == 'heatwavedarea':
            event_1_name = 'HW'
        if compound_event[0] == 'cropfailedarea':
            event_1_name = 'CF'
        if compound_event[0] =='burntarea':
            event_1_name = 'WF'
        if compound_event[0] == 'tropicalcyclonedarea':
            event_1_name ='TC'
        
        # renaming event 2 with an acronym for plotting purposes
        if compound_event[1] == 'floodedarea':
            event_2_name = 'RF'
        if compound_event[1] == 'driedarea':
            event_2_name = 'DR'
        if compound_event[1] == 'heatwavedarea':
            event_2_name = 'HW'
        if compound_event[1] == 'cropfailedarea':
            event_2_name = 'CF'
        if compound_event[1] =='burntarea':
            event_2_name = 'WF'
        if compound_event[1] == 'tropicalcyclonedarea':
            event_2_name ='TC'
        
        compound_event_acronyms = '{} & {}'.format(event_1_name, event_2_name)
        list_of_compound_event_acronyms.append(compound_event_acronyms)
        
   
    # Scenarios:
    scenarios = ['Early-industrial', 'Present day', 'RCP2.6', 'RCP6.0', 'RCP8.5']

    # Setting the projection of the map to cylindrical / Mercator
    fig, axs = plt.subplots(15,5, figsize=(12, 30), subplot_kw = {'projection': ccrs.PlateCarree()})  # , constrained_layout=True
    
    # since axs is a 2 dimensional array of geozaxes, we have to flatten it into 1D; as explained on a similar example on this page: https://kpegion.github.io/Pangeo-at-AOES/examples/multi-panel-cartopy.html
    #axs=axs.flatten()
    
    # Setting up the discrete color bar scheme
    #cmap = mpl.cm.bwr
    cmap = plt.cm.get_cmap('YlOrRd')
    bounds = [1, 5, 10, 15, 20, 25, 30]
    norm = mpl.colors.BoundaryNorm(bounds, cmap.N, extend='both')
    #cmap = plt.cm.get_cmap('YlOrRd')
    #norm = mpl.colors.Normalize(vmin=1, vmax=30)
    
    
# =============================================================================
#     # Subplot labels
#     subplot_labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',
#                   'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x']  # List of labels for each subplot
# 
# =============================================================================
    # Add row labels
    for row in range(15):
        axs[row, 0].text(-0.2, 0.5, list_of_compound_event_acronyms[row], transform=axs[row, 0].transAxes,
                         fontsize=10, ha='center', va='center', rotation=90)
        
        
    # Subplots for each extreme event (rows) and scenario (columns)
    
    # Iterate through each column
    for col in range(5):
        # Iterate through each row
        for row in range(15):
            # Get the index of the sublist within the main list
            sublist_idx = col
            
            # Get the index of the xarray within the sublist
            xarray_idx = row
            
            # Get the index of the subplot label
            label_index = row * 4 + col
            
            # Check if the sublist index is within the range of the nested list
            if sublist_idx < len(average_length_of_spells_for_all_compound_extreme_events):
                # Get the sublist of xarrays
                sublist = average_length_of_spells_for_all_compound_extreme_events[sublist_idx]
                
                # Check if the xarray index is within the range of the sublist
                if xarray_idx < len(sublist):
                    # Plot the map for the current xarray in the corresponding subplot
                    # Set the extent of the plotn
                    axs[row, col].set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                    
                    # Plot the coastlines along the continents on the map
                    axs[row, col].coastlines(color='dimgrey', linewidth=0.7)
                    
                    # Plot features: lakes, rivers and boarders
                    #axs[row, col].add_feature(cfeature.LAKES, alpha =0.5)
                    #axs[row, col].add_feature(cfeature.RIVERS)
                    #axs[row, col].add_feature(cfeature.OCEAN)
                    #ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                    # Check if CF is in the event name and if it's the RCP8.5 column (last column)
                    if "CF" in list_of_compound_event_acronyms[row] and col == 4:
                        # Apply hatching for RCP8.5 scenario for CF
                        axs[row, col].add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
                    else:
                        axs[row, col].add_feature(cfeature.LAND, facecolor='lightgrey')
                    #ax.add_feature(cfeature.BORDERS, linestyle=':')
                                                        

                    # Remove borders
                    axs[row, col].set_frame_on(False)
                    
                    
                    plot = axs[row, col].imshow(sublist[xarray_idx], origin = 'upper' , extent= map_extent, cmap = cmap, norm=norm)  # Assuming sublist contains arrays that can be plotted with imshow
                    #plot = axs[row, col].imshow(sublist[xarray_idx], origin = 'upper' , extent= map_extent, cmap = plt.cm.get_cmap('bwr'), vmin = 0, vmax =2)  # Assuming sublist contains arrays that can be plotted with imshow
                    
# =============================================================================
#                     # Subplot labels
#                     # Add subplot label
#                     axs[row, col].text(0.02, 0.95, subplot_labels[label_index], transform=axs[row, col].transAxes, fontsize=9, ha='left')
#     
# =============================================================================
                    # Add row and column labels
                    if row == 0:
                        axs[row, col].set_title(scenarios[col], fontsize=10)
                    
    # Add the title and legend to the figure and show the figure
    fig.suptitle('95th percentile of length of spell of compound events \n'.format(event_name),fontsize=10) #Plot title     
    
    # Discrete color bar legend
    fig.subplots_adjust(wspace=0.2)
    #cbar_ax = fig.add_axes([0.22, 0.05, 0.6, 0.015])
    cbar_ax = fig.add_axes([0.4, 0.05, 0.25, 0.015])
    
    cbar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax, orientation='horizontal', extend='max', shrink = 0.3)  
    #cbar = fig.colorbar(plot, cax=cbar_ax, orientation='horizontal', extend='both', ticks=[0, 0.5, 1, 1.5, 2], shrink = 0.5)  #Plots the legend color bar   
    cbar.set_ticks([1, 5, 10, 15, 20, 25, 30])
    cbar.ax.tick_params(labelsize=9) # Text size on legend color bar 
    cbar.set_label(label = 'Years', size = 9)

# =============================================================================
#     # Text outside the plot to display the scenario (top-center)
#     fig.text(0.5,0.935,'{}'.format(scenario_name), fontsize = 8, ha = 'center', va = 'center')
# =============================================================================

    #plt.tight_layout()
    
    # Change this directory to save the plots to your desired directory
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/95th percentile of length of spell for compound extremes under the different scenarios considering all impact models and their respective driving GCMs.pdf', dpi = 300)
    
    plt.show()
    
    #plt.close()
    
    return average_length_of_spells_for_all_compound_extreme_events




#%%
def calculate_difference_length_of_spell(present, rcp60):
    difference = [
        np.where(np.isnan(present[i]) & ~np.isnan(rcp60[i]), rcp60[i], rcp60[i] - present[i])
        if present[i] is not None and rcp60[i] is not None
        else None
        for i in range(len(present))
    ]
    return difference

#%%
def plot_quantile_95th_of_length_of_spell_with_selected_compound_event_occurrence_considering_all_gcms_and_showing_all_extremes(
        average_length_of_spells_for_all_compound_extreme_events, 
        compound_events_names, 
        selected_indices):
    """
    Plot a map showing the 95th quantile for the length of spell of compound events for selected scenarios.
    
    Parameters
    ----------
    average_length_of_spells_for_all_compound_extreme_events : List of Xarray data arrays 
        The 95th quantile for length of spell of all possible combinations of six extreme events considering 
        all the gcms and scenarios.
    compound_events_names : List of Strings
        Names of the extreme events.
    selected_indices : List of integers
        Indices of the selected compound events to plot.
    
    Returns
    -------
    Plot (Figure) showing the 95th quantile for length of spell of compound events.
    """
    
    # Mapping event names to acronyms
    event_acronyms = {
        'floodedarea': 'RF',
        'driedarea': 'DR',
        'heatwavedarea': 'HW',
        'cropfailedarea': 'CF',
        'burntarea': 'WF',
        'tropicalcyclonedarea': 'TC'
    }
    
    list_of_compound_event_acronyms = []
    for compound_event in compound_events_names:
        event_1_name = event_acronyms.get(compound_event[0], compound_event[0])
        event_2_name = event_acronyms.get(compound_event[1], compound_event[1])
        compound_event_acronyms = '{} & {}'.format(event_1_name, event_2_name)
        list_of_compound_event_acronyms.append(compound_event_acronyms)
    
    selected_compound_events_names = [list_of_compound_event_acronyms[i] for i in selected_indices]
    
    # Scenarios for the main figure:
    scenarios_main = ['Present day', 'RCP6.0', 'Difference']

    # Calculate the difference between Present day and RCP6.0
    difference = calculate_difference_length_of_spell(
        average_length_of_spells_for_all_compound_extreme_events[1], 
        average_length_of_spells_for_all_compound_extreme_events[3]
    )
    
    main_scenarios_data = [
        average_length_of_spells_for_all_compound_extreme_events[1],  # Present day
        average_length_of_spells_for_all_compound_extreme_events[3],  # RCP6.0
        difference  # Difference
    ]
    
    # Supplementary scenarios
    scenarios_supplementary = ['Early-industrial', 'RCP2.6', 'RCP8.5']
    
    supplementary_scenarios_data = [
        average_length_of_spells_for_all_compound_extreme_events[0],  # Early-industrial
        average_length_of_spells_for_all_compound_extreme_events[2],  # RCP2.6
        average_length_of_spells_for_all_compound_extreme_events[4]   # RCP8.5
    ]
    
    subplot_labels = 'abcdefghijklmnopqrstuvwxyz'
    
    # Setting the projection of the map to cylindrical / Mercator
    fig_main, axs_main = plt.subplots(len(selected_indices), 3, figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})
    
    # Flatten the 2D array of axes into 1D
    axs_main = axs_main.flatten()
    
    # Setting up the discrete color bar scheme
    cmap = plt.cm.get_cmap('YlOrRd')
    norm = mpl.colors.BoundaryNorm([1, 5, 10, 15, 20, 25, 30], cmap.N, extend='both')
    
    # Diverging color map for the difference plot
    cmap_diff = plt.cm.get_cmap('bwr')
    norm_diff = mpl.colors.TwoSlopeNorm(vmin=-10, vcenter=0, vmax=10)
    
    # Add maps to the main figure
    for col in range(3):
        for row in range(len(selected_indices)):
            index = row * 3 + col
            ax = axs_main[index]
            ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            ax.coastlines(color='dimgrey', linewidth=0.7)
            ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            ax.spines['geo'].set_visible(False)  # Remove border
            
            if col == 2:  # For the difference column
                plot = ax.imshow(
                    main_scenarios_data[col][selected_indices[row]], 
                    origin='lower', 
                    extent=[-180, 180, 90, -60], 
                    cmap=cmap_diff, 
                    norm=norm_diff
                )
            else:
                plot = ax.imshow(
                    main_scenarios_data[col][selected_indices[row]], 
                    origin='lower', 
                    extent=[-180, 180, 90, -60], 
                    cmap=cmap, 
                    norm=norm
                )
           
            if row == 0:
                ax.set_title(scenarios_main[col], fontsize=10)
            if col == 0:
                ax.text(-0.2, 0.5, selected_compound_events_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
            # Add subplot label
            ax.text(0.02, 1.05, subplot_labels[index], transform=ax.transAxes, fontsize=9, va='top', ha='left')
        
        
    fig_main.subplots_adjust(wspace=0.1, hspace=-0.5)
    cbar_ax_main = fig_main.add_axes([0.3, 0.18, 0.2, 0.015])
    cbar_main = fig_main.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax_main, orientation='horizontal', extend='max')
    cbar_main.ax.tick_params(labelsize=9)
    cbar_main.set_label(label='Years', size=9)
    
    # Color bar for the difference column
    cbar_ax_diff = fig_main.add_axes([0.6, 0.18, 0.2, 0.015])
    cbar_diff = fig_main.colorbar(mpl.cm.ScalarMappable(norm=norm_diff, cmap=cmap_diff), cax=cbar_ax_diff, orientation='horizontal', extend='both')
    cbar_diff.ax.tick_params(labelsize=9)
    cbar_diff.set_label(label='Difference (Years)', size=9)

    # Save and show the main plot
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/95th_percentile_of_length_of_spell_for_selected_compound_extremes_under_the_present_day_and_rcp60.pdf', dpi=300)
    plt.show()
    
    # Supplementary figure
    fig_supp, axs_supp = plt.subplots(len(selected_indices), 3, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})
    axs_supp = axs_supp.flatten()
    
    # Add maps to the supplementary figure
    for col in range(3):
        for row in range(len(selected_indices)):
            index = row * 3 + col
            ax = axs_supp[index]
            ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            ax.coastlines(color='dimgrey', linewidth=0.7)
            #ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            # Check if CF is in the event name and if it's the RCP8.5 column (last column)
            if "CF" in selected_compound_events_names[row] and col == 2:
                # Apply hatching for RCP8.5 scenario for CF
                ax.add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
            else:
                ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            ax.spines['geo'].set_visible(False)  # Remove border

            plot = ax.imshow(
                supplementary_scenarios_data[col][selected_indices[row]], 
                origin='lower', 
                extent=[-180, 180, 90, -60], 
                cmap=cmap, 
                norm=norm
            )
           
            if row == 0:
                ax.set_title(scenarios_supplementary[col], fontsize=10)
            if col == 0:
                ax.text(-0.2, 0.5, selected_compound_events_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
            # Add subplot label
            ax.text(0.02, 1.05, subplot_labels[index], transform=ax.transAxes, fontsize=9, va='top', ha='left')
        
        
    fig_supp.subplots_adjust(wspace=0.1, hspace=-0.5)
    cbar_ax_supp = fig_supp.add_axes([0.4, 0.18, 0.25, 0.015])
    cbar_supp = fig_supp.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax_supp, orientation='horizontal', extend='max')
    cbar_supp.ax.tick_params(labelsize=9)
    cbar_supp.set_label(label='Years', size=9)

    # Save and show the supplementary plot
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/95th_percentile_of_length_of_spell_for_selected_compound_extremes_under_the_early_industrial_rcp26_andrcp85_SUPPLEMENT.pdf', dpi=300)
    plt.show()

    return main_scenarios_data, supplementary_scenarios_data

#%%
def plot_quantile_95th_of_length_of_spell_with_selected_compound_event_occurrence_considering_all_gcms_and_showing_all_extremes_second_plot(
        average_length_of_spells_for_all_compound_extreme_events, 
        compound_events_names, 
        selected_indices):
    """
    Plot a map showing the 95th quantile for the length of spell of compound events for selected scenarios.
    
    Parameters
    ----------
    average_length_of_spells_for_all_compound_extreme_events : List of Xarray data arrays 
        The 95th quantile for length of spell of all possible combinations of six extreme events considering 
        all the gcms and scenarios.
    compound_events_names : List of Strings
        Names of the extreme events.
    selected_indices : List of integers
        Indices of the selected compound events to plot.
    
    Returns
    -------
    Plot (Figure) showing the 95th quantile for length of spell of compound events.
    """
    
    # Mapping event names to acronyms
    event_acronyms = {
        'floodedarea': 'RF',
        'driedarea': 'DR',
        'heatwavedarea': 'HW',
        'cropfailedarea': 'CF',
        'burntarea': 'WF',
        'tropicalcyclonedarea': 'TC'
    }
    
    list_of_compound_event_acronyms = []
    for compound_event in compound_events_names:
        event_1_name = event_acronyms.get(compound_event[0], compound_event[0])
        event_2_name = event_acronyms.get(compound_event[1], compound_event[1])
        compound_event_acronyms = '{} & {}'.format(event_1_name, event_2_name)
        list_of_compound_event_acronyms.append(compound_event_acronyms)
    
    selected_compound_events_names = [list_of_compound_event_acronyms[i] for i in selected_indices]
    
    # Scenarios for the main figure:
    scenarios_main = ['Present day', 'RCP6.0', 'Difference']

    # Calculate the difference between Present day and RCP6.0
    difference = calculate_difference_length_of_spell(
        average_length_of_spells_for_all_compound_extreme_events[1], 
        average_length_of_spells_for_all_compound_extreme_events[3]
    )
    
    main_scenarios_data = [
        average_length_of_spells_for_all_compound_extreme_events[1],  # Present day
        average_length_of_spells_for_all_compound_extreme_events[3],  # RCP6.0
        difference  # Difference
    ]
    
    # Supplementary scenarios
    scenarios_supplementary = ['Early-industrial', 'RCP2.6', 'RCP8.5']
    
    supplementary_scenarios_data = [
        average_length_of_spells_for_all_compound_extreme_events[0],  # Early-industrial
        average_length_of_spells_for_all_compound_extreme_events[2],  # RCP2.6
        average_length_of_spells_for_all_compound_extreme_events[4]   # RCP8.5
    ]
    
    subplot_labels = 'abcdefghijklmnopqrstuvwxyz'
    
    # Setting the projection of the map to cylindrical / Mercator
    fig_main, axs_main = plt.subplots(len(selected_indices), 3, figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})
    
    # Flatten the 2D array of axes into 1D
    axs_main = axs_main.flatten()
    
    # Setting up the discrete color bar scheme
    cmap = plt.cm.get_cmap('YlOrRd')
    norm = mpl.colors.BoundaryNorm([1, 5, 10, 15, 20, 25, 30], cmap.N, extend='both')
    
    # Diverging color map for the difference plot
    cmap_diff = plt.cm.get_cmap('bwr')
    norm_diff = mpl.colors.TwoSlopeNorm(vmin=-10, vcenter=0, vmax=10)
    
    # Add maps to the main figure
    for col in range(3):
        for row in range(len(selected_indices)):
            index = row * 3 + col
            ax = axs_main[index]
            ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            ax.coastlines(color='dimgrey', linewidth=0.7)
            ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            ax.spines['geo'].set_visible(False)  # Remove border
            
            if col == 2:  # For the difference column
                plot = ax.imshow(
                    main_scenarios_data[col][selected_indices[row]], 
                    origin='lower', 
                    extent=[-180, 180, 90, -60], 
                    cmap=cmap_diff, 
                    norm=norm_diff
                )
            else:
                plot = ax.imshow(
                    main_scenarios_data[col][selected_indices[row]], 
                    origin='lower', 
                    extent=[-180, 180, 90, -60], 
                    cmap=cmap, 
                    norm=norm
                )
           
            if row == 0:
                ax.set_title(scenarios_main[col], fontsize=10)
            if col == 0:
                ax.text(-0.2, 0.5, selected_compound_events_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
            # Add subplot label
            ax.text(0.02, 1.05, subplot_labels[index], transform=ax.transAxes, fontsize=9, va='top', ha='left')
        
        
    fig_main.subplots_adjust(wspace=0.1, hspace=-0.7)
    cbar_ax_main = fig_main.add_axes([0.3, 0.25, 0.2, 0.015])
    cbar_main = fig_main.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax_main, orientation='horizontal', extend='max')
    cbar_main.ax.tick_params(labelsize=9)
    cbar_main.set_label(label='Years', size=9)
    
    # Color bar for the difference column
    cbar_ax_diff = fig_main.add_axes([0.6, 0.25, 0.2, 0.015])
    cbar_diff = fig_main.colorbar(mpl.cm.ScalarMappable(norm=norm_diff, cmap=cmap_diff), cax=cbar_ax_diff, orientation='horizontal', extend='both')
    cbar_diff.ax.tick_params(labelsize=9)
    cbar_diff.set_label(label='Difference (Years)', size=9)

    # Save and show the main plot
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/95th_percentile_of_length_of_spell_for_selected_compound_extremes_under_the_present_day_and_rcp60.pdf', dpi=300)
    plt.show()
    
    # Supplementary figure
    fig_supp, axs_supp = plt.subplots(len(selected_indices), 3, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})
    axs_supp = axs_supp.flatten()
    
    # Add maps to the supplementary figure
    for col in range(3):
        for row in range(len(selected_indices)):
            index = row * 3 + col
            ax = axs_supp[index]
            ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            ax.coastlines(color='dimgrey', linewidth=0.7)
            #ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            # Check if CF is in the event name and if it's the RCP8.5 column (last column)
            if "CF" in selected_compound_events_names[row] and col == 2:
                # Apply hatching for RCP8.5 scenario for CF
                ax.add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
            else:
                ax.add_feature(cfeature.LAND, facecolor='lightgrey')
            ax.spines['geo'].set_visible(False)  # Remove border

            plot = ax.imshow(
                supplementary_scenarios_data[col][selected_indices[row]], 
                origin='lower', 
                extent=[-180, 180, 90, -60], 
                cmap=cmap, 
                norm=norm
            )
           
            if row == 0:
                ax.set_title(scenarios_supplementary[col], fontsize=10)
            if col == 0:
                ax.text(-0.2, 0.5, selected_compound_events_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
            # Add subplot label
            ax.text(0.02, 1.05, subplot_labels[index], transform=ax.transAxes, fontsize=9, va='top', ha='left')
        
        
    fig_supp.subplots_adjust(wspace=0.1, hspace=-0.7)
    cbar_ax_supp = fig_supp.add_axes([0.4, 0.25, 0.25, 0.015])
    cbar_supp = fig_supp.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax_supp, orientation='horizontal', extend='max')
    cbar_supp.ax.tick_params(labelsize=9)
    cbar_supp.set_label(label='Years', size=9)

    # Save and show the supplementary plot
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/95th_percentile_of_length_of_spell_for_selected_compound_extremes_under_the_early_industrial_rcp26_andrcp85_SUPPLEMENT.pdf', dpi=300)
    plt.show()

    return main_scenarios_data, supplementary_scenarios_data


#%% Function for plotting the average frequency of all the extreme events
def plot_average_frequency_of_extreme_events_considering_all_gcms_and_showing_all_extremes(average_frequency_of_all_extreme_events, list_of_extreme_events):
    
    list_of_extreme_event_names = []
    for extreme_event in list_of_extreme_events:
        if extreme_event == 'floodedarea':
            event_name = 'River Floods'
        if extreme_event == 'driedarea':
            event_name = 'Droughts'
        if extreme_event == 'heatwavedarea':
            event_name = 'Heatwaves'
        if extreme_event == 'cropfailedarea':
            event_name = 'Crop Failures'
        if extreme_event =='burntarea':
            event_name = 'Wildfires'
        if extreme_event == 'tropicalcyclonedarea':
            event_name ='Tropical Cyclones'
        list_of_extreme_event_names.append(event_name)
   
    # Scenarios for the first figure:
    scenarios_main = ['Present day', 'RCP6.0', 'Difference']
    
    # Scenarios for the supplementary figure:
    #scenarios_supplementary = ['Early-industrial', 'RCP2.6']
    scenarios_supplementary = ['Early-industrial', 'RCP2.6', 'RCP8.5']
    
    # Calculate the difference between Present day and RCP6.0
    difference = calculate_difference(average_frequency_of_all_extreme_events[1], average_frequency_of_all_extreme_events[3])
    
    # Add the difference to the main scenarios data
    main_scenarios_data = [average_frequency_of_all_extreme_events[1], average_frequency_of_all_extreme_events[3], difference]
    
    #supplementary_scenarios_data = [average_frequency_of_all_extreme_events[0], average_frequency_of_all_extreme_events[2]]
    supplementary_scenarios_data = [average_frequency_of_all_extreme_events[0], average_frequency_of_all_extreme_events[2], average_frequency_of_all_extreme_events[4]]
   
    
    # Setting the projection of the map to cylindrical / Mercator
    fig_main, axs_main = plt.subplots(6, 3, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})
    #fig_supp, axs_supp = plt.subplots(6, 3, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})
    
    # Flattening the 2D arrays of axes into 1D
    axs_main = axs_main.flatten()
    #axs_supp = axs_supp.flatten()
    
    # Setting up the discrete color bar schemes
    cmap_main = plt.cm.get_cmap('viridis')
    norm_main = mpl.colors.BoundaryNorm([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3], cmap_main.N, extend='max')
    
    cmap_heatwave_wildfire = plt.cm.get_cmap('viridis')
    norm_heatwave_wildfire = mpl.colors.BoundaryNorm([0, 0.2, 0.4, 0.6, 0.8, 1], cmap_heatwave_wildfire.N, extend='max')
    
    # Diverging colormap for the difference plot
    cmap_diff = plt.cm.get_cmap('bwr')
    norm_diff = mpl.colors.TwoSlopeNorm(vmin=-0.3, vcenter=0, vmax=0.3)
    
    # Function to add maps to a figure
    def add_maps_to_figure(fig, axs, scenarios, cmap, norm, data):
        subplot_labels = 'abcdefghijklmnopqrstuvwx'
        
        for col in range(3):
            for row in range(6):
                index = row * 3 + col
                ax = axs[index]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                ax.spines['geo'].set_visible(False)  # Remove border
                
                if col == 2:  # Apply diverging colormap for the difference column
                    plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_diff, norm=norm_diff)
                elif list_of_extreme_event_names[row] in ['Heatwaves', 'Wildfires']:
                    plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_heatwave_wildfire, norm=norm_heatwave_wildfire)
                else:
                    plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_main, norm=norm_main)
           
                    
                ax.text(0.02, 0.95, subplot_labels[index], transform=ax.transAxes, fontsize=9, ha='left')
                if row == 0:
                    ax.set_title(scenarios[col], fontsize=10)
                if col == 0:
                    ax.text(-0.2, 0.5, list_of_extreme_event_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
        
        fig.subplots_adjust(wspace=0.1, hspace=0.1)
        cbar_ax_main = fig.add_axes([0.15, 0.05, 0.2, 0.015])
        cbar_main = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_main, cmap=cmap_main), cax=cbar_ax_main, orientation='horizontal', extend='max')
        cbar_main.ax.tick_params(labelsize=9)
        cbar_main.set_label(label='Probability of occurrence (RF, DR, TC & CF)', size=9)
        
        cbar_ax_hw_wf = fig.add_axes([0.415, 0.05, 0.2, 0.015])
        cbar_hw_wf = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_heatwave_wildfire, cmap=cmap_heatwave_wildfire), cax=cbar_ax_hw_wf, orientation='horizontal', extend='max')
        cbar_hw_wf.ax.tick_params(labelsize=9)
        cbar_hw_wf.set_label(label='Probability of occurrence (HW & WF)', size=9)

        # Add a colorbar specifically for the difference plot
        cbar_ax_diff = fig.add_axes([0.68, 0.05, 0.2, 0.015])
        cbar_diff = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_diff, cmap=cmap_diff), cax=cbar_ax_diff, orientation='horizontal', extend='both')
        cbar_diff.ax.tick_params(labelsize=9)
        cbar_diff.set_label(label='Difference in Probability of Occurrence', size=9)    
    
    add_maps_to_figure(fig_main, axs_main, scenarios_main, cmap_main, norm_main, main_scenarios_data)
    fig_main.suptitle('Frequency of extreme events - Main Scenarios', fontsize=12)
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Frequency_of_extremes_main_scenarios.pdf', dpi=300)
    plt.show()
    plt.close()
    
    
    fig_supp, axs_supp = plt.subplots(6, 3, figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})
    axs_supp = axs_supp.flatten()
    def add_maps_to_sup_figure(fig, axs, scenarios, cmap, norm, data):
        subplot_labels = 'abcdefghijklmnopqrstuvwx'
        
        for col in range(3):
            for row in range(6):
                index = row * 3 + col
                ax = axs[index]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                #ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                if list_of_extreme_event_names[row] == 'Crop Failures' and col == 2:
                    # Apply hatching for RCP8.5 scenario for CF

                    ax.add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
                else:
                    ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                ax.spines['geo'].set_visible(False)  # Remove border
                if data[col][row] is None:
                    ax.axis('off')
                else:
                    # Check if it's Crop Failures (CF) and RCP8.5 (third column)
                    
                        
                    
                    if list_of_extreme_event_names[row] in ['Heatwaves', 'Wildfires']:
                        plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_heatwave_wildfire, norm=norm_heatwave_wildfire)
                    else:
                        plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_main, norm=norm_main)
               
                    
                ax.text(0.02, 0.95, subplot_labels[index], transform=ax.transAxes, fontsize=9, ha='left')
                if row == 0:
                    ax.set_title(scenarios[col], fontsize=10)
                if col == 0:
                    ax.text(-0.2, 0.5, list_of_extreme_event_names[row], transform=ax.transAxes, fontsize=10, ha='center', va='center', rotation=90)
        
        fig.subplots_adjust(wspace=0.1, hspace=0.1)
        cbar_ax_main = fig.add_axes([0.15, 0.05, 0.3, 0.015])
        cbar_main = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_main, cmap=cmap_main), cax=cbar_ax_main, orientation='horizontal', extend='max')
        cbar_main.ax.tick_params(labelsize=9)
        cbar_main.set_label(label='Probability of occurrence (RF, DR, TC & CF)', size=9)
        
        cbar_ax_hw_wf = fig.add_axes([0.55, 0.05, 0.3, 0.015])
        cbar_hw_wf = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_heatwave_wildfire, cmap=cmap_heatwave_wildfire), cax=cbar_ax_hw_wf, orientation='horizontal', extend='max')
        cbar_hw_wf.ax.tick_params(labelsize=9)
        cbar_hw_wf.set_label(label='Probability of occurrence (HW & WF)', size=9)
    add_maps_to_sup_figure(fig_supp, axs_supp, scenarios_supplementary, cmap_main, norm_main, supplementary_scenarios_data)
    fig_supp.suptitle('Frequency of extreme events - Supplementary Scenarios', fontsize=12)
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Frequency_of_extremes_supplementary_scenarios.pdf', dpi=300)
    plt.show()
    plt.close()

    return supplementary_scenarios_data

#%% Function for plotting the co-occurrence ratio
def plot_cooccurrence_ratio_considering_all_gcms(average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms, gcms):
    
    """ Plot a map showing the average co-occurrence ratio across cross category impact models driven by the same GCM such that a co-occurrence ratio > 1 means that there are more co-occurring extremes than isolated extremes, and a co-occurence ratio < 1 means that there are less co-occurring extremes than isolated ones.  
    
    Parameters
    ----------
    average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms : Xarray data array of the average co-occurrence ratio across cross category impact models driven by the same GCM. In the order of gcms = ['gfdl-esm2m', 'hadgem2-es', 'ipsl-cm5a-lr', 'miroc5'], as stated at the beginning of this script
    event_name : String (Extreme Event whose propensity is to be calculated)
    gcms: List (Driving GCMs)
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the propensity of an extreme event per location per scenario
    """
    
    #average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms = [[], [], [], []] # In the order of gcms = ['gfdl-esm2m', 'hadgem2-es', 'ipsl-cm5a-lr', 'miroc5'], as stated at the beginning of this script
    
    scenarios =[0, 1, 2, 3]
    
    for scenario in scenarios:
        
        if scenario == 0:
            scenario_name = 'Early-industrial'
        if scenario == 1:
            scenario_name = 'Present-Day'
        if scenario == 2:
            scenario_name = 'RCP2.6'
        if scenario == 3:
            scenario_name = 'RCP6.0'

        
        # Setting the projection of the map to cylindrical / Mercator
        fig, axs = plt.subplots(2,2, figsize=(10, 6.5), subplot_kw = {'projection': ccrs.PlateCarree()})  # , constrained_layout=True
        
        # since axs is a 2 dimensional array of geozaxes, we have to flatten it into 1D; as explained on a similar example on this page: https://kpegion.github.io/Pangeo-at-AOES/examples/multi-panel-cartopy.html
        axs=axs.flatten()
        
        
        # Subplots for each GCM
        for gcm in range(len(average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms)): 
            
            # GCM
            gcm_name = gcms[gcm]
            
            if gcm_name == 'gfdl-esm2m':
                gcm_title = 'GFDL-ESM2M'
            if gcm_name == 'hadgem2-es':
                gcm_title =  'HadGEM2-ES'
            if gcm_name == 'ipsl-cm5a-lr':
                gcm_title = 'IPSL-CM5A-LR'
            if gcm_name == 'miroc5':
                gcm_title = 'MIROC5'
            
            # GCM data
            gcm_data = average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms[gcm]
            
                    
            # Plot the co-occurrence of an extreme event per GCM in a subplot
            plot = axs[gcm].imshow(gcm_data[scenario], origin = 'upper' , extent= map_extent, cmap = plt.cm.get_cmap('bwr'), vmin = 0, vmax =2)
            
            # Create a mask identifying infinite values brought about by having no extreme events occurring in isolation in the scenarios, thus having only compound event occurring.
            masked_array = xr.where(np.isinf(gcm_data[scenario]), 2, np.nan) # Here chose 2, because it is the limit of the legend(upper).
            
    
            # Overlay the mask on the plot with hatch for infinite values
            axs[gcm].contourf(masked_array, colors ='red', origin='upper', extent=map_extent, alpha =1) 
            
            
            # Add the background map to the plot  
            #ax.stock_img() 
                  
            # Set the extent of the plotn
            axs[gcm].set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            
            # Plot the coastlines along the continents on the map
            axs[gcm].coastlines(color='dimgrey', linewidth=0.7)
            
            # Plot features: lakes, rivers and boarders
            axs[gcm].add_feature(cfeature.LAKES, alpha =0.5)
            axs[gcm].add_feature(cfeature.RIVERS)
            axs[gcm].add_feature(cfeature.OCEAN)
            axs[gcm].add_feature(cfeature.LAND, facecolor ='lightgrey')
            #ax.add_feature(cfeature.BORDERS, linestyle=':')
            
            
            # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
            xticks = [-180, -120, -60, 0, 60, 120, 180] # Longitudes
            axs[gcm].set_xticks(xticks, crs=ccrs.PlateCarree()) 
            axs[gcm].set_xticklabels(xticks, fontsize = 8)
            
            yticks = [90, 60, 30, 0, -30, -60]
            axs[gcm].set_yticks(yticks, crs=ccrs.PlateCarree()) 
            axs[gcm].set_yticklabels(yticks, fontsize = 8)
            
            lon_formatter = LongitudeFormatter()
            lat_formatter = LatitudeFormatter()
            axs[gcm].xaxis.set_major_formatter(lon_formatter)
            axs[gcm].yaxis.set_major_formatter(lat_formatter)
            
            
            # Subplot labels
            # label
            subplot_labels = ['a.','b.','c.','d.']
            axs[gcm].text(0, 1.06, subplot_labels[gcm], transform=axs[gcm].transAxes, fontsize=9, ha='left')
           
            axs[gcm].set_title(gcm_title, fontsize = 9, loc = 'right')
           
       
        # Add the title and legend to the figure and show the figure
        fig.suptitle('Compound occurrence ratio',fontsize=10) #Plot title     
        
        # Discrete color bar legend
        fig.subplots_adjust(bottom=0.1, top=1.1, left=0.1, right=0.9, wspace=0.2, hspace=-0.5)
        cbar_ax = fig.add_axes([0.35, 0.2, 0.3, 0.02])
        cbar = fig.colorbar(plot, cax=cbar_ax, orientation='horizontal', extend='both', ticks=[0, 0.5, 1, 1.5, 2], shrink = 0.5)  #Plots the legend color bar   
        cbar.ax.tick_params(labelsize=9) # Text size on legend color bar 
        cbar.set_label(label = 'Co-occurrence ratio', size = 9)
    
        # Text outside the plot to display the scenario (top-center)
        fig.text(0.5,0.935,'{}'.format(scenario_name), fontsize = 8, ha = 'center', va = 'center')
    
        #plt.tight_layout()
        
        # Change this directory to save the plots to your desired directory
        plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Cooccurrence ratio under the {} scenario.pdf'.format(scenario_name), dpi = 300)
        
        plt.show()
            
                #plt.close()
        
    return average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms



#%% Function for plotting the co-occurrence ratio in A SINGLE PLOT
def plot_cooccurrence_ratio_considering_all_gcms_in_a_single_plot(summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios, mask_for_oceans):
    
    """ Plot a map showing the average co-occurrence ratio across cross category impact models driven by the same GCM such that a co-occurrence ratio > 1 means that there are more co-occurring extremes than isolated extremes, and a co-occurence ratio < 1 means that there are less co-occurring extremes than isolated ones.  
    
    Parameters
    ----------
    average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms : Xarray data array of the average co-occurrence ratio across cross category impact models driven by the same GCM. In the order of gcms = ['gfdl-esm2m', 'hadgem2-es', 'ipsl-cm5a-lr', 'miroc5'], as stated at the beginning of this script
    
    Returns
    -------
    Plot (Figure) showing the propensity of an extreme event per location per scenario
    """
    
    #average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms = [[], [], [], []] # In the order of gcms = ['gfdl-esm2m', 'hadgem2-es', 'ipsl-cm5a-lr', 'miroc5'], as stated at the beginning of this script
    
# =============================================================================
#     # Setting the projection of the map to cylindrical / Mercator
#     fig, axs = plt.subplots(1,3, figsize=(12, 10), subplot_kw = {'projection': ccrs.PlateCarree()})  # , constrained_layout=True
#     
#     # since axs is a 2 dimensional array of geozaxes, we have to flatten it into 1D; as explained on a similar example on this page: https://kpegion.github.io/Pangeo-at-AOES/examples/multi-panel-cartopy.html
#     axs=axs.flatten()
#     
#     # Custom colormap: grey for NaN, followed by 'bwr' colormap for other values
#     cmap = plt.cm.get_cmap('bwr')
#     cmap.set_bad('none')  # Setting the color for NaN values
# =============================================================================
        
    scenarios =[0, 1, 2, 3]
    
    scenarios_main = ['Present day', 'RCP6.0', 'Difference']
    
    scenarios_supplementary = ['Early-industrial', 'RCP2.6']
    
    def calculate_difference(present, rcp60):
        difference = [rcp60[i] - present[i] if present[i] is not None and rcp60[i] is not None else None for i in range(len(present))]
        return difference
    
    # Calculate the difference between Present day and RCP6.0
    difference = calculate_difference(summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[1], summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[3])
    
    main_scenarios_data = [summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[1], summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[3], difference]
    
    supplementary_scenarios_data = [summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[0], summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[2]]
    
    # Setting the projection of the map to cylindrical / Mercator
    fig_main, axs_main = plt.subplots(1, 3, figsize=(15, 5), subplot_kw={'projection': ccrs.PlateCarree()})
    axs_main = axs_main.flatten()
    
    # Setting up the discrete color bar schemes
    cmap_main = plt.cm.get_cmap('bwr')
    norm_main = mpl.colors.Normalize(vmin=0, vmax=2)
    
    # Diverging color map for the difference plot
    cmap_diff = plt.cm.get_cmap('PRGn')
    norm_diff = mpl.colors.TwoSlopeNorm(vmin=-2, vcenter=0, vmax=2)
    #boundaries = [0, 0.5, 1.0, 1.5, 2.0]
    #norm_main = mpl.colors.BoundaryNorm(boundaries, cmap_main.N, extend='max')
    
    # Function to add maps to a figure
    def add_maps_to_figure(fig, axs, scenarios, cmap, norm, data):
        subplot_labels = 'abcdefghijklmnopqrstuvwx'
        
        for col in range(3):
            for row in range(1):
                index = row*3 + col
                ax = axs[index]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                ax.spines['geo'].set_visible(False)  # Remove border
                if col == 2:  # Use coolwarm colormap for the difference column
                    plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_diff, norm=norm_diff)
                else:  # Use the default colormap for the other columns
                    plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_main, norm=norm_main)
                
                    
                ax.text(0.02, 0.95, subplot_labels[index], transform=ax.transAxes, fontsize=9, ha='left')
                if row == 0:
                    ax.set_title(scenarios[col], fontsize=10)
        
        fig.subplots_adjust(wspace=0.1, hspace=0.1)
        cbar_ax_main = fig.add_axes([0.35, 0.27, 0.15, 0.02])
        cbar_main = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_main, cmap=cmap_main), cax=cbar_ax_main, orientation='horizontal', extend='max', ticks=[0, 0.5, 1, 1.5, 2])
        cbar_main.ax.tick_params(labelsize=9)
        cbar_main.set_label(label='Co-occurrence ratio', size=9)
        
        # Add a colorbar specifically for the difference plot
        cbar_ax_diff = fig.add_axes([0.60, 0.27, 0.15, 0.02])
        cbar_diff = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_diff, cmap=cmap_diff), cax=cbar_ax_diff, orientation='horizontal', extend='both')
        cbar_diff.ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'{x:.1f}'))
        cbar_diff.ax.tick_params(labelsize=9)
        cbar_diff.set_label(label='Difference in Co-occurrence ratio', size=9)    
        
   
    add_maps_to_figure(fig_main, axs_main, scenarios_main, cmap_main, norm_main, main_scenarios_data)
    fig_main.suptitle('Frequency of extreme events - Main Scenarios', fontsize=12)
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Co_occurrence_ratio_main_scenarios.pdf', dpi=300)
    plt.show()
    plt.close()
    
    
    fig_supp, axs_supp = plt.subplots(1, 2, figsize=(15, 5), subplot_kw={'projection': ccrs.PlateCarree()})
    axs_supp = axs_supp.flatten()
    def add_maps_to_sup_figure(fig, axs, scenarios, cmap, norm, data):
        subplot_labels = 'abcdefghijklmnopqrstuvwx'
        
        for col in range(2):
            for row in range(1):
                index = row * 3 + col
                ax = axs[index]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                ax.spines['geo'].set_visible(False)  # Remove border
                plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_main, norm=norm_main)

                  
                ax.text(0.02, 0.95, subplot_labels[index], transform=ax.transAxes, fontsize=9, ha='left')
                if row == 0:
                    ax.set_title(scenarios[col], fontsize=10)

        
        fig.subplots_adjust(wspace=0.1, hspace=0.1)
        cbar_ax_main = fig.add_axes([0.45, 0.15, 0.15, 0.02])
        cbar_main = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_main, cmap=cmap_main), cax=cbar_ax_main, orientation='horizontal', extend='max', ticks=[0, 0.5, 1, 1.5, 2])
        cbar_main.ax.tick_params(labelsize=9)
        cbar_main.set_label(label='Co-occurrence ratio', size=9)
        
        
    
    add_maps_to_sup_figure(fig_supp, axs_supp, scenarios_supplementary, cmap_main, norm_main, supplementary_scenarios_data)
    fig_supp.suptitle('Frequency of extreme events - Supplementary Scenarios', fontsize=12)
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Co_occurrence_ratio_supplementary_scenarios.pdf', dpi=300)
    plt.show()
    plt.close()
    
    

        
    return summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios

#%%
def plot_cooccurrence_ratio_considering_all_gcms_in_a_single_plot_including_rcp85(summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios, mask_for_oceans):
    
    """ Plot a map showing the average co-occurrence ratio across cross category impact models driven by the same GCM such that a co-occurrence ratio > 1 means that there are more co-occurring extremes than isolated extremes, and a co-occurence ratio < 1 means that there are less co-occurring extremes than isolated ones.  
    
    Parameters
    ----------
    average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms : Xarray data array of the average co-occurrence ratio across cross category impact models driven by the same GCM. In the order of gcms = ['gfdl-esm2m', 'hadgem2-es', 'ipsl-cm5a-lr', 'miroc5'], as stated at the beginning of this script
    
    Returns
    -------
    Plot (Figure) showing the propensity of an extreme event per location per scenario
    """
    
    #average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms = [[], [], [], []] # In the order of gcms = ['gfdl-esm2m', 'hadgem2-es', 'ipsl-cm5a-lr', 'miroc5'], as stated at the beginning of this script
    
# =============================================================================
#     # Setting the projection of the map to cylindrical / Mercator
#     fig, axs = plt.subplots(1,3, figsize=(12, 10), subplot_kw = {'projection': ccrs.PlateCarree()})  # , constrained_layout=True
#     
#     # since axs is a 2 dimensional array of geozaxes, we have to flatten it into 1D; as explained on a similar example on this page: https://kpegion.github.io/Pangeo-at-AOES/examples/multi-panel-cartopy.html
#     axs=axs.flatten()
#     
#     # Custom colormap: grey for NaN, followed by 'bwr' colormap for other values
#     cmap = plt.cm.get_cmap('bwr')
#     cmap.set_bad('none')  # Setting the color for NaN values
# =============================================================================
        
    scenarios =[0, 1, 2, 3, 4]
    
    scenarios_main = ['Present day', 'RCP6.0', 'Difference']
    
    scenarios_supplementary = ['Early-industrial', 'RCP2.6', 'RCP8.5']
    
    def calculate_difference_present_rcp60(present, rcp60):
        difference = [rcp60[i] - present[i] if present[i] is not None and rcp60[i] is not None else None for i in range(len(present))]
# =============================================================================
#         difference = [
#             np.where((np.isnan(present[i]) | np.isinf(present[i])), rcp60[i], rcp60[i] - present[i])
#             if present[i] is not None and rcp60[i] is not None
#             else None
#             for i in range(len(present))
#         ]
# =============================================================================
        
        return difference
    
    # Calculate the difference between Present day and RCP6.0
    difference = calculate_difference_present_rcp60(summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[1], summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[3])
    
    main_scenarios_data = [summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[1], summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[3], difference]
    
    supplementary_scenarios_data = [summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[0], summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[2], summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios[4]]
    
    # Setting the projection of the map to cylindrical / Mercator
    fig_main, axs_main = plt.subplots(1, 3, figsize=(15, 5), subplot_kw={'projection': ccrs.PlateCarree()})
    axs_main = axs_main.flatten()
    
    # Setting up the discrete color bar schemes
    cmap_main = plt.cm.get_cmap('bwr')
    norm_main = mpl.colors.Normalize(vmin=0, vmax=2)
    
    # Diverging color map for the difference plot
    cmap_diff = plt.cm.get_cmap('PRGn')
    norm_diff = mpl.colors.TwoSlopeNorm(vmin=-2, vcenter=0, vmax=2)
    #boundaries = [0, 0.5, 1.0, 1.5, 2.0]
    #norm_main = mpl.colors.BoundaryNorm(boundaries, cmap_main.N, extend='max')
    
    # Function to add maps to a figure
    def add_maps_to_figure(fig, axs, scenarios, cmap, norm, data):
        subplot_labels = 'abcdefghijklmnopqrstuvwx'
        
        for col in range(3):
            for row in range(1):
                index = row*3 + col
                ax = axs[index]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                ax.spines['geo'].set_visible(False)  # Remove border
                if col == 2:  # Use coolwarm colormap for the difference column
                    plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_diff, norm=norm_diff)
                else:  # Use the default colormap for the other columns
                    plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_main, norm=norm_main)
                
                    
                ax.text(0.02, 0.95, subplot_labels[index], transform=ax.transAxes, fontsize=9, ha='left')
                if row == 0:
                    ax.set_title(scenarios[col], fontsize=10)
        
        fig.subplots_adjust(wspace=0.1, hspace=0.1)
        cbar_ax_main = fig.add_axes([0.35, 0.27, 0.15, 0.02])
        cbar_main = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_main, cmap=cmap_main), cax=cbar_ax_main, orientation='horizontal', extend='max', ticks=[0, 0.5, 1, 1.5, 2])
        cbar_main.ax.tick_params(labelsize=9)
        cbar_main.set_label(label='Co-occurrence ratio', size=9)
        
        # Add a colorbar specifically for the difference plot
        cbar_ax_diff = fig.add_axes([0.60, 0.27, 0.15, 0.02])
        cbar_diff = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_diff, cmap=cmap_diff), cax=cbar_ax_diff, orientation='horizontal', extend='both')
        cbar_diff.ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'{x:.1f}'))
        cbar_diff.ax.tick_params(labelsize=9)
        cbar_diff.set_label(label='Difference in Co-occurrence ratio', size=9)    
        
   
    add_maps_to_figure(fig_main, axs_main, scenarios_main, cmap_main, norm_main, main_scenarios_data)
    fig_main.suptitle('Frequency of extreme events - Main Scenarios', fontsize=12)
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Co_occurrence_ratio_main_scenarios.pdf', dpi=300)
    plt.show()
    plt.close()
    
    
    fig_supp, axs_supp = plt.subplots(1, 3, figsize=(15, 5), subplot_kw={'projection': ccrs.PlateCarree()})
    axs_supp = axs_supp.flatten()
    def add_maps_to_sup_figure(fig, axs, scenarios, cmap, norm, data):
        subplot_labels = 'abcdefghijklmnopqrstuvwx'
        
        for col in range(3):
            for row in range(1):
                index = row * 3 + col
                ax = axs[index]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                ax.spines['geo'].set_visible(False)  # Remove border
                plot = ax.imshow(data[col][row], origin='lower', extent=[-180, 180, 90, -60], cmap=cmap_main, norm=norm_main)

                  
                ax.text(0.02, 0.95, subplot_labels[index], transform=ax.transAxes, fontsize=9, ha='left')
                if row == 0:
                    ax.set_title(scenarios[col], fontsize=10)

        
        fig.subplots_adjust(wspace=0.1, hspace=0.1)
        cbar_ax_main = fig.add_axes([0.45, 0.27, 0.15, 0.02])
        cbar_main = fig.colorbar(mpl.cm.ScalarMappable(norm=norm_main, cmap=cmap_main), cax=cbar_ax_main, orientation='horizontal', extend='max', ticks=[0, 0.5, 1, 1.5, 2])
        cbar_main.ax.tick_params(labelsize=9)
        cbar_main.set_label(label='Co-occurrence ratio', size=9)
        
        
    
    add_maps_to_sup_figure(fig_supp, axs_supp, scenarios_supplementary, cmap_main, norm_main, supplementary_scenarios_data)
    fig_supp.suptitle('Frequency of extreme events - Supplementary Scenarios', fontsize=12)
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Co_occurrence_ratio_supplementary_scenarios.pdf', dpi=300)
    plt.show()
    plt.close()
    
    

        
    return summary_of_average_cooccurrence_ratio_considering_all_GCMs_and_scenarios

#%%
def plot_compound_event_occurrences_changes(total_average_compound_event_occurrence):
    """
    Plots the compound event occurrences across different scenarios with customization options.
    
    Parameters:
    - total_average_compound_event_occurrence: List containing values for each scenario [Early-industrial, Present day, RCP2.6, RCP6.0, RCP8.5]
    """
    # Extract individual scenario values
    early_industrial = total_average_compound_event_occurrence[0]
    present_day = total_average_compound_event_occurrence[1]
    future_scenarios = total_average_compound_event_occurrence[2:]

    # Set colors
    dot_colors = [(0.996, 0.89, 0.569), (0.996, 0.769, 0.31), (0.996, 0.6, 0.001), (0.851, 0.373, 0.0549), (0.6, 0.204, 0.016)]
    
    # The scenarios and their corresponding x positions
    scenarios = ['Early-industrial', 'Present day', 'Future Scenarios']
    x_positions = [0, 1, 2]
    rcp_offsets = [0, 0, 0]  # Small offsets for RCP2.6, RCP6.0, RCP8.5 within the "Future Scenarios" point

    # Plotting
    plt.figure(figsize=(10, 6))

    # Plot individual data points
    plt.plot(x_positions[0], early_industrial, 'o', color=dot_colors[0], label='Early-industrial')
    plt.text(0.2, early_industrial, f'{early_industrial:.2f}', fontsize=9, ha='right', va='bottom')

    plt.plot(x_positions[1], present_day, 'o', color=dot_colors[1], label='Present day')
    plt.text(1.2, present_day, f'{present_day:.2f}', fontsize=9, ha='right', va='bottom')

    # Plot the future scenarios with offsets for RCP2.6, RCP6.0, and RCP8.5
    future_labels = ['RCP2.6', 'RCP6.0', 'RCP8.5']
    for i, offset in enumerate(rcp_offsets):
        x_pos = x_positions[2] + offset
        plt.plot(x_pos, future_scenarios[i], 'o', color=dot_colors[i + 2], label=future_labels[i])
        plt.text(1.97, future_scenarios[i], f'{future_scenarios[i]:.2f}', fontsize=9, ha='right', va='bottom')

    # Customize the plot
    plt.xticks(x_positions, scenarios)
    plt.xlabel('Scenarios')
    plt.ylabel('Total Average Compound Event Occurrence')
    plt.title('Total Average Compound Event Occurrence Across Scenarios')
    plt.legend()
    plt.grid(True)
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/total_average_compound_event_occurrence.pdf', dpi = 300)
    

    # Show the plot
    plt.show()

#%% Function for plotting the 95th percentile of length of spell of extreme event
def plot_95th_percentile_of_length_of_spell_of_extreme_events_considering_all_gcms(quantile_95th_of_length_of_spell_with_occurrence_of_extreme_events_considering_all_gcms, extreme_event_categories, gcms):
    
    """ Plot a map showing the average co-occurrence ratio across cross category impact models driven by the same GCM such that a co-occurrence ratio > 1 means that there are more co-occurring extremes than isolated extremes, and a co-occurence ratio < 1 means that there are less co-occurring extremes than isolated ones.  
    
    Parameters
    ----------
    average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms : Xarray data array of the average co-occurrence ratio across cross category impact models driven by the same GCM. In the order of gcms = ['gfdl-esm2m', 'hadgem2-es', 'ipsl-cm5a-lr', 'miroc5'], as stated at the beginning of this script
    event_name : String (Extreme Event whose propensity is to be calculated)
    gcms: List (Driving GCMs)
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the propensity of an extreme event per location per scenario
    """
    
    for extreme_event_category in extreme_event_categories:
        
        extreme_event_name = event_name(extreme_event_category)
        extreme_event_index = extreme_event_categories.index(extreme_event_category)
        
        scenarios =[0, 1, 2, 3, 4] # Representing the scenarios: [early industrial, present day, rcp 2.6, rcp6.0, rcp 8.5]
        
        for scenario in scenarios:
            
            if scenario == 0:
                scenario_name = 'Early-industrial'
            if scenario == 1:
                scenario_name = 'Present-Day'
            if scenario == 2:
                scenario_name = 'RCP2.6'
            if scenario == 3:
                scenario_name = 'RCP6.0'
            if scenario == 4:
                scenario_name = 'RCP8.5'
            
            # Setting the projection of the map to cylindrical / Mercator
            fig, axs = plt.subplots(2,2, figsize=(10, 6.5), subplot_kw = {'projection': ccrs.PlateCarree()})  # , constrained_layout=True
            
            # since axs is a 2 dimensional array of geozaxes, we have to flatten it into 1D; as explained on a similar example on this page: https://kpegion.github.io/Pangeo-at-AOES/examples/multi-panel-cartopy.html
            axs=axs.flatten()
            
            if scenario == 4 and extreme_event_name == 'Crop Failures':
                
                print('No data available for the crop failures for the selected GCM scenario under RCP8.5')
                plt.close()
            
            else: 
                for gcm in range(len(quantile_95th_of_length_of_spell_with_occurrence_of_extreme_events_considering_all_gcms)):
                
                    gcm_name = gcms[gcm] # GCM name
                    
                    if gcm_name == 'gfdl-esm2m':
                        gcm_title = 'GFDL-ESM2M'
                    if gcm_name == 'hadgem2-es':
                        gcm_title =  'HadGEM2-ES'
                    if gcm_name == 'ipsl-cm5a-lr':
                        gcm_title = 'IPSL-CM5A-LR'
                    if gcm_name == 'miroc5':
                        gcm_title = 'MIROC5'
                    
                    
                    if extreme_event_name == 'Crop Failures' and scenario == 4:
                        print('No data available on occurrence of crop failures for selected impact model under RCP 8.5 \n')
                    else:
                    
                        gcm_data_for_an_extreme_event_per_scenario = quantile_95th_of_length_of_spell_with_occurrence_of_extreme_events_considering_all_gcms[gcm][scenario][extreme_event_index]
        
                
                    
                        # Plot the propensity of an extreme event per GCM in a subplot
                        plot = axs[gcm].imshow(gcm_data_for_an_extreme_event_per_scenario, origin = 'upper' , extent= map_extent, cmap = plt.cm.get_cmap('YlOrRd'), vmin = 1, vmax =30)
                                
                        # Add the background map to the plot
                        #ax.stock_img()
                              
                        # Set the extent of the plotn
                        axs[gcm].set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                        
                        # Plot the coastlines along the continents on the map
                        axs[gcm].coastlines(color='dimgrey', linewidth=0.7)
                        
                        # Plot features: lakes, rivers and boarders
                        axs[gcm].add_feature(cfeature.LAKES, alpha =0.5)
                        axs[gcm].add_feature(cfeature.RIVERS)
                        axs[gcm].add_feature(cfeature.OCEAN)
                        axs[gcm].add_feature(cfeature.LAND, facecolor ='lightgrey')
                        #ax.add_feature(cfeature.BORDERS, linestyle=':')
                        
                        
                        # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
                        xticks = [-180, -120, -60, 0, 60, 120, 180] # Longitudes
                        axs[gcm].set_xticks(xticks, crs=ccrs.PlateCarree()) 
                        axs[gcm].set_xticklabels(xticks, fontsize = 8)
                        
                        yticks = [90, 60, 30, 0, -30, -60]
                        axs[gcm].set_yticks(yticks, crs=ccrs.PlateCarree()) 
                        axs[gcm].set_yticklabels(yticks, fontsize = 8)
                        
                        lon_formatter = LongitudeFormatter()
                        lat_formatter = LatitudeFormatter()
                        axs[gcm].xaxis.set_major_formatter(lon_formatter)
                        axs[gcm].yaxis.set_major_formatter(lat_formatter)
                        
                        
                        # Subplot labels
                        # label
                        subplot_labels = ['a.','b.','c.','d.']
                        axs[gcm].text(0, 1.06, subplot_labels[gcm], transform=axs[gcm].transAxes, fontsize=9, ha='left')
                       
                        axs[gcm].set_title(gcm_title, fontsize = 9, loc = 'right')
                       
                    
                # Add the title and legend to the figure and show the figure
                fig.suptitle('95th Percentile of Length of {} Spells'.format(extreme_event_name),fontsize=10) #Plot title     
                
                # Discrete color bar legend
                fig.subplots_adjust(bottom=0.1, top=1.1, left=0.1, right=0.9, wspace=0.2, hspace=-0.5)
                cbar_ax = fig.add_axes([0.35, 0.2, 0.3, 0.02])
                cbar = fig.colorbar(plot, cax=cbar_ax, orientation='horizontal', extend='max', boundaries=[1, 5, 10, 15, 20, 25, 30], spacing = 'uniform', shrink = 0.5)  #Plots the legend color bar   
                cbar.ax.tick_params(labelsize=9) # Text size on legend color bar 
                cbar.set_label(label = 'Length of spell (years)', size = 9)
            
                # Text outside the plot to display the scenario (top-center)
                fig.text(0.5,0.935,'{}'.format(scenario_name), fontsize = 8, ha = 'center', va = 'center')
            
                #plt.tight_layout()
                
                # Change this directory to save the plots to your desired directory
                plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/current_results/95th percentile of length of {} spells under the {} scenario.pdf'.format(extreme_event_name, scenario_name), dpi = 300)
                
                plt.show()
                
            #plt.close()
        
    return quantile_95th_of_length_of_spell_with_occurrence_of_extreme_events_considering_all_gcms



#%% Function for plotting map showing the Probability of joint occurrence of the extreme events in one grid cell over the entire dataset period
def plot_probability_of_occurrence_of_compound_events(no_of_years_with_compound_events, event_1_name, event_2_name, time_period, gcm, scenario):
    
    """ Plot a map showing the probability of occurrence of a compound extreme climate event over the entire dataset time period
    
    Parameters
    ----------
    occurrence of compound extreme event : Xarray data array (boolean with true for locations with the occurrence of both events within the same year)
    event_1_name, event_2_name : String (Extreme Events)
    gcm : String (Driving GCM)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the probability of joint occurrence of two extreme climate events over the entire dataset time period
    
    """
    
    # Probability of occurrence
    probability_of_occurrence_of_the_compound_event = no_of_years_with_compound_events/50  # as 50 years were considered to determine the probability 
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree()) 
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    #grid= ax.gridlines(draw_labels = True, dms = True )
    #grid.top_labels = False #Removes the grid labels from the top of the plot
    #grid.right_labels= False #Removes the grid labels from the right of the plot
    
    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(probability_of_occurrence_of_the_compound_event, origin = 'upper' , extent=map_extent, cmap = plt.cm.get_cmap('viridis', 10))
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.65,0.85,'{}, {}'.format(time_period, scenario), fontsize = 8)
    plt.gcf().text(0.15,0.03,'{}'.format(gcm), fontsize= 8)
    
    # Add the title and legend to the figure and show the figure
    plt.title('Probability of joint occurrence of {} and {} \n'.format(event_1_name, event_2_name),fontsize=11) #Plot title
    
    # discrete color bar legend
    #bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
    plt.colorbar( orientation = 'horizontal', shrink =0.5).set_label(label = 'Probability of joint occurrence', size = 9) #Plots the legend color bar
    plt.clim(0,1)
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels
    plt.show()
    
    #plt.close()
    
    return probability_of_occurrence_of_the_compound_event


#%% Function for plotting map showing the Probability Ration (PR) of joint occurrence of the extreme events in one grid cell over the entire dataset period
def plot_probability_ratio_of_occurrence_of_compound_events(probability_of_occurrence_of_the_compound_event_in_scenario, probability_of_occurrence_of_the_compound_event_in_historical_early_industrial_scenario, event_1_name, event_2_name, time_period, gcm, scenario):
    
    """ Plot a map showing the probability of occurrence of a compound extreme climate event over the entire dataset time period
    
    Parameters
    ----------
    probability_of_occurrence_of_the_compound_event_in_scenario : Xarray data array (probability of joint occurrence of two extreme climate events over the entire dataset time period in new scenario)
    probability_of_occurrence_of_the_compound_event_in_historical_early_industrial_scenario : Xarray data array (probability of joint occurrence of two extreme climate events over the entire dataset time period in historical early industrial time period)
    event_1_name, event_2_name : String (Extreme Events)
    gcm : String (Driving GCM)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the Probability Ratio (PR) of joint occurrence of two extreme climate events to compare the change in new scenario from the historical early industrial times
    
    """
    
    # Probability Ratio (PR) of occurrence: ratio between probability of occurrence of compound event in new situation (scenario) to the probability of occurrence in the reference situtaion (historical early industrial times)
    probability_of_ratio_of_occurrence_of_the_compound_event = probability_of_occurrence_of_the_compound_event_in_scenario/probability_of_occurrence_of_the_compound_event_in_historical_early_industrial_scenario
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    #grid= ax.gridlines(draw_labels = True, dms = True )
    #grid.top_labels = False #Removes the grid labels from the top of the plot
    #grid.right_labels= False #Removes the grid labels from the right of the plot
    
    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(probability_of_ratio_of_occurrence_of_the_compound_event, origin = 'upper' , extent=map_extent, cmap = plt.cm.get_cmap('viridis', 12))
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.25,0.85,'Comparing {} ({}) to 1861-1890 (historical)'.format(time_period, scenario), fontsize = 8)
    plt.gcf().text(0.15,0.03,'{}'.format(gcm), fontsize= 8)
    
    # Add the title and legend to the figure and show the figure
    plt.title('Average Probability Ratio of Joint Occurrence  of {} and {} events \n'.format(event_1_name, event_2_name),fontsize=10) #Plot title
    
    # discrete color bar legend
    #bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
    plt.colorbar( orientation = 'horizontal', extend = 'max', shrink = 0.5).set_label(label = 'Probability Ratio', size = 9) #Plots the legend color bar
    plt.clim(0,30)
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels
    plt.show()
    
    #plt.close()
    
    return probability_of_ratio_of_occurrence_of_the_compound_event

#%% Plot average probability of compound events accross all impact models and all their driving GCMs
def plot_average_probability_of_occurrence_of_compound_events(average_probability_of_occurrence_of_compound_events_across_the_gcms, event_1_name, event_2_name, time_period, scenario):
    
    """ Plot a map showing the probability of occurrence of a compound extreme climate event over the entire dataset time period
    
    Parameters
    ----------
    occurrence of compound extreme event : Xarray data array (boolean with true for locations with the occurrence of both events within the same year)
    event_1_name, event_2_name : String (Extreme Events)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the probability of joint occurrence of two extreme climate events over the entire dataset time period
    
    """
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    #grid= ax.gridlines(draw_labels = True, dms = True )
    #grid.top_labels = False #Removes the grid labels from the top of the plot
    #grid.right_labels= False #Removes the grid labels from the right of the plot
    
    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(average_probability_of_occurrence_of_compound_events_across_the_gcms, origin = 'upper' , extent=map_extent, cmap = plt.cm.get_cmap('viridis', 12))
    
    # Average probability across the entire region (1 value for the whole region per scenario)
    average_probability_across_the_entire_region = average_probability_of_occurrence_of_compound_events_across_the_gcms.mean()
    plt.gcf().text(0.15,0.01,'Average Probability across the entire region = {}'.format(round(average_probability_across_the_entire_region.item(), 3)), fontsize = 8)
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.65,0.85,'{}, {}'.format(time_period, scenario), fontsize = 8)
    
    # Incase you want to add the title to the plot
    #plt.title('Average probability of joint occurrence of {} and {} considering all impact models and their respective driving GCMs'.format(event_1_name, event_2_name),fontsize=11) #Plot title
    
    # discrete color bar legend
    #bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
    plt.colorbar( orientation = 'horizontal', extend = 'max', shrink = 0.5).set_label(label = 'Probability of joint occurrence', size = 9) #Plots the legend color bar
    plt.clim(0,0.6)
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels
    
    # Change this directory to save the plots to your desired directory
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/Average probability of joint occurrence of {} and {} under the {} scenario.pdf'.format(event_1_name, event_2_name, scenario), dpi = 300)
     
    plt.show()
    #plt.close()
    
    return average_probability_of_occurrence_of_compound_events_across_the_gcms

#%% Function for plotting map showing the maximum number of years with consecutive compound events in the same location during the entire dataset period
def maximum_no_of_years_with_consecutive_compound_events(occurrence_of_compound_event):
    
    """Determine the maximum number of years with consecutive compound extreme events in the same location over the entire dataset period
    
    Parameters
    ----------
    occurrence of compound extreme event : Xarray data array (boolean with true for locations with the occurrence of both events within the same year)
    
    Returns
    -------
    Xarray with the maximum number of years with consecutive compound extreme events in the same location over the entire dataset period
              
    """
    
    # Calculates the cumulative sum and whenever a false is met, it resets the sum to zero. thus the .max() returns the maximum cummumlative sum along the entire time dimension
    maximum_no_of_years_with_consecutive_compound_events = (occurrence_of_compound_event.cumsum('time',skipna=False) - occurrence_of_compound_event.cumsum('time',skipna=False).where(occurrence_of_compound_event.values == False).ffill('time').fillna(0)).max('time')
       
    
    return maximum_no_of_years_with_consecutive_compound_events

#%% Function for plotting map showing the maximum number of years with consecutive compound events in the same location during the entire dataset period
def plot_maximum_no_of_years_with_consecutive_compound_events(average_maximum_no_of_years_with_consecutive_compound_events, event_1_name, event_2_name, time_period, gcm, scenario):
    
    """Plot a map showing the maximum number of years with consecutive compound extreme events in the same location over the entire dataset period
    
    Parameters
    ----------
    average_maximum_no_of_years_with_consecutive_compound_events : Xarray data array (with the maximum number of years with consecutive compound extreme events in the same location over the entire dataset period)
    event_1_name, event_2_name : String (Extreme Events)
    gcm : String (Driving GCM)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the maximum number of years with consecutive compound extreme events in the same location over the entire dataset period
              
    """
     
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    # grid= ax.gridlines(draw_labels = True, dms = True )
    # grid.top_labels = False #Removes the grid labels from the top of the plot
    # grid.right_labels= False #Removes the grid labels from the right of the plot
    
    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(average_maximum_no_of_years_with_consecutive_compound_events, origin = 'upper' , extent=map_extent, cmap = plt.cm.get_cmap('YlOrRd', 10))
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.65,0.85,'{}, {}'.format(time_period, scenario), fontsize = 8)
    plt.gcf().text(0.15,0.03,'{}'.format(gcm), fontsize= 8)
    
    # Add the title and legend to the figure and show the figure
    plt.title('Maximum no. of consecutive years with Joint occurrence of \n{} and {} \n '.format(event_1_name, event_2_name),fontsize=11) #Plot title
    
    # discrete color bar legend
    #bounds = [0,5,10,15,20,25,30]
    
    plt.colorbar(orientation = 'horizontal', shrink = 0.5).set_label(label = 'Number of years', size = 9) #Plots the legend color bar
    plt.clim(0,50)
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels

    plt.show()
    plt.close()
    
    return average_maximum_no_of_years_with_consecutive_compound_events


#%% Function for plotting map showing the averAGE maximum number of years with consecutive compound events in the same location during the entire dataset period CONSIDERING ALL IMPACT MODELS AND THIER RESPECTIVE DRIVING GCMS
def plot_average_maximum_no_of_years_with_consecutive_compound_events_considering_all_impact_models_and_their_driving_gcms(average_max_no_of_consecutive_years_with_compound_events, event_1_name, event_2_name, time_period, scenario):
    
    """Plot a map showing the maximum number of years with consecutive compound extreme events in the same location over the entire dataset period
    
    Parameters
    ----------
    average_max_no_of_consecutive_years_with_compound_events : Xarray data array (with the maximum number of years with consecutive compound extreme events in the same location over the entire dataset period)
    event_1_name, event_2_name : String (Extreme Events)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the average maximum number of years with consecutive compound extreme events in the same location over the entire dataset period
              
    """
    
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    # grid= ax.gridlines(draw_labels = True, dms = True )
    # grid.top_labels = False #Removes the grid labels from the top of the plot
    # grid.right_labels= False #Removes the grid labels from the right of the plot
    
    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(average_max_no_of_consecutive_years_with_compound_events, origin = 'upper' , extent=map_extent, cmap = plt.cm.get_cmap('YlOrRd', 12))
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.65,0.85,'{}, {}'.format(time_period, scenario), fontsize = 8)
    
    # Incase you want to add the title and legend to the figure and show the figure
    #plt.title('Maximum no. of consecutive years with Joint occurrence of \n{} and {} \n '.format(event_1_name, event_2_name),fontsize=11) #Plot title
    
    # discrete color bar legend
    #bounds = [0,5,10,15,20,25,30]
    
    plt.colorbar(orientation = 'horizontal', extend = 'max', shrink = 0.5).set_label(label = 'Number of years', size = 9) #Plots the legend color bar
    plt.clim(0,30)
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels

    
    # Change this directory to save the plots to your desired directory
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/Average maximum number of years with concurrent {} and {} under the {} scenario.pdf'.format(event_1_name, event_2_name, scenario), dpi = 300)
    
    plt.show()
    #plt.close()
    
    return average_max_no_of_consecutive_years_with_compound_events



#%% A timeseries showing the fraction of the area affected by an extreme event across full timescale in the scenario
def timeseries_fraction_of_area_affected(occurrence_of_extreme_event, entire_globe_grid_cell_areas_in_xarray):
    """ A timeseries showing the fraction of the area affected (AS A PERCENTAGE) by an extreme event OR compound extreme event

    Parameters
    ----------
    occurrence_of_extreme_event : Xarray

    Returns
    -------
    Timeseries (Xarray)

    """
    
    
    # Clip out cell grid area data for East African Region that lies between LATITUDES 24N and 13S & LONGITUDES 18E and 55E
    latitude_bounds, longitude_bounds =[90, -60], [-180, 180]
    east_africa_grid_cell_areas = entire_globe_grid_cell_areas_in_xarray.sel(lat=slice(*latitude_bounds), lon=slice(*longitude_bounds)) #Clipped dataset
    
    
    # mask area with nan values e.g. over the ocean
    masked_area_affected_by_occurrence_of_extreme_event = xr.where(np.isnan(occurrence_of_extreme_event), np.nan, east_africa_grid_cell_areas)
    # mask area without occurrence of compound events
    area_affected_by_occurrence_of_extreme_event = xr.where(occurrence_of_extreme_event == 1, masked_area_affected_by_occurrence_of_extreme_event, np.nan)
    # total area affected by occurrence of compound events per year
    total_area_affected_by_occurrence_of_extreme_event = area_affected_by_occurrence_of_extreme_event.sum(dim=['lon', 'lat'], skipna=True)
    
    # total area of the region (excluding the ocean)
    total_area_of_the_region = masked_area_affected_by_occurrence_of_extreme_event.sum(dim=['lon','lat'], skipna= True)
    
    # percentage of area affected
    percentage_of_area_affected_by_occurrence_of_extreme_event = (total_area_affected_by_occurrence_of_extreme_event/total_area_of_the_region)*100
     
    
    return percentage_of_area_affected_by_occurrence_of_extreme_event
            
#%% Plot timeseries showing the fraction of the area affected by the compound extreme event across full timescale in the scenario
def plot_timeseries_fraction_of_area_affected_by_compound_events(timeseries_of_joint_occurrence_of_compound_events, event_1_name, event_2_name, gcm):
    """ Plot timeseries showing the fraction of the area affected by the compound extreme event across full timescale in the scenario
    
    Parameters
    ----------
    timeseries_of_joint_occurrence_of_compound_events : Tuple

    Returns
    Time series plot 
 
    """
    
    colors = [(0.996, 0.89, 0.569), (0.996, 0.769, 0.31), (0.996, 0.6, 0.001), (0.851, 0.373, 0.0549), (0.6, 0.204, 0.016)] # color palette
  
    plt.figure(figsize = (5.5,4))
    
    # Considering a 10-year moving average window to reduce the noise in the timeseries plots
    
    # historical plot #considering historical starting from 1930
    all_impact_model_historical_timeseries = xr.concat(timeseries_of_joint_occurrence_of_compound_events[0], dim='impact_model').mean(dim='impact_model', skipna= True) # Mean accross all impact models driven by the same GCM
    historical_timeseries = (all_impact_model_historical_timeseries[69:]).rolling(time=10, min_periods=1).mean()
    std_historical_timeseries = (all_impact_model_historical_timeseries[69:]).rolling(time=10, min_periods=1).std() # standard deviation accross all impact models driven by the same GCM
    lower_line_historical_timeseries = (historical_timeseries - 2 * std_historical_timeseries) # lower 95% confidence interval 
    upper_line_historical_timeseries = (historical_timeseries + 2 * std_historical_timeseries) # upper 95% confidence interval
    plot1 = historical_timeseries.plot.line(x ='time', color=(0.996, 0.89, 0.569), add_legend='False') # plot line
    plot1_fill = plt.fill_between(np.ravel(np.array((historical_timeseries.time), dtype = 'datetime64[ns]')), xr.where((lower_line_historical_timeseries.squeeze())<0,0,(lower_line_historical_timeseries.squeeze())), xr.where((upper_line_historical_timeseries.squeeze())>100,100,(upper_line_historical_timeseries.squeeze())), color=(0.996, 0.89, 0.569), alpha = 0.1) # plot the uncertainty bands #np.ravel() to avoid arrays in arrays e.g [[]]
    
    # rcp26 plot
    all_impact_model_rcp26_timeseries = xr.concat(timeseries_of_joint_occurrence_of_compound_events[1], dim='impact_model').mean(dim='impact_model', skipna= True) # Mean accross all impact models driven by the same GCM
    rcp26_timeseries = (all_impact_model_rcp26_timeseries).rolling(time=10, min_periods=1).mean()
    std_rcp26_timeseries = (all_impact_model_rcp26_timeseries).rolling(time=10, min_periods=1).std() # standard deviation accross all impact models driven by the same GCM
    lower_line_rcp26_timeseries = (rcp26_timeseries - 2 * std_rcp26_timeseries) # lower 95% confidence interval
    upper_line_rcp26_timeseries = (rcp26_timeseries + 2 * std_rcp26_timeseries) # upper 95% confidence interval
    plot2 = rcp26_timeseries.plot.line(x = 'time', color=(0.996, 0.6, 0.001), add_legend='False') # plot line
    plot2_fill = plt.fill_between(np.ravel(np.array((rcp26_timeseries.time), dtype = 'datetime64[ns]')), xr.where((lower_line_rcp26_timeseries.squeeze())<0,0,(lower_line_rcp26_timeseries.squeeze())), xr.where((upper_line_rcp26_timeseries.squeeze())>100,100,(upper_line_rcp26_timeseries.squeeze())), color=(0.996, 0.6, 0.001), alpha = 0.1) # plot the uncertainty bands
    
    # rcp60 plot
    all_impact_model_rcp60_timeseries = xr.concat(timeseries_of_joint_occurrence_of_compound_events[2], dim='impact_model').mean(dim='impact_model', skipna= True) # Mean accross all impact models driven by the same GCM
    rcp60_timeseries = (all_impact_model_rcp60_timeseries).rolling(time=10, min_periods=1).mean()
    std_rcp60_timeseries = (all_impact_model_rcp60_timeseries).rolling(time=10, min_periods=1).std() # standard deviation accross all impact models driven by the same GCM
    lower_line_rcp60_timeseries = (rcp60_timeseries - 2 * std_rcp60_timeseries) # lower 95% confidence interval
    upper_line_rcp60_timeseries = (rcp60_timeseries + 2 * std_rcp60_timeseries) # upper 95% confidence interval
    plot3 = rcp60_timeseries.plot.line(x= 'time', color= (0.851, 0.373, 0.0549), add_legend='False') # plot line
    plot3_fill = plt.fill_between(np.ravel(np.array((rcp60_timeseries.time), dtype = 'datetime64[ns]')), xr.where((lower_line_rcp60_timeseries.squeeze())<0,0,(lower_line_rcp60_timeseries.squeeze())), xr.where((upper_line_rcp60_timeseries.squeeze())>100,100,(upper_line_rcp60_timeseries.squeeze())), color=(0.851, 0.373, 0.0549), alpha = 0.1) # plot the uncertainty bands
    
    
    # legend in order of plots: historical, rcp2.6, rcp6.0
    plt.legend([(plot1[0],plot1_fill), (plot2[0],plot2_fill), (plot3[0],plot3_fill)],['historical','rcp2.6','rcp6.0'], loc = 'upper left', frameon= False, fontsize = 12)
    
    if len(timeseries_of_joint_occurrence_of_compound_events) == 4:
        
        #rcp85 plot
        all_impact_model_rcp85_timeseries = xr.concat(timeseries_of_joint_occurrence_of_compound_events[3], dim='impact_model').mean(dim='impact_model', skipna= True) # Mean accross all impact models driven by the same GCM
        rcp85_timeseries = (all_impact_model_rcp85_timeseries).rolling(time=10, min_periods=1).mean()
        std_rcp85_timeseries = (all_impact_model_rcp85_timeseries).rolling(time=10, min_periods=1).std() # standard deviation accross all impact models driven by the same GCM
        lower_line_rcp85_timeseries = (rcp85_timeseries - (2 * std_rcp85_timeseries)) # lower 95% confidence interval
        upper_line_rcp85_timeseries = (rcp85_timeseries + (2 * std_rcp85_timeseries)) # upper 95% confidence interval
        plot4 = rcp85_timeseries.plot.line(x= 'time', color= (0.6, 0.204, 0.016), add_legend='False') # plot line
        plot4_fill = plt.fill_between(np.ravel(np.array((rcp85_timeseries.time), dtype = 'datetime64[ns]')), xr.where((lower_line_rcp85_timeseries.squeeze())<0,0,(lower_line_rcp85_timeseries.squeeze())), xr.where((upper_line_rcp85_timeseries.squeeze())>100,100,(upper_line_rcp85_timeseries.squeeze())), color=(0.6, 0.204, 0.016), alpha = 0.1) # plot the uncertainty bands
        
        
        # legend in order of plots: historical, rcp2.6, rcp6.0, rcp8.5
        plt.legend([(plot1[0],plot1_fill), (plot2[0],plot2_fill), (plot3[0],plot3_fill), (plot4[0],plot4_fill)],['historical','rcp2.6','rcp6.0', 'rcp8.5'], loc = 'upper left', frameon= False, fontsize = 12)
    
    
    # Add the title and legend to the figure and show the figure
    plt.title('Timeseries showing the fraction of region with Joint occurrence of \n {} and {} '.format(event_1_name, event_2_name),fontsize=12) #Plot title
    plt.ylim(0,70)
    plt.xlabel('Years', fontsize = 10)
    plt.ylabel('Percentage of area', fontsize =10)
    plt.tight_layout()
    
    plt.gcf().text(0.12,0.03,'{}'.format(gcm), fontsize= 10)
    
    plt.show()
    
    
    return timeseries_of_joint_occurrence_of_compound_events
    

    
#%% Plot comparison timeseries showing the fraction of the area affected by extreme events across a 50 year timescale in the scenario
def plot_comparison_timeseries_fraction_of_area_affected_by_extreme_events(timeseries_of_occurrence_of_extreme_event_1, timeseries_of_occurrence_of_extreme_event_2, timeseries_of_fraction_of_area_with_occurrence_of_compound_events_during_same_time_period, event_1_name, event_2_name, time_period, gcm, scenario):
    """ Plot comparison timeseries showing the fraction of the area affected by the extreme events across 50 year timescale in the scenario
    
    Parameters
    ----------
    timeseries_of_occurrence_of_extreme_event_1, timeseries_of_occurrence_of_extreme_event_2, timeseries_of_fraction_of_area_with_occurrence_of_compound_events_during_same_time_period : Arrays

    Returns
    Time series plot 
 
    """
    plt.figure(figsize = (5.5,4))
      
    # extreme event 1
    timeseries_of_occurrence_of_extreme_event_1.plot.line(x = 'time', color='blue', add_legend='False')
    
    # extreme event 2
    timeseries_of_occurrence_of_extreme_event_2.plot.line(x ='time', color='green', add_legend='False')
    
    # joint occurrence
    timeseries_of_fraction_of_area_with_occurrence_of_compound_events_during_same_time_period.plot.line(x ='time', color='red', add_legend='False')
    
    # legend in order of plots: historical, rcp2.6, rcp6.0
    plt.legend([event_1_name,event_2_name,'Joint occurrence'], loc = 'upper left', frameon= False)
    
    plt.title('Timeseries showing the percentage of the region with \n Occurrence of {} and {} \n '.format(event_1_name, event_2_name),fontsize=11) #Plot title
    
    plt.gcf().text(0.68,0.85,'{}, {}'.format(time_period, scenario), fontsize = 10)
    plt.gcf().text(0.12,0.03,'{}'.format(gcm), fontsize= 10)
    
    plt.ylim(0,100)
    plt.xlabel('Years', fontsize = 12)
    plt.ylabel('Percentage of area', fontsize = 12)
    plt.tight_layout()
    plot_comparison = plt.show()
    
    return plot_comparison  


#%% Function for plotting the confidence ellipse of the covariance of two extreme event occurrences
def confidence_ellipse(x, y, ax, **kwargs):
    """
    Create a plot of the covariation confidence ellipse op `x` and `y`

    Parameters
    ----------
    x, y : array_like, shape (n, )
        Input data

    Returns
    -------
    float: the Pearson Correlation Coefficient for `x` and `y`.

    Other parameters
    ----------------
    kwargs : `~matplotlib.patches.Patch` properties

    author : Carsten Schelp
    license: GNU General Public License v3.0 (https://github.com/CarstenSchelp/CarstenSchelp.github.io/blob/master/LICENSE)
    """
    
    if x.size != y.size:
        raise ValueError("x and y must be the same size")
    
    cov = np.cov(x, y)
    
    if det(cov) == 0:
        pearson = -9999
        #pass # avoiding a matrix with a determinant of zero (in which case inverting it would lead to an error, see: https://www.statology.org/python-numpy-linalg-singular-matrix/)
    
    else:
    
        pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1,1])
        ell_radius_x = np.sqrt(1 + pearson)
        ell_radius_y = np.sqrt(1 - pearson)
        ellipse = Ellipse((0,0), width=ell_radius_x * 2, height=ell_radius_y * 2, **kwargs)
    
        # Considering number of standard deviations (n_std) where: sigmas 1s, 2s or 3s represent 68%, 95% and 99.7% rule respectively (https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule)
        n_std=1
    
        # calculating the stdandarddeviation of x from  the squareroot of the variance
        scale_x = np.sqrt(cov[0, 0]) * n_std
        mean_x = np.mean(x)
        
        # calculating the stdandarddeviation of y from  the squareroot of the variance
        scale_y = np.sqrt(cov[1, 1]) * n_std
        mean_y = np.mean(y)
        
        transf = transforms.Affine2D() \
            .rotate_deg(45) \
            .scale(scale_x, scale_y) \
            .translate(mean_x, mean_y)
            
        ellipse.set_transform(transf + ax.transData)
        
# =============================================================================
#         #Plot the ellipse
#         ax.add_patch(ellipse)
# =============================================================================
        
        #plt.show()       
    return pearson


#%% Function for creating a set of the timeseries of occurrence of extremes (extreme event 1 and extreme event 2) for plotting later on the same scatter graph
def set_of_timeseries_of_occurrence_of_two_extreme_events(timeseries_of_occurrence_of_extreme_event_1, timeseries_of_occurrence_of_extreme_event_2):
    """
    

    Parameters
    ----------
    timeseries_of_occurrence_of_extreme_event_1 : Array
    timeseries_of_occurrence_of_extreme_event_2 : Array

    Returns
    -------
    List of timeseries of occurrence of two extreme event to be used further to plot a scatter plot (extreme event 1 occurrence on x axis and extreme event 2 occurrence on y axis).

    """
    
    set_of_timeseries_of_occurrence_of_two_extreme_events= [timeseries_of_occurrence_of_extreme_event_1, timeseries_of_occurrence_of_extreme_event_2]
    
    return set_of_timeseries_of_occurrence_of_two_extreme_events

     


#%% Function for plotting the correleation of two extreme events using the Spearmans rank correlation coefficient
def plot_correlation_with_spearmans_rank_correlation_coefficient(gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events, event_1_name, event_2_name, gcm):
    """

    Parameters
    ----------
    spearmans_rank_correlation_coefficient_of_extreme_events : Tuple (containing pearson correlation coefficent - confidence ellipse patches)

    Returns
    -------
    Plot showing the correleation of two extreme events using the spearmans_rank correlation coefficient.

    """
    
    # Setting up 4 subplots as axis objects using Gridspec:
    gs = gridspec.GridSpec(2, 2, width_ratios=[3,1], height_ratios = [1,3])
    # Add space between scatter plot and KDE plots to accomodate axis labels
    gs.update(hspace =0.1, wspace =0.1)
    
    # Set background canvas color 
    fig = plt.figure(figsize = [8,6])
    fig.patch.set_facecolor('white')
    
    ax = plt.subplot(gs[1,0]) # Scatter plot area and axis range
    ax.set_xlabel('Percentage of area affected by {}'.format(event_1_name))
    ax.set_ylabel('Percentage of area affected by {}'.format(event_2_name))
    
    axu = plt.subplot(gs[0,0], sharex = ax) # Upper KDE plot area
    axu.get_xaxis().set_visible(False) # Hide the tick marks and spines
    axu.get_yaxis().set_visible(False)
    axu.spines['right'].set_visible(False)
    axu.spines['top'].set_visible(False)
    axu.spines['left'].set_visible(False)
    
    
    axr = plt.subplot(gs[1,1], sharey = ax) # Right KDE plot area
    axr.get_xaxis().set_visible(False) # Hide the tick marks and spines
    axr.get_yaxis().set_visible(False)
    axr.spines['right'].set_visible(False)
    axr.spines['top'].set_visible(False)
    axr.spines['bottom'].set_visible(False)
        
    axl = plt.subplot(gs[0,1]) # Legend plot area
    axl.axis('off') # Hide tick marks and spines
    
       
    
    # historical plot from 1861 until 1910 
    all_values_of_spearmans_rank_correlation_coefficient_from_1861_until_1910 = []
    for i in range(len(gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[0])):
                  
        timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = (gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[0][i][0].squeeze()) 
        timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = (gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[0][i][1].squeeze())  

        if (np.var(timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)).values <= np.array([0.000001]):
            continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses. 
        
        #pearson_from_1861_until_1910 = confidence_ellipse(timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910, timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910, ax, facecolor='none', edgecolor=(0.996, 0.89, 0.569))
        #all_values_of_pearson_from_1861_until_1910.append(pearson_from_1861_until_1910)
        
        # To calculate Spearman's Rank correlation coefficient
        spearmans_rank_correlation_coefficient_from_1861_until_1910 = spearmanr(timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910, timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)[0] # Spearman Rank correlation coefficient
        all_values_of_spearmans_rank_correlation_coefficient_from_1861_until_1910.append(spearmans_rank_correlation_coefficient_from_1861_until_1910)
        
        # Stacking the two extreme event datasets 
        combined_data= np.vstack([timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910, timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910])
        
        # Create a kernel density estimate
        kde_combined_data = stats.gaussian_kde(combined_data)
        
        # Define the grid for the contour plot
        xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
        ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
        xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
        zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
        
        # Plot the contours
        percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
        ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#FEE491')
       
      
        # marginal distribution using KDE method
        kde = stats.gaussian_kde(timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)
        xx = np.linspace(timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910.min(), timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910.max(), 1000)
        axu.plot(xx, kde(xx), color=(0.996, 0.89, 0.569))
        kde = stats. gaussian_kde(timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)
        yy = np.linspace(timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910.min(), timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910.max(), 1000)
        axr.plot(kde(yy), yy, color = (0.996, 0.89, 0.569))
            
    
    average_spearmans_rank_correlation_coefficient_from_1861_until_1910 = mean(all_values_of_spearmans_rank_correlation_coefficient_from_1861_until_1910) # Mean correlataion coefficient


    # historical plot from 1956 until 2005
    all_values_of_spearmans_rank_correlation_coefficient_from_1956_until_2005 = []
    for i in range(len(gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[1])):
            
        timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = (gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[1][i][0].squeeze())
        timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = (gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[1][i][1].squeeze())
        
        if  (np.var(timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)).values <= np.array([0.000001]):
            continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
        
        
        #pearson_from_1956_until_2005 = confidence_ellipse(timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005, timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005, ax, facecolor='none', edgecolor=(0.996, 0.769, 0.31))
        #all_values_of_pearson_from_1956_until_2005.append(pearson_from_1956_until_2005)
        
        # To calculate Spearman's Rank correlation coefficient
        spearmans_rank_correlation_coefficient_from_1956_until_2005 = spearmanr(timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005, timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)[0] # Spearman Rank correlation coefficient
        all_values_of_spearmans_rank_correlation_coefficient_from_1956_until_2005.append(spearmans_rank_correlation_coefficient_from_1956_until_2005)
               
        
        # Stacking the two extreme event datasets 
        combined_data= np.vstack([timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005, timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005])
        
        # Create a kernel density estimate
        kde_combined_data = stats.gaussian_kde(combined_data)
        
        # Define the grid for the contour plot
        xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
        ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
        xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
        zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
        
        # Plot the contours
        percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
        ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#FEC44F')
             

        #Points showing fraction affected per extreme event
        #ax.scatter(timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005, timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005, s=3, c='black')
        
        # distribution
        kde = stats.gaussian_kde(timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)
        xx = np.linspace(timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005.min(), timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005.max(), 1000)
        axu.plot(xx, kde(xx), color=(0.996, 0.769, 0.31))
        kde = stats. gaussian_kde(timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)
        yy = np.linspace(timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005.min(), timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005.max(), 1000)
        axr.plot(kde(yy), yy, color = (0.996, 0.769, 0.31))
        
     
    average_spearmans_rank_correlation_coefficient_from_1956_until_2005 = mean(all_values_of_spearmans_rank_correlation_coefficient_from_1956_until_2005) # Mean correlataion coefficient
    
    
    # projected scenarios....from 2050 until 2099
    # rcp26 plot
    all_values_of_spearmans_rank_correlation_coefficient_rcp26 = []
    for i in range(len(gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[2])):
    
        timeseries_of_occurrence_of_extreme_event_1_rcp26 = (gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[2][i][0].squeeze())
        timeseries_of_occurrence_of_extreme_event_2_rcp26 = (gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[2][i][1].squeeze())
        
        if  (np.var(timeseries_of_occurrence_of_extreme_event_1_rcp26)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_rcp26)).values <= np.array([0.000001]):
            continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
        
        #pearson_rcp26 = confidence_ellipse(timeseries_of_occurrence_of_extreme_event_1_rcp26, timeseries_of_occurrence_of_extreme_event_2_rcp26, ax, facecolor='none', edgecolor=(0.996, 0.6, 0.001))
        #all_values_of_pearson_rcp26.append(pearson_rcp26)
        
        # To calculate Spearman's Rank correlation coefficient
        spearmans_rank_correlation_coefficient_rcp26 = spearmanr(timeseries_of_occurrence_of_extreme_event_1_rcp26, timeseries_of_occurrence_of_extreme_event_2_rcp26)[0] # Spearman Rank correlation coefficient
        all_values_of_spearmans_rank_correlation_coefficient_rcp26.append(spearmans_rank_correlation_coefficient_rcp26)
          
        
        # Stacking the two extreme event datasets 
        combined_data= np.vstack([timeseries_of_occurrence_of_extreme_event_1_rcp26, timeseries_of_occurrence_of_extreme_event_2_rcp26])
        
        # Create a kernel density estimate
        kde_combined_data = stats.gaussian_kde(combined_data)
        
        # Define the grid for the contour plot
        xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
        ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
        xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
        zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
        
        # Plot the contours
        percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
        ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#FE9900')
        
        
        #Points showing fraction affected per extreme event
        #ax.scatter(timeseries_of_occurrence_of_extreme_event_1_rcp26, timeseries_of_occurrence_of_extreme_event_2_rcp26, s=3, c='navy')
        
        # distribution
        
        
        kde = stats.gaussian_kde(timeseries_of_occurrence_of_extreme_event_1_rcp26)
        xx = np.linspace(timeseries_of_occurrence_of_extreme_event_1_rcp26.min(), timeseries_of_occurrence_of_extreme_event_1_rcp26.max(), 1000)
        axu.plot(xx, kde(xx), color=(0.996, 0.6, 0.001))
        kde = stats. gaussian_kde(timeseries_of_occurrence_of_extreme_event_2_rcp26)
        yy = np.linspace(timeseries_of_occurrence_of_extreme_event_2_rcp26.min(), timeseries_of_occurrence_of_extreme_event_2_rcp26.max(), 1000)
        axr.plot(kde(yy), yy, color = (0.996, 0.6, 0.001))
        
    
    average_spearmans_rank_correlation_coefficient_rcp26 = mean(all_values_of_spearmans_rank_correlation_coefficient_rcp26) # Mean correlataion coefficient
        
    
    # rcp60 plot
    all_values_of_spearmans_rank_correlation_coefficient_rcp60 = []
    for i in range(len(gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[3])):
    
        timeseries_of_occurrence_of_extreme_event_1_rcp60 = (gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[3][i][0].squeeze())
        timeseries_of_occurrence_of_extreme_event_2_rcp60 = (gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[3][i][1].squeeze())
        
        if  (np.var(timeseries_of_occurrence_of_extreme_event_1_rcp60)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_rcp60)).values <= np.array([0.000001]):
            continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
        
        
        #pearson_rcp60 = confidence_ellipse(timeseries_of_occurrence_of_extreme_event_1_rcp60, timeseries_of_occurrence_of_extreme_event_2_rcp60, ax, facecolor='none', edgecolor=(0.851, 0.373, 0.0549))
        #all_values_of_pearson_rcp60.append(pearson_rcp60)
        
        # To calculate Spearman's Rank correlation coefficient
        spearmans_rank_correlation_coefficient_rcp60 = spearmanr(timeseries_of_occurrence_of_extreme_event_1_rcp60, timeseries_of_occurrence_of_extreme_event_2_rcp60)[0] # Spearman Rank correlation coefficient
        all_values_of_spearmans_rank_correlation_coefficient_rcp60.append(spearmans_rank_correlation_coefficient_rcp60)
          
        
        # Stacking the two extreme event datasets 
        combined_data= np.vstack([timeseries_of_occurrence_of_extreme_event_1_rcp60, timeseries_of_occurrence_of_extreme_event_2_rcp60])
        
        # Create a kernel density estimate
        kde_combined_data = stats.gaussian_kde(combined_data)
        
        # Define the grid for the contour plot
        xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
        ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
        xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
        zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
        
        # Plot the contours
        percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
        ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#D95F0E')
          
        
        #Points showing fraction affected per extreme event 
        #ax.scatter(timeseries_of_occurrence_of_extreme_event_1_rcp60, timeseries_of_occurrence_of_extreme_event_2_rcp60, s=3, c='red')
        
        # distribution
        kde = stats.gaussian_kde(timeseries_of_occurrence_of_extreme_event_1_rcp60)
        xx = np.linspace(timeseries_of_occurrence_of_extreme_event_1_rcp60.min(), timeseries_of_occurrence_of_extreme_event_1_rcp60.max(), 1000)
        axu.plot(xx, kde(xx), color=(0.851, 0.373, 0.0549))
        kde = stats. gaussian_kde(timeseries_of_occurrence_of_extreme_event_2_rcp60)
        yy = np.linspace(timeseries_of_occurrence_of_extreme_event_2_rcp60.min(), timeseries_of_occurrence_of_extreme_event_2_rcp60.max(), 1000)
        axr.plot(kde(yy), yy, color = (0.851, 0.373, 0.0549))
        
        
        #axl.legend(['early industrial: ' +'\u03C1'+ f'= {pearson_from_1861_until_1910:.3f}' ,'present-day: ' +'\u03C1'+ f'= {pearson_from_1956_until_2005:.3f}','rcp2.6: ' +'\u03C1'+ f'= {pearson_rcp26:.3f}', 'rcp6.0: ' +'\u03C1'+ f'= {pearson_rcp60:.3f}'], fontsize='small')
    
    average_spearmans_rank_correlation_coefficient_rcp60 = mean(all_values_of_spearmans_rank_correlation_coefficient_rcp60)  # Mean correlataion coefficient  
    
    legend_elements = [Line2D([0], [0], marker ='o', color= (0.996, 0.89, 0.569), markerfacecolor=(0.996, 0.89, 0.569), label= 'Early-industrial: ' +'\u03C1'+ f'= {average_spearmans_rank_correlation_coefficient_from_1861_until_1910:.2f}', markersize= 6) , Line2D([0], [0], marker ='o', color= (0.996, 0.769, 0.31), markerfacecolor=(0.996, 0.769, 0.31), label='Present day: ' +'\u03C1'+ f'= {average_spearmans_rank_correlation_coefficient_from_1956_until_2005:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.996, 0.6, 0.001), markerfacecolor=(0.996, 0.6, 0.001), label='RCP2.6: ' +'\u03C1'+ f'= {average_spearmans_rank_correlation_coefficient_rcp26:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.851, 0.373, 0.0549), markerfacecolor=(0.851, 0.373, 0.0549), label='RCP6.0: ' +'\u03C1'+ f'= {average_spearmans_rank_correlation_coefficient_rcp60:.2f}', markersize= 6)]
    
    axl.legend(handles = legend_elements, loc = 'center', fontsize = 8, frameon=False)
    
    #if len(gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events) == 5:
    if len(gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[4]) != 0:
        
        # rcp85 plot
        all_values_of_spearmans_rank_correlation_coefficient_rcp85 = []
        for i in range(len(gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[4])):
        
            timeseries_of_occurrence_of_extreme_event_1_rcp85 = (gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[4][i][0].squeeze())
            timeseries_of_occurrence_of_extreme_event_2_rcp85 = (gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events[4][i][1].squeeze())
            
            if  (np.var(timeseries_of_occurrence_of_extreme_event_1_rcp85)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_rcp85)).values <= np.array([0.000001]):
                continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
            
            #pearson_rcp85 = confidence_ellipse(timeseries_of_occurrence_of_extreme_event_1_rcp85, timeseries_of_occurrence_of_extreme_event_2_rcp85, ax, facecolor='none', edgecolor=(0.6, 0.204, 0.016))
            #all_values_of_pearson_rcp85.append(pearson_rcp85)
            
            # To calculate Spearman's Rank correlation coefficient
            spearmans_rank_correlation_coefficient_rcp85 = spearmanr(timeseries_of_occurrence_of_extreme_event_1_rcp85, timeseries_of_occurrence_of_extreme_event_2_rcp85)[0] # Spearman Rank correlation coefficient
            all_values_of_spearmans_rank_correlation_coefficient_rcp85.append(spearmans_rank_correlation_coefficient_rcp85)
            
            # Stacking the two extreme event datasets 
            combined_data= np.vstack([timeseries_of_occurrence_of_extreme_event_1_rcp85, timeseries_of_occurrence_of_extreme_event_2_rcp85])
            
            # Create a kernel density estimate
            kde_combined_data = stats.gaussian_kde(combined_data)
            
            # Define the grid for the contour plot
            xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
            ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
            xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
            zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
            
            # Plot the contours
            percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
            ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#993300')
              
            #Points showing fraction affected per extreme event
            #ax.scatter(timeseries_of_occurrence_of_extreme_event_1_rcp85, timeseries_of_occurrence_of_extreme_event_2_rcp85, s=3, c='darkred')
            
            # distribution          
            kde = stats.gaussian_kde(timeseries_of_occurrence_of_extreme_event_1_rcp85)
            xx = np.linspace(timeseries_of_occurrence_of_extreme_event_1_rcp85.min(), timeseries_of_occurrence_of_extreme_event_1_rcp85.max(), 1000)
            axu.plot(xx, kde(xx), color=(0.6, 0.204, 0.016))
            kde = stats. gaussian_kde(timeseries_of_occurrence_of_extreme_event_2_rcp85)
            yy = np.linspace(timeseries_of_occurrence_of_extreme_event_2_rcp85.min(), timeseries_of_occurrence_of_extreme_event_2_rcp85.max(), 1000)
            axr.plot(kde(yy), yy, color = (0.6, 0.204, 0.016))

            
        #if len(all_values_of_pearson_rcp85) 
        average_spearmans_rank_correlation_coefficient_rcp85 = mean(all_values_of_spearmans_rank_correlation_coefficient_rcp85)    
            
        #axl.legend(['early industrial: ' +'\u03C1'+ f'= {pearson_from_1861_until_1910:.3f}' ,'present-day: ' +'\u03C1'+ f'= {pearson_from_1956_until_2005:.3f}','rcp2.6: ' +'\u03C1'+ f'= {pearson_rcp26:.3f}', 'rcp6.0: ' +'\u03C1'+ f'= {pearson_rcp60:.3f}', 'rcp8.5: ' +'\u03C1'+ f'= {pearson_rcp85:.3f}'], fontsize='small')
        
        legend_elements = [Line2D([0], [0], marker ='o', color= (0.996, 0.89, 0.569), markerfacecolor=(0.996, 0.89, 0.569), label= 'Early-industrial: ' +'\u03C1'+ f'= {average_spearmans_rank_correlation_coefficient_from_1861_until_1910:.2f}', markersize= 6) , Line2D([0], [0], marker ='o', color= (0.996, 0.769, 0.31), markerfacecolor=(0.996, 0.769, 0.31), label='Present day: ' +'\u03C1'+ f'= {average_spearmans_rank_correlation_coefficient_from_1956_until_2005:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.996, 0.6, 0.001), markerfacecolor=(0.996, 0.6, 0.001), label='RCP2.6: ' +'\u03C1'+ f'= {average_spearmans_rank_correlation_coefficient_rcp26:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.851, 0.373, 0.0549), markerfacecolor=(0.851, 0.373, 0.0549), label='RCP6.0: ' +'\u03C1'+ f'= {average_spearmans_rank_correlation_coefficient_rcp60:.2f}', markersize= 6), Line2D([0], [0], marker ='o', color= (0.6, 0.204, 0.016), markerfacecolor=(0.6, 0.204, 0.016), label='RCP8.5: ' +'\u03C1'+ f'= {average_spearmans_rank_correlation_coefficient_rcp85:.2f}', markersize =6)]
        
        axl.legend(handles = legend_elements, loc = 'center', fontsize = 9, frameon=False)
    
    # incase you want the title on the plot
    #axu.set_title('Spearmans rank correlation coefficient considering fraction of region affected yearly by \n {} and {} \n '.format(event_1_name, event_2_name),fontsize=11)
    
    
    #plt.tight_layout()
    
    plt.gcf().text(0.12,0.03,'{}'.format(gcm), fontsize= 10)
    
    #plt.tight_layout()
    
    # Change this directory to save the plots to your desired directory
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/Correlation coefficient_{} and {} illustrating all impact models driven by {}.pdf'.format(event_1_name, event_2_name, gcm), dpi = 300, bbox_inches = 'tight')
    
    plt.show()
    plot_of_correlation = plt.show()   

    return plot_of_correlation         
                                                


#%% Function for plotting the correleation of two extreme events using the Spearmans rank correlation coefficient
def plot_correlation_with_spearmans_rank__correlation_coefficient_considering_scatter_points_from_all_impact_models(all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events, event_1_name, event_2_name, gcms):
    
    """

    Parameters
    ----------
    all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events : Tuple 
    event_1_name: String
    event_2_name: String
    gcms: List

    Returns
    -------
    Plot showing the correleation of two extreme events using the spearmans_rank correlation coefficient.

    """
    
    
    for gcm in range(len(all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events)):
        
        
        # Setting up 4 subplots as axis objects using Gridspec:
        gs = gridspec.GridSpec(2, 2, width_ratios=[3,1], height_ratios = [1,3])
        # Add space between scatter plot and KDE plots to accomodate axis labels
        gs.update(hspace =0.1, wspace =0.1)
        
        # Set background canvas color 
        fig = plt.figure(figsize = [8,6])
        fig.patch.set_facecolor('white')
        
        ax = plt.subplot(gs[1,0]) # Scatter plot area and axis range
        ax.set_xlabel('Percentage of area affected by {}'.format(event_1_name))
        ax.set_ylabel('Percentage of area affected by {}'.format(event_2_name))
        
        axu = plt.subplot(gs[0,0], sharex = ax) # Upper KDE plot area
        axu.get_xaxis().set_visible(False) # Hide the tick marks and spines
        axu.get_yaxis().set_visible(False)
        axu.spines['right'].set_visible(False)
        axu.spines['top'].set_visible(False)
        axu.spines['left'].set_visible(False)
        
        
        axr = plt.subplot(gs[1,1], sharey = ax) # Right KDE plot area
        axr.get_xaxis().set_visible(False) # Hide the tick marks and spines
        axr.get_yaxis().set_visible(False)
        axr.spines['right'].set_visible(False)
        axr.spines['top'].set_visible(False)
        axr.spines['bottom'].set_visible(False)
            
        axl = plt.subplot(gs[0,1]) # Legend plot area
        axl.axis('off') # Hide tick marks and spines
        
        
        
        # historical plot from 1861 until 1910 
        all_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = []
        all_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = []
        all_values_of_pearson_from_1861_until_1910 = []
        for i in range(len(all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][0])):
                      
            timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][0][i][0]
            timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][0][i][1]
            
            if (np.var(timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)).values <= np.array([0.000001]):
                continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses. 
            
            all_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910.append(timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910.squeeze())
            all_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910.append(timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910.squeeze())
            
                   
            
        # Append full list of all timeseries into one 1D array representing the full scatter point data of all impact models driven by the same GCM. **Also known as pulling data    
        full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)
        full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)
        
        #pearson_from_1861_until_1910 = confidence_ellipse(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910, ax, facecolor='none', edgecolor=(0.996, 0.89, 0.569))
        #all_values_of_pearson_from_1861_until_1910.append(pearson_from_1861_until_1910)
        
        # To calculate Spearman's Rank correlation coefficient
        spearmans_rank_correlation_coefficient_from_1861_until_1910 = spearmanr(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)[0] # Spearman Rank correlation coefficient
        
        # Stacking the two extreme event datasets 
        combined_data= np.vstack([full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910])
        
        # Create a kernel density estimate
        kde_combined_data = stats.gaussian_kde(combined_data)
        
        # Define the grid for the contour plot
        xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
        ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
        xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
        zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
        
        # Plot the contours
        percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
        ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#FEE491')
        
        
        # marginal distribution using KDE method    
        kde = stats.gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)
        xx = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910.max(), 1000)
        axu.plot(xx, kde(xx), color=(0.996, 0.89, 0.569))
        kde = stats. gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)
        yy = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910.max(), 1000)
        axr.plot(kde(yy), yy, color = (0.996, 0.89, 0.569))
        
        
        # Calculate the mean and variance of the individual extreme events and plot the values across the PDFs
        mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)
        variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)
        fig.text(0.05, 2.2, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910:.2f}", ha='left', va='top', transform=axu.transAxes, color= (0.996, 0.89, 0.569))
        mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)
        variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)
        fig.text(1.5, 0.20, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910:.2f}", ha='left', va='bottom', transform=axr.transAxes, color= (0.996, 0.89, 0.569)) 
         
  
        # historical plot from 1956 until 2005
        all_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = []
        all_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = []
        all_values_of_pearson_from_1956_until_2005 = []
        for i in range(len(all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][1])):
                
            timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][1][i][0]
            timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][1][i][1]
            
            if  (np.var(timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)).values <= np.array([0.000001]):
                continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
            
            all_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005.append(timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005.squeeze())
            all_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005.append(timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005.squeeze())
            
        
        # Append full list of all timeseries into one 1D array representing the full scatter point data of all impact models driven by the same GCM. **Also known as pulling data    
        full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)
        full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)
        
        # pearson_from_1956_until_2005 = confidence_ellipse(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005, ax, facecolor='none', edgecolor=(0.996, 0.769, 0.31))
        
        # To calculate Spearman's Rank correlation coefficient
        spearmans_rank_correlation_coefficient_from_1956_until_2005 = spearmanr(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)[0] # Spearman Rank correlation coefficient
        
        # Stacking the two extreme event datasets 
        combined_data= np.vstack([full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005])
        
        # Create a kernel density estimate
        kde_combined_data = stats.gaussian_kde(combined_data)
        
        # Define the grid for the contour plot
        xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
        ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
        xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
        zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
        
        # Plot the contours
        percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
        ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#FEC44F')
        
        
        # marginal distribution using KDE method 
        kde = stats.gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)
        xx = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005.max(), 1000)
        axu.plot(xx, kde(xx), color=(0.996, 0.769, 0.31))
        kde = stats. gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)
        yy = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005.max(), 1000)
        axr.plot(kde(yy), yy, color = (0.996, 0.769, 0.31))
             
        # Calculate the mean and variance of the individual extreme events and plot the values across the PDFs
        mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)
        variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)
        fig.text(0.05, 2.0, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005:.2f}", ha='left', va='top', transform=axu.transAxes, color= (0.996, 0.769, 0.31))
        mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)
        variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)
        fig.text(1.5, 0.15, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005:.2f}", ha='left', va='bottom', transform=axr.transAxes, color= (0.996, 0.769, 0.31)) 
        
        
        # projected scenarios....from 2050 until 2099
        # rcp26 plot
        all_timeseries_of_occurrence_of_extreme_event_1_rcp26 = []
        all_timeseries_of_occurrence_of_extreme_event_2_rcp26 = []
        
        all_values_of_pearson_rcp26 = []
        for i in range(len(all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][2])):
        
            timeseries_of_occurrence_of_extreme_event_1_rcp26 = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][2][i][0]
            timeseries_of_occurrence_of_extreme_event_2_rcp26 = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][2][i][1]
            
            if  (np.var(timeseries_of_occurrence_of_extreme_event_1_rcp26)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_rcp26)).values <= np.array([0.000001]):
                continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
            
            all_timeseries_of_occurrence_of_extreme_event_1_rcp26.append(timeseries_of_occurrence_of_extreme_event_1_rcp26.squeeze())
            all_timeseries_of_occurrence_of_extreme_event_2_rcp26.append(timeseries_of_occurrence_of_extreme_event_2_rcp26.squeeze())
            

        
        # Append full list of all timeseries into one 1D array representing the full scatter point data of all impact models driven by the same GCM. **Also known as pulling data    
        full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_1_rcp26)
        full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_2_rcp26)
        
        # pearson_rcp26 = confidence_ellipse(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26, ax, facecolor='none', edgecolor=(0.996, 0.6, 0.001))
        
        # To calculate Spearman's Rank correlation coefficient
        spearmans_rank_correlation_coefficient_rcp26 = spearmanr(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26)[0] # Spearman Rank correlation coefficient
        
        
        # Stacking the two extreme event datasets 
        combined_data= np.vstack([full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26])
        
        # Create a kernel density estimate
        kde_combined_data = stats.gaussian_kde(combined_data)
        
        # Define the grid for the contour plot
        xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
        ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
        xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
        zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
        
        # Plot the contours
        percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
        ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#FE9900')
  

        # marginal distribution using KDE method
        kde = stats.gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26)
        xx = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26.max(), 1000)
        axu.plot(xx, kde(xx), color=(0.996, 0.6, 0.001))
        kde = stats. gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26)
        yy = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26.max(), 1000)
        axr.plot(kde(yy), yy, color = (0.996, 0.6, 0.001))
        
        # Calculate the mean and variance of the individual extreme events and plot the values across the PDFs
        mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26)
        variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26)
        fig.text(0.05, 1.8, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26:.2f}", ha='left', va='top', transform=axu.transAxes, color= (0.996, 0.6, 0.001))
        mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26)
        variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26)
        fig.text(1.5, 0.10, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26:.2f}", ha='left', va='bottom', transform=axr.transAxes, color= (0.996, 0.6, 0.001)) 
            
        
        # rcp60 plot
        all_timeseries_of_occurrence_of_extreme_event_1_rcp60 = []
        all_timeseries_of_occurrence_of_extreme_event_2_rcp60 = []
        
        all_values_of_pearson_rcp60 = []
        for i in range(len(all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][3])):
        
            timeseries_of_occurrence_of_extreme_event_1_rcp60 = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][3][i][0]
            timeseries_of_occurrence_of_extreme_event_2_rcp60 = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][3][i][1]
            
            if  (np.var(timeseries_of_occurrence_of_extreme_event_1_rcp60)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_rcp60)).values <= np.array([0.000001]):
                continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
            
            all_timeseries_of_occurrence_of_extreme_event_1_rcp60.append(timeseries_of_occurrence_of_extreme_event_1_rcp60.squeeze())
            all_timeseries_of_occurrence_of_extreme_event_2_rcp60.append(timeseries_of_occurrence_of_extreme_event_2_rcp60.squeeze())
            

        full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_1_rcp60)
        full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_2_rcp60)
        
        #pearson_rcp60 = confidence_ellipse(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60, ax, facecolor='none', edgecolor=(0.851, 0.373, 0.0549))
        
        # To calculate Spearman's Rank correlation coefficient
        spearmans_rank_correlation_coefficient_rcp60 = spearmanr(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60)[0] # Spearman Rank correlation coefficient
        
        # Stacking the two extreme event datasets 
        combined_data= np.vstack([full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60])
        
        # Create a kernel density estimate
        kde_combined_data = stats.gaussian_kde(combined_data)
        
        # Define the grid for the contour plot
        xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
        ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
        xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
        zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
        
        # Plot the contours
        percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
        ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#D95F0E')
       
        
        # marginal distribution using KDE method
        kde = stats.gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60)
        xx = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60.max(), 1000)
        axu.plot(xx, kde(xx), color=(0.851, 0.373, 0.0549))
        kde = stats. gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60)
        yy = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60.max(), 1000)
        axr.plot(kde(yy), yy, color = (0.851, 0.373, 0.0549))
        
        # Calculate the mean and variance of the individual extreme events and plot the values across the PDFs
        mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60)
        variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60)
        fig.text(0.05, 1.6, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60:.2f}", ha='left', va='top', transform=axu.transAxes, color= (0.851, 0.373, 0.0549))
        mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60)
        variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60)
        fig.text(1.5, 0.05, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60:.2f}", ha='left', va='bottom', transform=axr.transAxes, color= (0.851, 0.373, 0.0549)) 
        
        ax.set_xlim(-1, full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60.max()+9)
        ax.set_ylim(-1, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60.max()+9)
        #ax.set_aspect('auto')
        
        legend_elements = [Line2D([0], [0], marker ='o', color= (0.996, 0.89, 0.569), markerfacecolor=(0.996, 0.89, 0.569), label= 'Early-industrial: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_from_1861_until_1910:.2f}', markersize= 6) , Line2D([0], [0], marker ='o', color= (0.996, 0.769, 0.31), markerfacecolor=(0.996, 0.769, 0.31), label='Present day: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_from_1956_until_2005:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.996, 0.6, 0.001), markerfacecolor=(0.996, 0.6, 0.001), label='RCP2.6: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_rcp26:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.851, 0.373, 0.0549), markerfacecolor=(0.851, 0.373, 0.0549), label='RCP6.0: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_rcp60:.2f}', markersize= 6)]
        
        axl.legend(handles = legend_elements, loc = 'center', fontsize = 8, frameon=False)
        
        #if len(gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events) == 5:
        if len(all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][4]) != 0:
            
            # rcp85 plot
            all_timeseries_of_occurrence_of_extreme_event_1_rcp85 = []
            all_timeseries_of_occurrence_of_extreme_event_2_rcp85 = []
            
            all_values_of_pearson_rcp85 = []
            for i in range(len(all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][4])):
            
                timeseries_of_occurrence_of_extreme_event_1_rcp85 = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][4][i][0]
                timeseries_of_occurrence_of_extreme_event_2_rcp85 = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm][4][i][1]
                
                if  (np.var(timeseries_of_occurrence_of_extreme_event_1_rcp85)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_rcp85)).values <= np.array([0.000001]):
                    continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
                
                all_timeseries_of_occurrence_of_extreme_event_1_rcp85.append(timeseries_of_occurrence_of_extreme_event_1_rcp85.squeeze())
                all_timeseries_of_occurrence_of_extreme_event_2_rcp85.append(timeseries_of_occurrence_of_extreme_event_2_rcp85.squeeze())
                
            
            full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_1_rcp85)
            full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_2_rcp85)
            
            # pearson_rcp85 = confidence_ellipse(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85, ax, facecolor='none', edgecolor=(0.6, 0.204, 0.016))
            
            # To calculate Spearman's Rank correlation coefficient
            spearmans_rank_correlation_coefficient_rcp85 = spearmanr(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85)[0] # Spearman Rank correlation coefficient
            
            # Stacking the two extreme event datasets 
            combined_data= np.vstack([full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85])
            
            # Create a kernel density estimate
            kde_combined_data = stats.gaussian_kde(combined_data)
            
            # Define the grid for the contour plot
            xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
            ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
            xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
            zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
            
            # Plot the contours
            percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
            ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#993300')
           
           
            # marginal distribution using KDE method
            kde = stats.gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85)
            xx = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85.max(), 1000)
            axu.plot(xx, kde(xx), color=(0.6, 0.204, 0.016))
            kde = stats. gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85)
            yy = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85.max(), 1000)
            axr.plot(kde(yy), yy, color = (0.6, 0.204, 0.016))
                
            # Calculate the mean and variance of the individual extreme events 
            mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85)
            variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85)
            fig.text(0.05, 1.4, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85:.2f}", ha='left', va='top', transform=axu.transAxes, color= (0.6, 0.204, 0.016))
            mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85)
            variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85)
            fig.text(1.5, 0, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85:.2f}", ha='left', va='bottom', transform=axr.transAxes, color= (0.6, 0.204, 0.016))

            ax.set_xlim(-1, full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85.max()+9)
            ax.set_ylim(-1, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85.max()+9)
            #ax.set_aspect('auto')
            
            legend_elements = [Line2D([0], [0], marker ='o', color= (0.996, 0.89, 0.569), markerfacecolor=(0.996, 0.89, 0.569), label= 'Early-industrial: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_from_1861_until_1910:.2f}', markersize= 6) , Line2D([0], [0], marker ='o', color= (0.996, 0.769, 0.31), markerfacecolor=(0.996, 0.769, 0.31), label='Present day: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_from_1956_until_2005:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.996, 0.6, 0.001), markerfacecolor=(0.996, 0.6, 0.001), label='RCP2.6: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_rcp26:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.851, 0.373, 0.0549), markerfacecolor=(0.851, 0.373, 0.0549), label='RCP6.0: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_rcp60:.2f}', markersize= 6), Line2D([0], [0], marker ='o', color= (0.6, 0.204, 0.016), markerfacecolor=(0.6, 0.204, 0.016), label='RCP8.5: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_rcp85:.2f}', markersize =6)]
            
            axl.legend(handles = legend_elements, loc = 'center', fontsize = 9, frameon=False)
        
        
        # incase you want the title on the plot
        #axu.set_title('Spearmans rank correlation coefficient considering fraction of region affected yearly by \n {} and {} \n '.format(event_1_name, event_2_name),fontsize=11)
        

        #plt.tight_layout()
        
        # driving GCM noted on the plot
        plt.gcf().text(0.12,0.03,'considering all impact models driven by {}'.format(gcms[gcm]), fontsize= 10)
        
        #plt.tight_layout()
        
        # Change this directory to save the plots to your desired directory
        plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/Correlation coefficient_{} and {} considering all impact models driven by {}.pdf'.format(event_1_name, event_2_name, gcms[gcm]), dpi = 300, bbox_inches = 'tight')
        
        plt.show()
        plot_of_correlation = plt.show()   
    
    
    return plot_of_correlation         
                                
    

#%% Function for plotting the correleation of two extreme events using the Spearman's rank correlation coefficient using all the GCM data 
def plot_correlation_with_spearmans_rank_correlation_coefficient_considering_scatter_points_from_all_impact_models_and_all_gcms(all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events, event_1_name, event_2_name):
    
    """
    Parameters
    -------
    all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events: Tuple 
    event_1_name: String
    event_2_name: String

    Returns
    -------
    Plot showing the correleation of two extreme events using the spearmans_rank correlation coefficient considering all impact models and all their driving GCMs.

    """
     
    
    all_data_considering_all_gcms = [[],[],[],[],[]] #To enable sorting all the data from all the GCMS into their respective scenarios: i.e early industrial, present day, rcp 2.6, ...etc
    for gcm in range(len(all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events)):
        gcm_data = all_gcms_full_set_of_timeseries_of_occurrence_of_two_extreme_events[gcm]
        for scenario in range(len(gcm_data)):
            data_per_gcm = gcm_data[scenario]
            all_data_considering_all_gcms[scenario].extend(data_per_gcm)
    
    # Setting up 4 subplots as axis objects using Gridspec:
    gs = gridspec.GridSpec(2, 2, width_ratios=[3,1], height_ratios = [1,3])
    # Add space between scatter plot and KDE plots to accomodate axis labels
    gs.update(hspace =0.1, wspace =0.1)
    
    # Set background canvas color 
    fig = plt.figure(figsize = [8,6])
    fig.patch.set_facecolor('white')
    
    ax = plt.subplot(gs[1,0]) # Scatter plot area and axis range
    ax.set_xlabel('Percentage of area affected by {}'.format(event_1_name))
    ax.set_ylabel('Percentage of area affected by {}'.format(event_2_name))
    
    axu = plt.subplot(gs[0,0], sharex = ax) # Upper KDE plot area
    axu.get_xaxis().set_visible(False) # Hide the tick marks and spines
    axu.get_yaxis().set_visible(False)
    axu.spines['right'].set_visible(False)
    axu.spines['top'].set_visible(False)
    axu.spines['left'].set_visible(False)
    
    
    axr = plt.subplot(gs[1,1], sharey = ax) # Right KDE plot area
    axr.get_xaxis().set_visible(False) # Hide the tick marks and spines
    axr.get_yaxis().set_visible(False)
    axr.spines['right'].set_visible(False)
    axr.spines['top'].set_visible(False)
    axr.spines['bottom'].set_visible(False)
        
    axl = plt.subplot(gs[0,1]) # Legend plot area
    axl.axis('off') # Hide tick marks and spines
    
    
    
    # historical plot from 1861 until 1910 
    all_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = []
    all_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = []
      
    for compound_event in range(len(all_data_considering_all_gcms[0])): 
        timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = all_data_considering_all_gcms[0][compound_event][0]
        timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = all_data_considering_all_gcms[0][compound_event][1]
        
        if (np.var(timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)).values <= np.array([0.000001]):
            continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses. 
        
        all_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910.append(timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910.squeeze())
        all_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910.append(timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910.squeeze())
        
        
    # Append full list of all timeseries into one 1D array representing the full scatter point data of all impact models driven by the same GCM. **Also known as pulling data    
    full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)
    full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)
    
    #pearson_from_1861_until_1910 = confidence_ellipse(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910, ax, facecolor='none', edgecolor=(0.996, 0.89, 0.569))
    
    # To calculate Spearman's Rank correlation coefficient
    spearmans_rank_correlation_coefficient_from_1861_until_1910 = spearmanr(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)[0] # Spearman Rank correlation coefficient
    
    # Stacking the two extreme event datasets 
    combined_data= np.vstack([full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910])
    
    # Create a kernel density estimate
    kde_combined_data = stats.gaussian_kde(combined_data)
    
    # Define the grid for the contour plot
    xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
    ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
    xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
    zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
    

    # Plot the contours 
    percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
    ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#FEE491')
    
  
    # marginal distribution using KDE method    
    kde = stats.gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)
    xx = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910.max(), 1000)
    axu.plot(xx, kde(xx), color=(0.996, 0.89, 0.569))
    kde = stats. gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)
    yy = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910.max(), 1000)
    axr.plot(kde(yy), yy, color = (0.996, 0.89, 0.569))
    
    # Calculate the mean and variance of the individual extreme events and plot the values across the PDFs
    mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)
    variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910)
    fig.text(0.05, 2.2, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1861_until_1910:.2f}", ha='left', va='top', transform=axu.transAxes, color= (0.996, 0.89, 0.569))
    mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)
    variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910)
    fig.text(1.5, 0.20, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1861_until_1910:.2f}", ha='left', va='bottom', transform=axr.transAxes, color= (0.996, 0.89, 0.569)) 
      
    

    # historical plot from 1956 until 2005
    all_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = []
    all_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = []

    for compound_event in range(len(all_data_considering_all_gcms[1])): 
        timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = all_data_considering_all_gcms[1][compound_event][0]
        timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = all_data_considering_all_gcms[1][compound_event][1]
    
        if  (np.var(timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)).values <= np.array([0.000001]):
            continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
        
        all_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005.append(timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005.squeeze())
        all_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005.append(timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005.squeeze())
        

    # Append full list of all timeseries into one 1D array representing the full scatter point data of all impact models driven by the same GCM. **Also known as pulling data    
    full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)
    full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)
    
    # To calculate pearsons correlation coefficient 
    #pearson_from_1956_until_2005 = confidence_ellipse(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005, ax, facecolor='none', edgecolor=(0.996, 0.769, 0.31))
    
    # To calculate Spearman's Rank correlation coefficient 
    spearmans_rank_correlation_coefficient_from_1956_until_2005 = spearmanr(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)[0] # Spearman's Rank correlation coefficient
    
    # Stacking the two extreme event datasets 
    combined_data= np.vstack([full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005, full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005])
    
    # Create a kernel density estimate
    kde_combined_data = stats.gaussian_kde(combined_data)
    
    # Define the grid for the contour plot
    xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
    ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
    xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
    zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
    
    
    # Plot the contours
    percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
    ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#FEC44F')
    
        
    # marginal distribution using KDE method 
    kde = stats.gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)
    xx = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005.max(), 1000)
    axu.plot(xx, kde(xx), color=(0.996, 0.769, 0.31))
    kde = stats. gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)
    yy = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005.max(), 1000)
    axr.plot(kde(yy), yy, color = (0.996, 0.769, 0.31))
 
    # Calculate the mean and variance of the individual extreme events and plot the values across the PDFs
    mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)
    variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005)
    fig.text(0.05, 2.0, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_from_1956_until_2005:.2f}", ha='left', va='top', transform=axu.transAxes, color= (0.996, 0.769, 0.31))
    mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)
    variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005)
    fig.text(1.5, 0.15, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_from_1956_until_2005:.2f}", ha='left', va='bottom', transform=axr.transAxes, color= (0.996, 0.769, 0.31)) 
    
    
    # projected scenarios....from 2050 until 2099
    # rcp26 plot
    all_timeseries_of_occurrence_of_extreme_event_1_rcp26 = []
    all_timeseries_of_occurrence_of_extreme_event_2_rcp26 = []

    for compound_event in range(len(all_data_considering_all_gcms[2])): 
        timeseries_of_occurrence_of_extreme_event_1_rcp26 = all_data_considering_all_gcms[2][compound_event][0]
        timeseries_of_occurrence_of_extreme_event_2_rcp26 = all_data_considering_all_gcms[2][compound_event][1]
        
        if  (np.var(timeseries_of_occurrence_of_extreme_event_1_rcp26)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_rcp26)).values <= np.array([0.000001]):
            continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
        
        all_timeseries_of_occurrence_of_extreme_event_1_rcp26.append(timeseries_of_occurrence_of_extreme_event_1_rcp26.squeeze())
        all_timeseries_of_occurrence_of_extreme_event_2_rcp26.append(timeseries_of_occurrence_of_extreme_event_2_rcp26.squeeze())
        
    
    # Append full list of all timeseries into one 1D array representing the full scatter point data of all impact models driven by the same GCM. **Also known as pulling data    
    full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_1_rcp26)
    full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_2_rcp26)
    
    # To calculate pearsons correlation coefficient
    # pearson_rcp26 = confidence_ellipse(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26, ax, facecolor='none', edgecolor=(0.996, 0.6, 0.001))
    
    # To calculate Spearman's Rank correlation coefficient 
    spearmans_rank_correlation_coefficient_rcp26 = spearmanr(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26)[0] # Spearman's Rank correlation coefficient
    
    
    # Stacking the two extreme event datasets 
    combined_data= np.vstack([full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26])
    
    # Create a kernel density estimate
    kde_combined_data = stats.gaussian_kde(combined_data)
    
    # Define the grid for the contour plot
    xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
    ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
    xi, yi = np.mgrid[xmin-5:xmax+10:200j, ymin-5:ymax+10:200j]
    zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
    
   
    # Plot the contours
    percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
    ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#FE9900')
    
    
    # marginal distribution using KDE method
    kde = stats.gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26)
    xx = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26.max(), 1000)
    axu.plot(xx, kde(xx), color=(0.996, 0.6, 0.001))
    kde = stats. gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26)
    yy = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26.max(), 1000)
    axr.plot(kde(yy), yy, color = (0.996, 0.6, 0.001))
    
    #average_pearson_rcp26 = mean(all_values_of_pearson_rcp26) # Mean Pearson's correlataion coefficient
        
    # Calculate the mean and variance of the individual extreme events and plot the values across the PDFs
    mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26)
    variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26)
    fig.text(0.05, 1.8, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp26:.2f}", ha='left', va='top', transform=axu.transAxes, color= (0.996, 0.6, 0.001))
    mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26)
    variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26)
    fig.text(1.5, 0.10, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26:.2f}", ha='left', va='bottom', transform=axr.transAxes, color= (0.996, 0.6, 0.001)) 
    
    
    # rcp60 plot
    all_timeseries_of_occurrence_of_extreme_event_1_rcp60 = []
    all_timeseries_of_occurrence_of_extreme_event_2_rcp60 = [] 

    for compound_event in range(len(all_data_considering_all_gcms[3])): 
        timeseries_of_occurrence_of_extreme_event_1_rcp60 = all_data_considering_all_gcms[3][compound_event][0]
        timeseries_of_occurrence_of_extreme_event_2_rcp60 = all_data_considering_all_gcms[3][compound_event][1]
    
        if  (np.var(timeseries_of_occurrence_of_extreme_event_1_rcp60)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_rcp60)).values <= np.array([0.000001]):
            continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
        
        all_timeseries_of_occurrence_of_extreme_event_1_rcp60.append(timeseries_of_occurrence_of_extreme_event_1_rcp60.squeeze())
        all_timeseries_of_occurrence_of_extreme_event_2_rcp60.append(timeseries_of_occurrence_of_extreme_event_2_rcp60.squeeze())
        
        
    full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_1_rcp60)
    full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_2_rcp60)
    
    # To calculate pearsons corelation coefficient
    # pearson_rcp60 = confidence_ellipse(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60, ax, facecolor='none', edgecolor=(0.85, 0.37, 0.05))
    
    # To calculate Spearman's Rank correlation coefficient 
    spearmans_rank_correlation_coefficient_rcp60 = spearmanr(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp26)[0] # Spearman's Rank correlation coefficient
    
    
    # Stacking the two extreme event datasets 
    combined_data= np.vstack([full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60])
    
    # Create a kernel density estimate
    kde_combined_data = stats.gaussian_kde(combined_data)
    
    # Define the grid for the contour plot
    xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
    ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
    xi, yi = np.mgrid[xmin-4:xmax+10:200j, ymin-5:ymax+10:200j]
    zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
    
    
    # Plot the contours
    percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
    ax.contour(xi, yi, zi.reshape(xi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#D95F0E')
    
   
    # marginal distribution using KDE method
    kde = stats.gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60)
    xx = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60.max(), 1000)
    axu.plot(xx, kde(xx), color=(0.851, 0.373, 0.0549))
    kde = stats. gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60)
    yy = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60.max(), 1000)
    axr.plot(kde(yy), yy, color = (0.851, 0.373, 0.0549))
        

    # Calculate the mean and variance of the individual extreme events and plot the values across the PDFs
    mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60)
    variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60)
    fig.text(0.05, 1.6, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60:.2f}", ha='left', va='top', transform=axu.transAxes, color= (0.851, 0.373, 0.0549))
    mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60)
    variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60)
    fig.text(1.5, 0.05, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60:.2f}", ha='left', va='bottom', transform=axr.transAxes, color= (0.851, 0.373, 0.0549)) 
    
    ax.set_xlim(-2, full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp60.max()+9)
    ax.set_ylim(-2, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp60.max()+9)
    #ax.set_aspect('auto')
    
    legend_elements = [Line2D([0], [0], marker ='o', color= (0.996, 0.89, 0.569), markerfacecolor=(0.996, 0.89, 0.569), label= 'Early-industrial: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_from_1861_until_1910:.2f}', markersize= 6) , Line2D([0], [0], marker ='o', color= (0.996, 0.769, 0.31), markerfacecolor=(0.996, 0.769, 0.31), label='Present day: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_from_1956_until_2005:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.996, 0.6, 0.001), markerfacecolor=(0.996, 0.6, 0.001), label='RCP2.6: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_rcp26:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.851, 0.373, 0.0549), markerfacecolor=(0.851, 0.373, 0.0549), label='RCP6.0: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_rcp60:.2f}', markersize= 6)]
    
    axl.legend(handles = legend_elements, loc = 'center', fontsize = 10, frameon=False)
    
    #if len(gcm_full_set_of_timeseries_of_occurrence_of_two_extreme_events) == 5:
    if len(all_data_considering_all_gcms[4]) != 0: # checking for an empty list, incase of crop failure data that doesnt have the rcp 8.5 scenario
        
        # rcp85 plot
        all_timeseries_of_occurrence_of_extreme_event_1_rcp85 = []
        all_timeseries_of_occurrence_of_extreme_event_2_rcp85 = []
        
        for compound_event in range(len(all_data_considering_all_gcms[4])): 
            timeseries_of_occurrence_of_extreme_event_1_rcp85 = all_data_considering_all_gcms[4][compound_event][0]
            timeseries_of_occurrence_of_extreme_event_2_rcp85 = all_data_considering_all_gcms[4][compound_event][1]
        
            if  (np.var(timeseries_of_occurrence_of_extreme_event_1_rcp85)).values <= np.array([0.000001]) or (np.var(timeseries_of_occurrence_of_extreme_event_2_rcp85)).values <= np.array([0.000001]):
                continue  # avoid datasets without variation in time in the bivariate distribution plot and plotting of the ellipses
            
            all_timeseries_of_occurrence_of_extreme_event_1_rcp85.append(timeseries_of_occurrence_of_extreme_event_1_rcp85.squeeze())
            all_timeseries_of_occurrence_of_extreme_event_2_rcp85.append(timeseries_of_occurrence_of_extreme_event_2_rcp85.squeeze())
            
        
        full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_1_rcp85)
        full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85 = np.concatenate(all_timeseries_of_occurrence_of_extreme_event_2_rcp85)
        
        # To calculate the pearsons correlation coefficient
        # pearson_rcp85 = confidence_ellipse(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85, ax, facecolor='none', edgecolor=(0.60, 0.20, 0.016))
        
        # To calculate Spearman's Rank correlation coefficient 
        spearmans_rank_correlation_coefficient_rcp85 = spearmanr(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85)[0] # Spearman's Rank correlation coefficient
        
        # Stacking the two extreme event datasets 
        combined_data= np.vstack([full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85])
        
        # Create a kernel density estimate
        kde_combined_data = stats.gaussian_kde(combined_data)
        
        # Define the grid for the contour plot
        xmin, xmax = np.min(combined_data[0]), np.max(combined_data[0])
        ymin, ymax = np.min(combined_data[1]), np.max(combined_data[1])
        xi, yi = np.mgrid[xmin-4:xmax+10:200j, ymin-5:ymax+10:200j]
        zi = kde_combined_data(np.vstack([xi.flatten(), yi.flatten()]))
        
        
        # Plot the contours
        percentile_68 = np.percentile(zi, 68) # This contour line will enclose approximately 68% of the values of the data in zi, allowing you to visualize the region by representing observations within one standard deviation (σ) to either side of the mean (μ). 
        ax.contour(xi, yi, zi.reshape(yi.shape), levels = [percentile_68], linestyles = 'dashed', colors='#993300')
        
    
       
        # marginal distribution using KDE method 
        kde = stats.gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85)
        xx = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85.max(), 1000)
        axu.plot(xx, kde(xx), color=(0.6, 0.204, 0.016))
        kde = stats. gaussian_kde(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85)
        yy = np.linspace(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85.min(), full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85.max(), 1000)
        axr.plot(kde(yy), yy, color = (0.6, 0.204, 0.016))
        
        # Calculate the mean and variance of the individual extreme events 
        mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85)
        variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85)
        fig.text(0.05, 1.4, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85:.2f}", ha='left', va='top', transform=axu.transAxes, color= (0.6, 0.204, 0.016))
        mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85 = np.mean(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85)
        variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85 = np.var(full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85)
        fig.text(1.5, 0, f"x\u0304: {mean_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85:.2f}, \u03C3\u00B2: {variance_full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85:.2f}", ha='left', va='bottom', transform=axr.transAxes, color= (0.6, 0.204, 0.016))

        ax.set_xlim(-1, full_scatter_timeseries_of_occurrence_of_extreme_event_1_rcp85.max()+9)
        ax.set_ylim(-1, full_scatter_timeseries_of_occurrence_of_extreme_event_2_rcp85.max()+9)
        #ax.set_aspect('auto')
                
        legend_elements = [Line2D([0], [0], marker ='o', color= (0.996, 0.89, 0.569), markerfacecolor=(0.996, 0.89, 0.569), label= 'Early-industrial: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_from_1861_until_1910:.2f}', markersize= 6) , Line2D([0], [0], marker ='o', color= (0.996, 0.769, 0.31), markerfacecolor=(0.996, 0.769, 0.31), label='Present day: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_from_1956_until_2005:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.996, 0.6, 0.001), markerfacecolor=(0.996, 0.6, 0.001), label='RCP2.6: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_rcp26:.2f}', markersize = 6), Line2D([0], [0], marker ='o', color= (0.851, 0.373, 0.0549), markerfacecolor=(0.851, 0.373, 0.0549), label='RCP6.0: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_rcp60:.2f}', markersize= 6), Line2D([0], [0], marker ='o', color= (0.6, 0.204, 0.016), markerfacecolor=(0.6, 0.204, 0.016), label='RCP8.5: ' +'\u03C1'+ f'= {spearmans_rank_correlation_coefficient_rcp85:.2f}', markersize =6)]
        
        
        axl.legend(handles = legend_elements, loc = 'center', fontsize = 10, frameon=False)
    
    # incase you require a title on the plot
    #axu.set_title('Spearmans rank correlation coefficient considering fraction of region affected yearly by \n {} and {} \n '.format(event_1_name, event_2_name),fontsize=11)
    
    #plt.tight_layout()
    
    # Change this directory to save the plots to your desired directory
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/Correlation coefficient_{} and {} considering impact models and driving GCMs.pdf'.format(event_1_name, event_2_name), dpi = 300, bbox_inches = 'tight')
    
    plt.show()
    plot_of_correlation = plt.show()   

    return plot_of_correlation




#%% Function for calculating the Probability of occurrence of an extreme event in one grid cell over the entire dataset period
def probability_of_occurrence_of_extreme_event(extreme_event_occurrence):
    
    """ Determine the probability of occurrence of an extreme climate event over the entire dataset time period
    
    Parameters
    ----------
    extreme_event_occurrence : Xarray data array (boolean with true for locations with the occurrence of an extreme event within the same year)
    
    Returns
    -------
    Probability of occurrence of an extreme climate event over the entire dataset time period (Xarray)
    
    """
    # Number of years with extreme event occurrence  
    no_of_years_with_extreme_event_occurrence = (extreme_event_occurrence).sum('time',skipna=False)
    
    # Total number of years in the dataset
    total_no_of_years_in_dataset = len(extreme_event_occurrence)
    
    # Probability of occurrence
    probability_of_occurrence_of_the_extreme_event = no_of_years_with_extreme_event_occurrence/total_no_of_years_in_dataset
    
    
    return probability_of_occurrence_of_the_extreme_event


#%% Function for plotting map showing the Probability Ratio (PR) of occurrence of an extreme event in one grid cell over the entire dataset period
def plot_probability_ratio_of_occurrence_of_an_extreme_event(probability_of_occurrence_of_extreme_event_in_new_scenario, probability_of_occurrence_of_same_extreme_event_in_a_past_scenario, event_name, time_period, gcm, scenario):
    
    """ Plot a map showing the probability of occurrence of an extreme climate event over the entire dataset time period
    
    Parameters
    ----------
    probability_of_occurrence_of_extreme_event_in_new_scenario : Xarray data array (probability of occurrence of an extreme event over the entire dataset time period in new scenario)
    probability_of_occurrence_of_same_extreme_event_in_a_past_scenario : Xarray data array (probability of occurrence of the same extreme event over the entire dataset time period in historical early industrial time period / reference dataset)
    event_name : String (Extreme Events)
    gcm : String (Driving GCM)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the Probability Ratio (PR) of joint occurrence of two extreme climate events to compare the change in new scenario from the historical early industrial times
    
    """
    
    
    # Probability Ratio (PR) of occurrence: ratio between probability of occurrence of an extreme event in new situation (scenario) to the probability of occurrence in the reference situtaion (historical early industrial times)
    probability_of_ratio_of_occurrence_of_an_extreme_event = probability_of_occurrence_of_extreme_event_in_new_scenario/probability_of_occurrence_of_same_extreme_event_in_a_past_scenario
    
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    #grid= ax.gridlines(draw_labels = True, dms = True )
    #grid.top_labels = False #Removes the grid labels from the top of the plot
    #grid.right_labels= False #Removes the grid labels from the right of the plot
    
    boundaries = [1, 2, 4, 6, 8, 10]
    n_colors = 6
    cmap = sns.color_palette('YlOrRd', n_colors=n_colors) # set color palette

    cmap = mcolors.ListedColormap(['cornflowerblue']+ [cmap[0]] + [cmap[1]]+ [cmap[2]]+ [cmap[3]] + [cmap[4]]+ ['darkred']) # manually select colors from palette
    norm = mcolors.BoundaryNorm(boundaries=boundaries, ncolors=len(boundaries)+1, extend='both')
    cbar = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
  
    formatter = ticker.FuncFormatter(lambda x, pos: str(Fraction(x).limit_denominator()) if x <= 1 else '{:.0f}'.format(x)) # convert the floats to fractions for those less than 1 and to integers for those above one in the color bar legend

    
    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(probability_of_ratio_of_occurrence_of_an_extreme_event, origin = 'upper' , extent=map_extent, cmap = cmap, norm= norm)
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.25,0.85,'Comparing {} ({}) to 1861-1910'.format(time_period, scenario), fontsize = 8)
    plt.gcf().text(0.15,0.03,'{}'.format(gcm), fontsize= 8)
    
    # Add the title and legend to the figure and show the figure
    plt.title('Probability Ratio of Occurrence of {} \n asssuming change in joint occurrence due to changes only in these events \n'.format(event_name),fontsize=10) #Plot title
    
    # discrete color bar legend
    #bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
    colorbar = plt.colorbar(cbar, orientation = 'horizontal', extend='both', shrink =0.5, extendfrac= 'auto', ticks=boundaries, format=formatter)
    colorbar.set_label(label = 'Probability Ratio', size = 9) #Plots the legend color bar
    
    plt.clim(0,10)
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels
    plt.show()
    #plt.close()
    
    return probability_of_ratio_of_occurrence_of_an_extreme_event


#%% Function for plotting map showing the Probability Ratio (PR) of occurrence of an extreme event in one grid cell over the entire dataset period
def plot_probability_ratio_of_occurrence_of_an_extreme_event_considering_all_gcms(probability_of_occurrence_of_extreme_event_in_new_scenario, probability_of_occurrence_of_same_extreme_event_in_a_past_scenario, event_name, second_event_name, time_period, scenario):
    
    """ Plot a map showing the probability of occurrence of an extreme climate event over the entire dataset time period
    
    Parameters
    ----------
    probability_of_occurrence_of_extreme_event_in_new_scenario : Xarray data array (probability of occurrence of an extreme event over the entire dataset time period in new scenario)
    probability_of_occurrence_of_same_extreme_event_in_a_past_scenario : Xarray data array (probability of occurrence of the same extreme event over the entire dataset time period in historical early industrial time period / reference dataset)
    event_name and second_event_name : String (Extreme Events)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the Probability Ratio (PR) of joint occurrence of two extreme climate events to compare the change in new scenario from the historical early industrial times
    
    """
    
    
    # Probability Ratio (PR) of occurrence: ratio between probability of occurrence of an extreme event in new situation (scenario) to the probability of occurrence in the reference situtaion (historical early industrial times)
    probability_of_ratio_of_occurrence_of_an_extreme_event = probability_of_occurrence_of_extreme_event_in_new_scenario/probability_of_occurrence_of_same_extreme_event_in_a_past_scenario
    
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    #grid= ax.gridlines(draw_labels = True, dms = True )
    #grid.top_labels = False #Removes the grid labels from the top of the plot
    #grid.right_labels= False #Removes the grid labels from the right of the plot
    
    # Set the legend limits  
    boundaries = [1, 2, 4, 6, 8, 10]
    n_colors = 6
    cmap = sns.color_palette('YlOrRd', n_colors=n_colors) # set color palette

    cmap = mcolors.ListedColormap(['cornflowerblue']+ [cmap[0]] + [cmap[1]]+ [cmap[2]]+ [cmap[3]] + [cmap[4]]+ ['darkred']) # manually select colors from palette
    norm = mcolors.BoundaryNorm(boundaries=boundaries, ncolors=len(boundaries)+1, extend='both')
    cbar = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
  
    formatter = ticker.FuncFormatter(lambda x, pos: str(Fraction(x).limit_denominator()) if x <= 1 else '{:.0f}'.format(x)) # convert the floats to fractions for those less than 1 and to integers for those above one in the color bar legend

    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(probability_of_ratio_of_occurrence_of_an_extreme_event, origin = 'upper' , extent=map_extent, cmap = cmap, norm= norm)
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.25,0.85,'Comparing {} ({}) to 1861-1910'.format(time_period, scenario), fontsize = 9)
    
    
    # Add the title and legend to the figure and show the figure
    #plt.title('Probability Ratio of Occurrence of {} \n asssuming change in joint occurrence due to changes only in these events \n'.format(event_name),fontsize=10) #Plot title
    
    
    colorbar = plt.colorbar(cbar, orientation = 'horizontal', extend='both', shrink =0.5, extendfrac= 'auto', ticks=boundaries, format=formatter)
    colorbar.set_label(label = 'Probability Ratio', size = 9) #Plots the legend color bar
    
    plt.clim(0,10)
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels
    
    # Change this directory to save the plots to your desired directory
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/PR_{} asssuming the change in cooccurrence with {} is due to changes only in {} with {} as the future scenario.pdf'.format(event_name, second_event_name, event_name, scenario), dpi = 300)
    
    plt.show()
    #plt.close()
    
    return probability_of_ratio_of_occurrence_of_an_extreme_event




#%% Function for plotting map showing the Probability Ratio (PR) of occurrence of an extreme event in one grid cell over the entire dataset period
def plot_probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only(probability_of_occurrence_of_extreme_event_1_in_new_scenario, probability_of_occurrence_of_extreme_event_2_in_new_scenario, probability_of_occurrence_of_extreme_event_1_in_a_past_scenario, probability_of_occurrence_of_extreme_event_2_in_a_past_scenario, probability_ratio_of_occurrence_of_the_compound_events_in_new_scenario, probability_ratio_of_occurrence_of_the_compound_events_in_a_past_scenario, event_1_name, event_2_name, time_period, gcm, scenario):
    
    """ Plot a map showing the probability of occurrence of a compound extreme climate event over the entire dataset time period
    
    Parameters
    ----------
    probability_of_occurrence_of_extreme_event_1_in_new_scenario : Xarray data array (probability of occurrence of extreme event 1 over the entire dataset time period in new scenario)
    probability_of_occurrence_of_extreme_event_2_in_new_scenario : Xarray data array (probability of occurrence of extreme event 2 over the entire dataset time period in new scenario)
    probability_of_occurrence_of_extreme_event_1_in_a_past_scenario : Xarray data array (probability_of_occurrence_of_extreme_event_1_in_a_past_scenario over the entire dataset time period in historical early industrial time period)
    probability_of_occurrence_of_extreme_event_2_in_a_past_scenario : Xarray data array (probability_of_occurrence_of_extreme_event_2_in_a_past_scenario over the entire dataset time period in historical early industrial time period)
    probability_ratio_of_occurrence_of_the_compound_events_in_new_scenario : Xarray data array
    probability_ratio_of_occurrence_of_the_compound_events_in_a_past_scenario : Xarray data array
    
    event_1_name, event_2_name : String (Extreme Events)
    gcm : String (Driving GCM)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the Probability Ratio (PR) of joint occurrence of two extreme climate events assuming DEPENDENCE ONLY to compare the change in new scenario from the historical early industrial times
    
    """
    
    
    # Probability Ratio (PR) of occurrence: ratio between probability of occurrence of compound event in new situation (scenario) to the probability of occurrence in the reference situtaion (historical early industrial times)
    probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only = ((probability_ratio_of_occurrence_of_the_compound_events_in_new_scenario/(probability_of_occurrence_of_extreme_event_1_in_new_scenario * probability_of_occurrence_of_extreme_event_2_in_new_scenario))/(probability_ratio_of_occurrence_of_the_compound_events_in_a_past_scenario/(probability_of_occurrence_of_extreme_event_1_in_a_past_scenario * probability_of_occurrence_of_extreme_event_2_in_a_past_scenario)))
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    #grid= ax.gridlines(draw_labels = True, dms = True )
    #grid.top_labels = False #Removes the grid labels from the top of the plot
    #grid.right_labels= False #Removes the grid labels from the right of the plot
    
    # Set the legend limits  
    boundaries = [1, 2, 4, 6, 8, 10]
    n_colors = 6
    cmap = sns.color_palette('YlOrRd', n_colors=n_colors) # set color palette

    cmap = mcolors.ListedColormap(['cornflowerblue']+ [cmap[0]] + [cmap[1]]+ [cmap[2]]+ [cmap[3]] + [cmap[4]]+ ['darkred']) # manually select colors from palette
    norm = mcolors.BoundaryNorm(boundaries=boundaries, ncolors=len(boundaries)+1, extend='both')
    cbar = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
  
    formatter = ticker.FuncFormatter(lambda x, pos: str(Fraction(x).limit_denominator()) if x <= 1 else '{:.0f}'.format(x)) # convert the floats to fractions for those less than 1 and to integers for those above one in the color bar legend

    
    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only, origin = 'upper' , extent=map_extent, cmap = cmap, norm= norm)
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.25,0.85,'Comparing {} ({}) to 1861-1910'.format(time_period, scenario), fontsize = 8)
    plt.gcf().text(0.15,0.03,'{}'.format(gcm), fontsize= 8)
    
    # Add the title and legend to the figure and show the figure
    plt.title('Probability Ratio of Joint Occurrence of {} and {} \n assuming only change in dependence \n'.format(event_1_name, event_2_name),fontsize=10) #Plot title
    
    # discrete color bar legend
    #bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
    
    colorbar = plt.colorbar(cbar, orientation = 'horizontal', extend='both',shrink =0.5, extendfrac= 'auto', ticks=boundaries, format=formatter)#Plots the legend color bar
    colorbar.set_label(label = 'Probability Ratio', size = 9)
    
    plt.clim(0,10)
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels
    plt.show()
    #plt.close()
    
    return probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only


#%% Function for plotting map showing the Probability Ratio (PR) of occurrence of an extreme event in one grid cell over the entire dataset period
def plot_probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only_considering_all_gcms(probability_of_occurrence_of_extreme_event_1_in_new_scenario, probability_of_occurrence_of_extreme_event_2_in_new_scenario, probability_of_occurrence_of_extreme_event_1_in_a_past_scenario, probability_of_occurrence_of_extreme_event_2_in_a_past_scenario, probability_ratio_of_occurrence_of_the_compound_events_in_new_scenario, probability_ratio_of_occurrence_of_the_compound_events_in_a_past_scenario, event_1_name, event_2_name, time_period, scenario):
    
    """ Plot a map showing the probability of occurrence of a compound extreme climate event over the entire dataset time period
    
    Parameters
    ----------
    probability_of_occurrence_of_extreme_event_1_in_new_scenario : Xarray data array (probability of occurrence of extreme event 1 over the entire dataset time period in new scenario)
    probability_of_occurrence_of_extreme_event_2_in_new_scenario : Xarray data array (probability of occurrence of extreme event 2 over the entire dataset time period in new scenario)
    probability_of_occurrence_of_extreme_event_1_in_a_past_scenario : Xarray data array (probability_of_occurrence_of_extreme_event_1_in_a_past_scenario over the entire dataset time period in historical early industrial time period)
    probability_of_occurrence_of_extreme_event_2_in_a_past_scenario : Xarray data array (probability_of_occurrence_of_extreme_event_2_in_a_past_scenario over the entire dataset time period in historical early industrial time period)
    probability_ratio_of_occurrence_of_the_compound_events_in_new_scenario : Xarray data array
    probability_ratio_of_occurrence_of_the_compound_events_in_a_past_scenario : Xarray data array
    
    event_1_name, event_2_name : String (Extreme Events)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the Probability Ratio (PR) of joint occurrence of two extreme climate events assuming DEPENDENCE ONLY to compare the change in new scenario from the historical early industrial times
    
    """
    
    
    # Probability Ratio (PR) of occurrence: ratio between probability of occurrence of compound event in new situation (scenario) to the probability of occurrence in the reference situtaion (historical early industrial times)
    probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only = ((probability_ratio_of_occurrence_of_the_compound_events_in_new_scenario/(probability_of_occurrence_of_extreme_event_1_in_new_scenario * probability_of_occurrence_of_extreme_event_2_in_new_scenario))/(probability_ratio_of_occurrence_of_the_compound_events_in_a_past_scenario/(probability_of_occurrence_of_extreme_event_1_in_a_past_scenario * probability_of_occurrence_of_extreme_event_2_in_a_past_scenario)))
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    #grid= ax.gridlines(draw_labels = True, dms = True )
    #grid.top_labels = False #Removes the grid labels from the top of the plot
    #grid.right_labels= False #Removes the grid labels from the right of the plot
    
    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    # Set the legend limits   
    boundaries = [1, 2, 4, 6, 8, 10]
    n_colors = 6
    cmap = sns.color_palette('YlOrRd', n_colors=n_colors) # set color palette

    cmap = mcolors.ListedColormap(['cornflowerblue']+ [cmap[0]] + [cmap[1]]+ [cmap[2]]+ [cmap[3]] + [cmap[4]]+ ['darkred']) # manually select colors from palette
    norm = mcolors.BoundaryNorm(boundaries=boundaries, ncolors=len(boundaries)+1, extend='both')
    cbar = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
  
    formatter = ticker.FuncFormatter(lambda x, pos: str(Fraction(x).limit_denominator()) if x <= 1 else '{:.0f}'.format(x)) # convert the floats to fractions for those less than 1 and to integers for those above one in the color bar legend
    
    plt.imshow(probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only, origin = 'upper' , extent=map_extent, cmap = cmap, norm= norm)
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.25,0.85,'Comparing {} ({}) to 1861-1910'.format(time_period, scenario), fontsize = 9)
    
    # Add the title and legend to the figure and show the figure
    #plt.title('Probability Ratio of Joint Occurrence of {} and {} \n assuming only change in dependence \n'.format(event_1_name, event_2_name),fontsize=10) #Plot title
    

    plt.clim(0,10)
    
    #Plots the legend color bar
    colorbar = plt.colorbar(cbar, orientation = 'horizontal', extend='both', shrink =0.5, extendfrac= 'auto', ticks=boundaries, format=formatter)
    colorbar.set_label(label = 'Probability Ratio', size = 9)
    
 
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels
    
    # Change this directory to save the plots to your desired directory
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/PR of cooccurrence of {} and {} assuming only change in their dependence with {} as the future secenario.pdf'.format(event_1_name, event_2_name, scenario), dpi = 300)
    
    plt.show()
    #plt.close()
    
    return probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only

#%%
def plot_percentage_contribution_of_probability_ratio_of_occurrence_of_an_extreme_event(percentage_contribution_of_ratio_of_occurrence_of_an_extreme_event, event_1_name, event_2_name, time_period, gcm, scenario):
    
    """ Plot a map showing the contribution of event to probability of joint occurrence of extreme climate events over the entire dataset time period
    
    Parameters
    ----------
    percentage_contribution_of_ratio_of_occurrence_of_an_extreme_event : Xarray data array 
    event_name : String (Extreme Events)
    gcm : String (Driving GCM)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the contribution of event to probability of joint occurrence of extreme climate events over the entire dataset time period
    
    """
    
    percentage_contribution_of_ratio_of_occurrence_of_an_extreme_event
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    #grid= ax.gridlines(draw_labels = True, dms = True )
    #grid.top_labels = False #Removes the grid labels from the top of the plot
    #grid.right_labels= False #Removes the grid labels from the right of the plot
    
    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(percentage_contribution_of_ratio_of_occurrence_of_an_extreme_event, origin = 'upper' , extent=map_extent, cmap = plt.cm.get_cmap('YlOrRd', 10))
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.25,0.85,'Comparing {} ({}) to 1861-1910'.format(time_period, scenario), fontsize = 8)
    plt.gcf().text(0.15,0.03,'{}'.format(gcm), fontsize= 10)
    
    # Add the title and legend to the figure and show the figure
    plt.title('Percentage contribution of occurrence of {} \n to the Joint Occurrence of {} and {} \n'.format(event_1_name, event_1_name, event_2_name),fontsize=10) #Plot title
    
    # discrete color bar legend
    #bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
    plt.colorbar( orientation = 'horizontal', extend ='both', shrink =0.5).set_label(label = 'Contribution to Joint Occurrence (%)', size = 9) #Plots the legend color bar
    plt.clim(0,100)
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels
    plt.show()
    #plt.close()
    
    return percentage_contribution_of_ratio_of_occurrence_of_an_extreme_event


#%%
def plot_percentage_contribution_of_probability_ratio_of_occurrence_of_two_extreme_events_in_dependence(percentage_contribution_of_probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only, event_1_name, event_2_name, time_period, gcm, scenario):
    
    """ Plot a map showing the contribution of event to probability of joint occurrence of extreme climate events over the entire dataset time period
    
    Parameters
    ----------
    percentage_contribution_of_probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only : Xarray data array 
    event_name : String (Extreme Events)
    gcm : String (Driving GCM)
    time_period: String
    scenario: String
    
    Returns
    -------
    Plot (Figure) showing the contribution of event to probability of joint occurrence of extreme climate events over the entire dataset time period
    
    """
    
    percentage_contribution_of_probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only
    
    # Setting the projection of the map to cylindrical / Mercator
    ax = plt.axes(projection=ccrs.PlateCarree())
    
    # Add the background map to the plot
    #ax.stock_img()
    
    # Set the extent of the plot, in this case the East African Region
    ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
    
    # Plot the coastlines along the continents on the map
    ax.coastlines(color='dimgrey', linewidth=0.7)
    
    # Plot features: lakes, rivers and boarders
    ax.add_feature(cfeature.LAKES, alpha =0.5)
    ax.add_feature(cfeature.RIVERS)
    ax.add_feature(cfeature.OCEAN)
    ax.add_feature(cfeature.LAND, facecolor ='lightgrey')
    #ax.add_feature(cfeature.BORDERS, linestyle=':')
    
     # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    ax.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) 
    ax.set_yticks([90, 60, 30, 0, -30, -60], crs=ccrs.PlateCarree())
    lon_formatter = LongitudeFormatter()
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)  
    
    
    # Plot the gridlines for the coordinate system on the map
    #grid= ax.gridlines(draw_labels = True, dms = True )
    #grid.top_labels = False #Removes the grid labels from the top of the plot
    #grid.right_labels= False #Removes the grid labels from the right of the plot
    
    # Plot probability of occurrence of compound extreme events per location with the extent of the East African Region; Specified as (left, right, bottom, right)
    plt.imshow(percentage_contribution_of_probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only, origin = 'upper' , extent=map_extent, cmap = plt.cm.get_cmap('YlOrRd', 10))
    
    # Text outside the plot to display the time period & scenario (top-right) and the two Global Impact Models used (bottom left)
    plt.gcf().text(0.25,0.85,'Comparing {} ({}) to 1861-1910'.format(time_period, scenario), fontsize = 8)
    plt.gcf().text(0.15,0.03,'{}'.format(gcm), fontsize= 8)
    
    # Add the title and legend to the figure and show the figure
    plt.title('Contribution to Joint Occurrence assuming only change in dependence of \n {} and {} \n'.format(event_1_name, event_2_name),fontsize=10) #Plot title
    
    # discrete color bar legend
    #bounds = [0, 0.2, 0.4, 0.6, 0.8, 1.0]
    plt.colorbar( orientation = 'horizontal', extend='both', shrink =0.5).set_label(label = 'Contribution to Joint Occurrence (%)', size = 9) #Plots the legend color bar
    plt.clim(0,100)
    plt.xticks(fontsize=8) # color and size of longitude labels
    plt.yticks(fontsize=8) # color and size of latitude labels
    plt.show()
    #plt.close()
    
    return percentage_contribution_of_probability_ratio_of_occurrence_of_two_extreme_events_assuming_dependence_only



#%% Plotting Probability Ratios
def plot_probability_ratios(
    average_pr_for_event_1, 
    average_pr_for_event_2, 
    average_pr_for_compound_events, 
    compound_events_names, 
    selected_indices
):
    """
    Plot a map showing the probability ratios for selected compound events across different scenarios.
    
    Parameters
    ----------
    average_pr_for_event_1 : List of Xarray DataArrays
        Probability ratios for Event 1 across all scenarios.
    average_pr_for_event_2 : List of Xarray DataArrays
        Probability ratios for Event 2 across all scenarios.
    average_pr_for_compound_events : List of Xarray DataArrays
        Probability ratios for the compound events across all scenarios.
    compound_events_names : List of Strings
        Names of the compound events.
    selected_indices : List of integers
        Indices of the selected compound events to plot.
    event_names : List of tuples
        Tuples containing the names of the individual events in each pair.
    
    Returns
    -------
    None
    """
     
    # Scenarios for the plots
    scenarios = ['Present Day', 'RCP2.6', 'RCP6.0', 'RCP8.5']
    
    # Generate subplot labels
    subplot_labels = 'abcdefghijklmnopqrstuvwxyz'
    
    # Mapping event names to acronyms
    event_acronyms = {
        'floodedarea': 'RF',
        'driedarea': 'DR',
        'heatwavedarea': 'HW',
        'cropfailedarea': 'CF',
        'burntarea': 'WF',
        'tropicalcyclonedarea': 'TC'
    }
    
    list_of_compound_event_acronyms = []
    for compound_event in compound_events_names:
        event_1_name = event_acronyms.get(compound_event[0])
        event_2_name = event_acronyms.get(compound_event[1])
        compound_event_acronyms = '{} & {}'.format(event_1_name, event_2_name)
        list_of_compound_event_acronyms.append(compound_event_acronyms)
    
    selected_compound_events_names = [list_of_compound_event_acronyms[i] for i in selected_indices]
    
    for scenario_idx, scenario_name in enumerate(scenarios):
        fig, axs = plt.subplots(len(selected_indices), 3, figsize=(15, 10), subplot_kw={'projection': ccrs.PlateCarree()})
        axs = axs.reshape(len(selected_indices), 3)  # Reshape axs to 2D array for easy indexing
        
        for row_idx, index in enumerate(selected_indices):
            
            compound_event_name = selected_compound_events_names[row_idx]
            # Split the compound event name into individual event names
            event_1_name, event_2_name = compound_event_name.split(" & ")
            
            data = [
                average_pr_for_event_1[scenario_idx][index],
                average_pr_for_event_2[scenario_idx][index],
                average_pr_for_compound_events[scenario_idx][index]
            ]
            

            # Define more divisions between 0 and 1
            boundaries = [0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8, 10]
            
            # Create a custom colormap
            # Blue shades for values between 0.1 and 1
            blues = sns.color_palette("Blues", n_colors=6)[::-1]  # 5 colors from dark to light blue
            # YlOrRd colors for values from 1 to 10
            ylorrd = sns.color_palette('YlOrRd', n_colors=6)  # Adjust this if you want more gradation
            
            # Combine the colors: light blue at 0.1, light blue at 1, then YlOrRd from 1 onwards
            colors = blues + ylorrd
            
            # Create the colormap
            cmap = mcolors.ListedColormap(colors)
            
            # Normalize based on the new boundaries
            norm = mcolors.BoundaryNorm(boundaries=boundaries, ncolors=13, extend='both')
            
            
            
            for col_idx in range(3):
                ax = axs[row_idx, col_idx]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                #ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                if scenario_name == 'RCP8.5' and ('CF' in [event_1_name, event_2_name]):
                    ax.add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
                else:
                    ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                ax.spines['geo'].set_visible(False)
                
                plot = ax.imshow(
                    data[col_idx],
                    origin='lower',
                    extent=[-180, 180, 90, -60],
                    cmap=cmap,
                    norm=norm
                )
                
                
                # Titles personalized with actual event names
                title_event_1 = f"Δ {event_1_name}"
                title_event_2 = f"Δ {event_2_name}"
                title_compound = "Δ Dependence"
                
                titles = [title_event_1, title_event_2, title_compound]
                
                # Set titles for every row
                ax.set_title(titles[col_idx], fontsize=10)
                
                # Add subplot label
                ax.text(0.02, 1.1, subplot_labels[row_idx * 3 + col_idx], transform=ax.transAxes, fontsize=9, va='top', ha='left')
                
            # Add row labels for the compound event names
            axs[row_idx, 0].text(-0.2, 0.5, compound_event_name, transform=axs[row_idx, 0].transAxes, fontsize=10,
                                 va='center', ha='right', rotation=90, color='black')
        
        fig.subplots_adjust(wspace=0.1, hspace=0)
        cbar_ax = fig.add_axes([0.39, 0.1, 0.25, 0.02])
        cbar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax, orientation='horizontal', extend='both')
        # Set the ticks explicitly
        cbar.set_ticks(boundaries)
        cbar.set_ticklabels([str(b) for b in boundaries])
        cbar.ax.tick_params(labelsize=9)
        cbar.set_label(label='Probability Ratio', size=9)
        
        fig.suptitle(f'Probability Ratios considering occurrence in {scenario_name} and Early-industrial scenarios', fontsize=12)
        plt.savefig(f'C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/probability_ratios_main_selection_{scenario_name}_wrt_EI.pdf', dpi=300)
        
        plt.show()
     
    return data

#%%
def plot_probability_ratios_for_second_selected_events(
    average_pr_for_event_1, 
    average_pr_for_event_2, 
    average_pr_for_compound_events, 
    compound_events_names, 
    selected_indices
):
    """
    Plot a map showing the probability ratios for selected compound events across different scenarios.
    
    Parameters
    ----------
    average_pr_for_event_1 : List of Xarray DataArrays
        Probability ratios for Event 1 across all scenarios.
    average_pr_for_event_2 : List of Xarray DataArrays
        Probability ratios for Event 2 across all scenarios.
    average_pr_for_compound_events : List of Xarray DataArrays
        Probability ratios for the compound events across all scenarios.
    compound_events_names : List of Strings
        Names of the compound events.
    selected_indices : List of integers
        Indices of the selected compound events to plot.
    event_names : List of tuples
        Tuples containing the names of the individual events in each pair.
    
    Returns
    -------
    None
    """
     
    # Scenarios for the plots
    scenarios = ['Present Day', 'RCP2.6', 'RCP6.0', 'RCP8.5']
    
    # Generate subplot labels
    subplot_labels = 'abcdefghijklmnopqrstuvwxyz'
    
    # Mapping event names to acronyms
    event_acronyms = {
        'floodedarea': 'RF',
        'driedarea': 'DR',
        'heatwavedarea': 'HW',
        'cropfailedarea': 'CF',
        'burntarea': 'WF',
        'tropicalcyclonedarea': 'TC'
    }
    
    list_of_compound_event_acronyms = []
    for compound_event in compound_events_names:
        event_1_name = event_acronyms.get(compound_event[0])
        event_2_name = event_acronyms.get(compound_event[1])
        compound_event_acronyms = '{} & {}'.format(event_1_name, event_2_name)
        list_of_compound_event_acronyms.append(compound_event_acronyms)
    
    selected_compound_events_names = [list_of_compound_event_acronyms[i] for i in selected_indices]
    
    for scenario_idx, scenario_name in enumerate(scenarios):
        fig, axs = plt.subplots(len(selected_indices), 3, figsize=(15, 10), subplot_kw={'projection': ccrs.PlateCarree()})
        axs = axs.reshape(len(selected_indices), 3)  # Reshape axs to 2D array for easy indexing
        
        for row_idx, index in enumerate(selected_indices):
            
            compound_event_name = selected_compound_events_names[row_idx]
            # Split the compound event name into individual event names
            event_1_name, event_2_name = compound_event_name.split(" & ")
            
            data = [
                average_pr_for_event_1[scenario_idx][index],
                average_pr_for_event_2[scenario_idx][index],
                average_pr_for_compound_events[scenario_idx][index]
            ]
            

            # Define more divisions between 0 and 1
            boundaries = [0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8, 10]
            
            # Create a custom colormap
            # Blue shades for values between 0.1 and 1
            blues = sns.color_palette("Blues", n_colors=6)[::-1]  # 5 colors from dark to light blue
            # YlOrRd colors for values from 1 to 10
            ylorrd = sns.color_palette('YlOrRd', n_colors=6)  # Adjust this if you want more gradation
            
            # Combine the colors: light blue at 0.1, light blue at 1, then YlOrRd from 1 onwards
            colors = blues + ylorrd
            
            # Create the colormap
            cmap = mcolors.ListedColormap(colors)
            
            # Normalize based on the new boundaries
            norm = mcolors.BoundaryNorm(boundaries=boundaries, ncolors=13, extend='both')
            
            
            
            for col_idx in range(3):
                ax = axs[row_idx, col_idx]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                #ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                if scenario_name == 'RCP8.5' and ('CF' in [event_1_name, event_2_name]):
                    ax.add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
                else:
                    ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                ax.spines['geo'].set_visible(False)
                
                plot = ax.imshow(
                    data[col_idx],
                    origin='lower',
                    extent=[-180, 180, 90, -60],
                    cmap=cmap,
                    norm=norm
                )
                
                
                # Titles personalized with actual event names
                title_event_1 = f"Δ {event_1_name}"
                title_event_2 = f"Δ {event_2_name}"
                title_compound = "Δ Dependence"
                
                titles = [title_event_1, title_event_2, title_compound]
                
                # Set titles for every row
                ax.set_title(titles[col_idx], fontsize=10)
                
                # Add subplot label
                ax.text(0.02, 1.1, subplot_labels[row_idx * 3 + col_idx], transform=ax.transAxes, fontsize=9, va='top', ha='left')
                
            # Add row labels for the compound event names
            axs[row_idx, 0].text(-0.2, 0.5, compound_event_name, transform=axs[row_idx, 0].transAxes, fontsize=10,
                                 va='center', ha='right', rotation=90, color='black')
        
        fig.subplots_adjust(wspace=0.1, hspace=-0.5)
        cbar_ax = fig.add_axes([0.39, 0.2, 0.25, 0.02])
        cbar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax, orientation='horizontal', extend='both')
        # Set the ticks explicitly
        cbar.set_ticks(boundaries)
        cbar.set_ticklabels([str(b) for b in boundaries])
        cbar.ax.tick_params(labelsize=9)
        cbar.set_label(label='Probability Ratio', size=9)
        
        fig.suptitle(f'Probability Ratios considering occurrence in {scenario_name} and Early-industrial scenarios', fontsize=12)
        plt.savefig(f'C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/probability_ratios_second_selection_{scenario_name}_wrt_EI.pdf', dpi=300)
        
        plt.show()
     
    return data


#%% PR with hatches
def plot_probability_ratios_with_hatches(
    average_pr_for_event_1, 
    average_pr_for_event_2, 
    average_pr_for_compound_events, 
    compound_events_names, 
    selected_indices,
    average_inf_for_compound_events
):
    """
    Plot a map showing the probability ratios for selected compound events across different scenarios.
    
    Parameters
    ----------
    average_pr_for_event_1 : List of Xarray DataArrays
        Probability ratios for Event 1 across all scenarios.
    average_pr_for_event_2 : List of Xarray DataArrays
        Probability ratios for Event 2 across all scenarios.
    average_pr_for_compound_events : List of Xarray DataArrays
        Probability ratios for the compound events across all scenarios.
    average_inf_for_event_1 : List of Xarray DataArrays
        Inf mask for Event 1 across all scenarios.
    average_inf_for_event_2 : List of Xarray DataArrays
        Inf mask for Event 2 across all scenarios.
    average_inf_for_compound_events : List of Xarray DataArrays
        Inf mask for the compound events across all scenarios.
    compound_events_names : List of Strings
        Names of the compound events.
    selected_indices : List of integers
        Indices of the selected compound events to plot.
    
    Returns
    -------
    None
    """
     
    # Scenarios for the plots
    scenarios = ['Present Day', 'RCP2.6', 'RCP6.0', 'RCP8.5']
    
    # Generate subplot labels
    subplot_labels = 'abcdefghijklmnopqrstuvwxyz'
    
    # Mapping event names to acronyms
    event_acronyms = {
        'floodedarea': 'RF',
        'driedarea': 'DR',
        'heatwavedarea': 'HW',
        'cropfailedarea': 'CF',
        'burntarea': 'WF',
        'tropicalcyclonedarea': 'TC'
    }
    
    list_of_compound_event_acronyms = []
    for compound_event in compound_events_names:
        event_1_name = event_acronyms.get(compound_event[0])
        event_2_name = event_acronyms.get(compound_event[1])
        compound_event_acronyms = '{} & {}'.format(event_1_name, event_2_name)
        list_of_compound_event_acronyms.append(compound_event_acronyms)
    
    selected_compound_events_names = [list_of_compound_event_acronyms[i] for i in selected_indices]
    
    for scenario_idx, scenario_name in enumerate(scenarios):
        fig, axs = plt.subplots(len(selected_indices), 3, figsize=(15, 10), subplot_kw={'projection': ccrs.PlateCarree()})
        axs = axs.reshape(len(selected_indices), 3)  # Reshape axs to 2D array for easy indexing
        
        for row_idx, index in enumerate(selected_indices):
            
            compound_event_name = selected_compound_events_names[row_idx]
            # Split the compound event name into individual event names
            event_1_name, event_2_name = compound_event_name.split(" & ")
            
            data = [
                average_pr_for_event_1[scenario_idx][index],
                average_pr_for_event_2[scenario_idx][index],
                average_pr_for_compound_events[scenario_idx][index]
            ]
            
            inf_mask = average_inf_for_compound_events[scenario_idx][index]
            
            boundaries = [0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8, 10]
            
            # Create a custom colormap
            blues = sns.color_palette("Blues", n_colors=6)
            ylorrd = sns.color_palette('YlOrRd', n_colors=6)
            colors = blues + ylorrd
            cmap = mcolors.ListedColormap(colors)
            norm = mcolors.BoundaryNorm(boundaries=boundaries, ncolors=len(boundaries) + 1, extend='both')
            
            for col_idx in range(3):
                ax = axs[row_idx, col_idx]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                if scenario_name == 'RCP8.5' and ('CF' in [event_1_name, event_2_name]):
                    ax.add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
                else:
                    ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                    # Apply hatching for inf areas only on the third column (compound events)
                    if col_idx == 2:
                        if np.any(inf_mask):
                            ax.contourf(data[col_idx].lon, data[col_idx].lat, inf_mask, levels=[0.9, 1.1], colors='none')  # Thin hatch lines)
                            
                            # Now, overlay crosses on the `True` areas
                            lon, lat = np.meshgrid(data[col_idx].lon, data[col_idx].lat)
                            ax.scatter(
                                lon[inf_mask], 
                                lat[inf_mask], 
                                color='gray',  # Black crosses
                                marker='x',  # Crosses
                                s=9,  # Size of the crosses
                                linewidths=0.05  # Thickness of the crosses
                            )
                            
                ax.spines['geo'].set_visible(False)
                

                        
                # Ensure data is valid
                if data[col_idx] is not None and not np.all(np.isnan(data[col_idx])):
                    plot = ax.imshow(
                        data[col_idx],
                        origin='lower',
                        extent=[-180, 180, 90, -60],
                        cmap=cmap,
                        norm=norm
                    )

                    # Set titles
                    titles = [f"Δ {event_1_name}", f"Δ {event_2_name}", "Δ Dependence"]
                    ax.set_title(titles[col_idx], fontsize=10)
                
                # Add subplot label
                ax.text(0.02, 1.1, subplot_labels[row_idx * 3 + col_idx], transform=ax.transAxes, fontsize=9, va='top', ha='left')
                
            # Add row labels for the compound event names
            axs[row_idx, 0].text(-0.2, 0.5, compound_event_name, transform=axs[row_idx, 0].transAxes, fontsize=10,
                                 va='center', ha='right', rotation=90, color='black')
        
        fig.subplots_adjust(wspace=0.1, hspace=0)
        cbar_ax = fig.add_axes([0.39, 0.1, 0.25, 0.02])
        cbar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax, orientation='horizontal', extend='both')
        # Set the ticks explicitly
        cbar.set_ticks(boundaries)
        cbar.set_ticklabels([str(b) for b in boundaries])
        cbar.ax.tick_params(labelsize=9)
        cbar.set_label(label='Probability Ratio', size=9)
        
        fig.suptitle(f'Probability Ratios considering occurrence in {scenario_name} and Early-industrial scenarios', fontsize=12)
        plt.savefig(f'C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/probability_ratios_main_selection_{scenario_name}_wrt_EI.pdf', dpi=300)
        
        plt.show()
     
    return data



def plot_probability_ratios_second_selection_with_hatches(
    average_pr_for_event_1, 
    average_pr_for_event_2, 
    average_pr_for_compound_events, 
    compound_events_names, 
    selected_indices,
    average_inf_for_compound_events
):
    """
    Plot a map showing the probability ratios for selected compound events across different scenarios.
    
    Parameters
    ----------
    average_pr_for_event_1 : List of Xarray DataArrays
        Probability ratios for Event 1 across all scenarios.
    average_pr_for_event_2 : List of Xarray DataArrays
        Probability ratios for Event 2 across all scenarios.
    average_pr_for_compound_events : List of Xarray DataArrays
        Probability ratios for the compound events across all scenarios.
    compound_events_names : List of Strings
        Names of the compound events.
    selected_indices : List of integers
        Indices of the selected compound events to plot.
    event_names : List of tuples
        Tuples containing the names of the individual events in each pair.
    
    Returns
    -------
    None
    """
     
    # Scenarios for the plots
    scenarios = ['Present Day', 'RCP2.6', 'RCP6.0', 'RCP8.5']
    
    # Generate subplot labels
    subplot_labels = 'abcdefghijklmnopqrstuvwxyz'
    
    # Mapping event names to acronyms
    event_acronyms = {
        'floodedarea': 'RF',
        'driedarea': 'DR',
        'heatwavedarea': 'HW',
        'cropfailedarea': 'CF',
        'burntarea': 'WF',
        'tropicalcyclonedarea': 'TC'
    }
    
    list_of_compound_event_acronyms = []
    for compound_event in compound_events_names:
        event_1_name = event_acronyms.get(compound_event[0])
        event_2_name = event_acronyms.get(compound_event[1])
        compound_event_acronyms = '{} & {}'.format(event_1_name, event_2_name)
        list_of_compound_event_acronyms.append(compound_event_acronyms)
    
    selected_compound_events_names = [list_of_compound_event_acronyms[i] for i in selected_indices]
    
    for scenario_idx, scenario_name in enumerate(scenarios):
        fig, axs = plt.subplots(len(selected_indices), 3, figsize=(15, 10), subplot_kw={'projection': ccrs.PlateCarree()})
        axs = axs.reshape(len(selected_indices), 3)  # Reshape axs to 2D array for easy indexing
        
        for row_idx, index in enumerate(selected_indices):
            
            compound_event_name = selected_compound_events_names[row_idx]
            # Split the compound event name into individual event names
            event_1_name, event_2_name = compound_event_name.split(" & ")
            
            data = [
                average_pr_for_event_1[scenario_idx][index],
                average_pr_for_event_2[scenario_idx][index],
                average_pr_for_compound_events[scenario_idx][index]
            ]
            
            # Define more divisions between 0 and 1
            boundaries = [0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8, 10]
            
            inf_mask = average_inf_for_compound_events[scenario_idx][index]
            
            # Create a custom colormap
            # Blue shades for values between 0.1 and 1
            blues = sns.color_palette("Blues", n_colors=6)  # 5 colors from dark to light blue
            # YlOrRd colors for values from 1 to 10
            ylorrd = sns.color_palette('YlOrRd', n_colors=6)  # Adjust this if you want more gradation
            
            # Combine the colors: light blue at 0.1, light blue at 1, then YlOrRd from 1 onwards
            colors = blues + ylorrd
            
            # Create the colormap
            cmap = mcolors.ListedColormap(colors)
            
            # Normalize based on the new boundaries
            norm = mcolors.BoundaryNorm(boundaries=boundaries, ncolors=13, extend='both')
            
            
            
            for col_idx in range(3):
                ax = axs[row_idx, col_idx]
                ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
                ax.coastlines(color='dimgrey', linewidth=0.7)
                if scenario_name == 'RCP8.5' and ('CF' in [event_1_name, event_2_name]):
                    ax.add_feature(cfeature.LAND, facecolor='none', edgecolor='lightgrey', hatch='///')
                else:
                    ax.add_feature(cfeature.LAND, facecolor='lightgrey')
                    # Apply hatching for inf areas only on the third column (compound events)
                    if col_idx == 2:
                        if np.any(inf_mask):
                            ax.contourf(data[col_idx].lon, data[col_idx].lat, inf_mask, levels=[0.9, 1.1], colors='none')  # Thin hatch lines)
                            
                            # Now, overlay crosses on the `True` areas
                            lon, lat = np.meshgrid(data[col_idx].lon, data[col_idx].lat)
                            ax.scatter(
                                lon[inf_mask], 
                                lat[inf_mask], 
                                color='gray',  # Black crosses
                                marker='x',  # Crosses
                                s=9,  # Size of the crosses
                                linewidths=0.05  # Thickness of the crosses
                            )
                            
                ax.spines['geo'].set_visible(False)
                

                        
                # Ensure data is valid
                if data[col_idx] is not None and not np.all(np.isnan(data[col_idx])):
                    plot = ax.imshow(
                        data[col_idx],
                        origin='lower',
                        extent=[-180, 180, 90, -60],
                        cmap=cmap,
                        norm=norm
                    )

                    # Set titles
                    titles = [f"Δ {event_1_name}", f"Δ {event_2_name}", "Δ Dependence"]
                    ax.set_title(titles[col_idx], fontsize=10)
                
                # Add subplot label
                ax.text(0.02, 1.1, subplot_labels[row_idx * 3 + col_idx], transform=ax.transAxes, fontsize=9, va='top', ha='left')
                ax.text(0.02, 1.1, subplot_labels[row_idx * 3 + col_idx], transform=ax.transAxes, fontsize=9, va='top', ha='left')
                
            # Add row labels for the compound event names
            axs[row_idx, 0].text(-0.2, 0.5, compound_event_name, transform=axs[row_idx, 0].transAxes, fontsize=10,
                                 va='center', ha='right', rotation=90, color='black')
        
        fig.subplots_adjust(wspace=0.1, hspace=-0.5)
        cbar_ax = fig.add_axes([0.39, 0.2, 0.25, 0.02])
        cbar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax, orientation='horizontal', extend='both')
        # Set the ticks explicitly
        cbar.set_ticks(boundaries)
        cbar.set_ticklabels([str(b) for b in boundaries])
        cbar.ax.tick_params(labelsize=9)
        cbar.set_label(label='Probability Ratio', size=9)
        
        fig.suptitle(f'Probability Ratios considering occurrence in {scenario_name} and Early-industrial scenarios', fontsize=12)
        plt.savefig(f'C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/probability_ratios_second_selection_{scenario_name}_wrt_EI.pdf', dpi=300)
        
        plt.show()
     
    return average_pr_for_compound_events



#%% Function for plotting the box plot to compare the output from the different GCMS driving the different impact models
def boxplot_comparing_gcms(all_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events, event_1_name, event_2_name, gcms):
    '''
    

    Parameters
    ----------
    gcms_timeseries_of_joint_occurrence_of_compound_events : List of xarrays and gcm considered

    Returns
    -------
    Box plot.

    '''
    
    fig = plt.figure()
    
    graph = fig.add_gridspec(nrows = 1, ncols = len(all_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events), wspace = 0) # create sub plots, with no space in between hence wspace = 0
    axes = graph.subplots(sharey =True) # to share the same y-axis
       
    colors = [(0.996, 0.89, 0.569), (0.996, 0.769, 0.31), (0.996, 0.6, 0.001), (0.851, 0.373, 0.0549), (0.6, 0.204, 0.016)] # color palette
    colors_palette = (sns.color_palette(colors)) # creating matplot color palette    

    
    for i in range(len(all_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events)):       
        
        sns.set_palette(colors_palette)
        sns.color_palette(palette = colors_palette)
        plot = sns.boxplot(data = all_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i], ax = axes[i], showmeans = True, showfliers = False, meanprops = {'marker':"o", 'markerfacecolor': "yellow", 'markersize': "5.0"})
        plot.set(xlabel = gcms [i])
        #plot.set(ylim=(0,60)) # limit y axis to fraction of 0.6
        
    for ax in fig.get_axes():

        ax.tick_params(bottom=True, labelbottom = False) # remove the x-axis labels
        
    for ax in axes.flat:
        ax.set(ylabel = 'Percentage of area')
        ax.label_outer() # avoid repetition of y axis label on all sub plots
    
    legend_elements = [Patch(facecolor= (0.996, 0.89, 0.569), edgecolor = (0.996, 0.89, 0.569), label ='Early-industrial'), Patch(facecolor= (0.996, 0.769, 0.31), edgecolor = (0.996, 0.769, 0.31), label ='Present day'), Patch(facecolor= (0.996, 0.6, 0.001), edgecolor = (0.996, 0.6, 0.001), label ='RCP2.6'), Patch(facecolor= (0.851, 0.373, 0.0549), edgecolor = (0.851, 0.373, 0.0549), label ='RCP6.0'), Patch(facecolor= (0.6, 0.204, 0.016), edgecolor = (0.6, 0.204, 0.016), label ='RCP8.5')]
   
    plt.legend(handles = legend_elements, loc = 'upper left', bbox_to_anchor = (1,1), fontsize = 10)
    
    plt.gcf().text(0.60,0.9,'{} & {}'.format(event_1_name, event_2_name), fontsize = 10)
    
    fig.suptitle('Variation in the fraction of region affected by joint occurrence of {} and {} \n during 50-year periods demonstrated by multi-impact model ensembles \n'. format(event_1_name, event_2_name), y = 1.05)
    
    
    return all_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events
    
    
#%% Function for sub plotting all the 15 box plots of the compound events
def all_boxplots(all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events, gcms):
    """
    

    Parameters
    ----------
    all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events 
    gcms : List

    Returns
    -------
    15 subplots of boxplots for all the compund event combinations.

    """
    
        
    fig = plt.figure(figsize=(8.27,11))
    outer = gridspec.GridSpec(5,3, wspace =0.3, hspace=0.25) #wspace is horizontal space between the subplots and hspace is the vertical space between the subplots
    
    
    legend_elements = [Patch(facecolor= (0.996, 0.89, 0.569), edgecolor = (0.996, 0.89, 0.569), label ='Early-industrial'), Patch(facecolor= (0.996, 0.769, 0.31), edgecolor = (0.996, 0.769, 0.31), label ='Present day'), Patch(facecolor= (0.996, 0.6, 0.001), edgecolor = (0.996, 0.6, 0.001), label ='RCP2.6'), Patch(facecolor= (0.851, 0.373, 0.0549), edgecolor = (0.851, 0.373, 0.0549), label ='RCP6.0'), Patch(facecolor= (0.6, 0.204, 0.016), edgecolor = (0.6, 0.204, 0.016), label ='RCP8.5')]
    fig.legend(handles = legend_elements, bbox_to_anchor = (0.23,0.88), fontsize = 5) 

    colors = [(0.996, 0.89, 0.569), (0.996, 0.769, 0.31), (0.996, 0.6, 0.001), (0.851, 0.373, 0.0549), (0.6, 0.204, 0.016)] # color palette
    colors_palette = (sns.color_palette(colors)) # creating matplot color palette
    
    
    for i in range(len(all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events)):
        
        all_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events = all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][0]
        event_1_name = all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][1]
        event_2_name = all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][2]
        
        inner = gridspec.GridSpecFromSubplotSpec(nrows=1, ncols = len(all_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events), subplot_spec = outer[i], wspace=0) # create sub plots, with no space in between hence wspace = 0
        
        
        
        for j in range(len(all_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events)):       
            
            
            if i == 0: ## Make first subplots of the 15, to have the certain y-axis limit 
                
                axes = plt.subplot(inner[j], ylim = (0,30))
            
            
            if i == 1: ## Make second subplots of the 15, to have the certain y-axis limit 
                               
                axes = plt.subplot(inner[j], ylim = (0,40))
            
            if i == 2: ## Make third subplot of the 15, to have a larger y-axis limit 
                
                axes = plt.subplot(inner[j], ylim = (0,70))
            
            if 2<i<5 : ## Make next subplots of the 15, to have a certain y-axis limit 
                
                axes = plt.subplot(inner[j], ylim = (0,20))
                
            if i == 5: ## Make next subplots of the 15, to have a certain y-axis limit 
                
                axes = plt.subplot(inner[j], ylim = (0,30))
                

            if i > 5:  ## Make next 12 subplots of the 15, to have a smaller y-axis limit
                
                axes = plt.subplot(inner[j], ylim = (0,10))                     
            
            axes.tick_params(axis ='y', labelsize = 7, pad = 2)
                                    
            sns.set_palette(colors_palette)
            sns.color_palette(palette = colors_palette)
            #sns.color_palette(palette = 'YlOrBr', as_cmap = True, n_colors=5)
            plot = sns.boxplot(data = all_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[j], ax = axes, width = 0.5, showmeans = True, showfliers = False, linewidth = 1, boxprops = {'linestyle': ' '}, meanprops = {'marker':"o", 'markerfacecolor': "yellow", "markeredgecolor": "black", 'markersize': "3.0"})
            
            if i > 11: 
                # Label the driving GCMs per subplot
                plot.set_xlabel(gcms [j], rotation = 45, fontsize = 7)
                
            
            if j == 3:
                
                # Titles of compound events on all the subplots
                plt.gca().set_title('{} & {}'.format(event_1_name, event_2_name), loc= 'right',fontsize = 7.5)
                
            if j > 0:
                
                plt.gca().set_yticks([])
            
            if i == 0 or i == 3 or i == 6 or i == 9 or i == 12:
                
                axes.set(ylabel = 'Percentage of area')
                axes.yaxis.label.set_size(7)
                axes.label_outer() # avoid repetition of y axis label on all sub plots
            
            #TO EDIT THE YAXIS LINES / SEPARATORS
            if j == 1:
                
                #axes.spines.right.set_visible(False) # To hide the lines
                #axes.spines.left.set_visible(False)
                
                axes.spines.right.set_color('gray')
                axes.spines.left.set_color('gray')
            
            if j == 2:
                
                #axes.spines.left.set_visible(False) # To hide the lines
                #axes.spines.right.set_visible(False)
                
                axes.spines.right.set_color('gray')
                axes.spines.left.set_color('gray')
                
            
            if j == 3:
                #axes.spines.left.set_visible(False) # To hide the lines
                
                axes.spines.left.set_color('gray')
                
            
        for ax in fig.get_axes():

            ax.tick_params(bottom=True, labelbottom = False) # remove the x-axis labels
        

    fig.suptitle('Variation in the fraction of region affected by joint occurrence of compound events \n during 50-year periods demonstrated by multi-impact model ensembles', fontsize =10)    
    
    return all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events

#%% Function for plotting the box plot to compare the compaund events with all the output from the different GCMS driving the different impact models
def comparison_boxplot(all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events):
    '''
    

    Parameters
    ----------
    all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events : List of xarrays 

    Returns
    -------
    Box plot.

    '''
    
    fig = plt.figure(figsize = (9,5.5))
    
    outer_box = gridspec.GridSpec(1,1) # outer box (bounding) all the sub plots
    outerax = fig.add_subplot(outer_box[0])
    outerax.tick_params(axis='both', which='both', length = 0, bottom=0, left=0, labelbottom=0, labelleft=0)  
    
    graph = fig.add_gridspec(nrows = 1, ncols = 15, wspace = 0.3) # create sub plots, with no space in between hence wspace = 0
    axes = graph.subplots(sharey =True) # to share the same y-axis
    
    
    colors = [(0.996, 0.89, 0.569), (0.996, 0.769, 0.31), (0.996, 0.6, 0.001), (0.851, 0.373, 0.0549), (0.6, 0.204, 0.016)] # color palette
    colors_palette = (sns.color_palette(colors)) # creating matplot color palette
    
    
    for i in range(len(all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events)):       
        
        #all_data_considering_all_gcms = [[]]*(len(all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][0][0]))
        
        all_data_considering_all_gcms = [[],[],[],[],[]]
        for gcm in range(len(all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][0])):
            gcm_data = all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][0][gcm]
            for scenario in range(len(gcm_data)):
                data_per_gcm = gcm_data[scenario]
                all_data_considering_all_gcms[scenario].extend(data_per_gcm)
                

        
        sns.set_palette(colors_palette)
        sns.color_palette(palette = colors_palette)
        plot = sns.boxplot(data = all_data_considering_all_gcms, ax = axes[i], showmeans = True, showfliers = False, width = 0.8, linewidth = 0.8, boxprops = {'linestyle': ' '}, meanprops = {'marker':"o", 'markerfacecolor': "yellow", "markeredgecolor": "black", 'markersize': "3.0"})
        #plot.set(xlabel = '{} and {}'.format(all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][1], all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][2]))
        plot.set(ylim=(0,62)) # limit y axis to fraction of 0.6
        
        
        # renaming event 1 with an acronym for plotting purposes
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][1] == 'River Floods':
            event_1_name = 'RF'
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][1] == 'Droughts':
            event_1_name = 'DR'
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][1] == 'Heatwaves':
            event_1_name = 'HW'
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][1] == 'Crop Failures':
            event_1_name = 'CF'
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][1] =='Wildfires':
            event_1_name = 'WF'
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][1] == 'Tropical Cyclones':
            event_1_name ='TC'
        
        # renaming event 2 with an acronym for plotting purposes
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][2] == 'River Floods':
            event_2_name = 'RF'
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][2] == 'Droughts':
            event_2_name = 'DR'
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][2] == 'Heatwaves':
            event_2_name = 'HW'
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][2] == 'Crop Failures':
            event_2_name = 'CF'
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][2] =='Wildfires':
            event_2_name = 'WF'
        if all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][2] == 'Tropical Cyclones':
            event_2_name ='TC'
        
        
        
        plot.set_xlabel('     {} & {}'.format(event_1_name, event_2_name), rotation = 315 , rotation_mode = 'anchor', labelpad = 0.0, loc = 'left', fontsize = 9)

        
        if 0<i<14:
            axes[i].spines.left.set_visible(False) # To hide the lines
            axes[i].spines.right.set_visible(False)
            axes[i].tick_params(axis='y', which='both', length = 0)
            
            xticks = axes[i].xaxis.get_major_ticks()
            xticks[0].set_visible(False)
            xticks[1].set_visible(False)
            xticks[3].set_visible(False)
            xticks[4].set_visible(False)
        
        if i == 0:
            axes[i].spines.right.set_visible(False)
            
            xticks = axes[i].xaxis.get_major_ticks()
            xticks[0].set_visible(False)
            xticks[1].set_visible(False)
            xticks[3].set_visible(False)
            xticks[4].set_visible(False)
            
        
        if i == 14:
            axes[i].spines.left.set_visible(False) # To hide the lines
            axes[i].tick_params(axis='y', which='both', length = 0)
            
            xticks = axes[i].xaxis.get_major_ticks()
            xticks[0].set_visible(False)
            xticks[1].set_visible(False)
            xticks[3].set_visible(False)
            xticks[4].set_visible(False)
        
        
    for ax in fig.get_axes():

        ax.tick_params(bottom=True, labelbottom = False) # remove the x-axis labels
        
    for ax in axes.flat:
        ax.set(ylabel = 'Land area affected by hazards/impacts pair [%]')
        ax.label_outer() # avoid repetition of y axis label on all sub plots
    
    
    
    legend_elements = [Patch(facecolor= (0.996, 0.89, 0.569), edgecolor = (0.996, 0.89, 0.569), label ='Early-industrial'), Patch(facecolor= (0.996, 0.769, 0.31), edgecolor = (0.996, 0.769, 0.31), label ='Present day'), Patch(facecolor= (0.996, 0.6, 0.001), edgecolor = (0.996, 0.6, 0.001), label ='RCP2.6'), Patch(facecolor= (0.851, 0.373, 0.0549), edgecolor = (0.851, 0.373, 0.0549), label ='RCP6.0'), Patch(facecolor= (0.6, 0.204, 0.016), edgecolor = (0.6, 0.204, 0.016), label ='RCP8.5')]
    fig.legend(handles = legend_elements, bbox_to_anchor = (0.90,0.88), fontsize = 10, frameon=False)

    
    
    #fig.suptitle('Variation in the fraction of region affected by compound events \n in 50-year periods demonstrated by multi-impact model ensembles \n', y = 1.05)
    
    # Change this directory to save the plots to your desired directory
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/Fraction of region affected by compound events demonstrated by multi-impact model ensembles.pdf', dpi = 300)
    
    plt.show()
    
    return all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events
    


#%%
def comparison_boxplot_with_median_values_per_boxplot(all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events):
    '''
    Parameters
    ----------
    all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events : List of xarrays 

    Returns
    -------
    Box plot.
    '''
    
    fig = plt.figure(figsize=(9, 5.5))
    
    outer_box = gridspec.GridSpec(1, 1)  # outer box (bounding) all the sub plots
    outerax = fig.add_subplot(outer_box[0])
    outerax.tick_params(axis='both', which='both', length=0, bottom=0, left=0, labelbottom=0, labelleft=0)  
    
    graph = fig.add_gridspec(nrows=1, ncols=15, wspace=0.3)  # create sub plots, with no space in between hence wspace=0
    axes = graph.subplots(sharey=True)  # to share the same y-axis
    
    colors = [(0.996, 0.89, 0.569), (0.996, 0.769, 0.31), (0.996, 0.6, 0.001), (0.851, 0.373, 0.0549), (0.6, 0.204, 0.016)]  # color palette
    colors_palette = sns.color_palette(colors)  # creating matplot color palette
    
    for i in range(len(all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events)):       
        
        all_data_considering_all_gcms = [[], [], [], [], []]
        for gcm in range(len(all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][0])):
            gcm_data = all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][0][gcm]
            for scenario in range(len(gcm_data)):
                data_per_gcm = gcm_data[scenario]
                all_data_considering_all_gcms[scenario].extend(data_per_gcm)
        
        sns.set_palette(colors_palette)
        sns.color_palette(palette=colors_palette)
        plot = sns.boxplot(data=all_data_considering_all_gcms, ax=axes[i], showmeans=True, showfliers=False, width=0.8, linewidth=0.8, 
                           boxprops={'linestyle': ' '}, meanprops={'marker': "o", 'markerfacecolor': "yellow", "markeredgecolor": "black", 'markersize': "3.0"})
        plot.set(ylim=(0, 62))  # limit y axis to fraction of 0.6
        
        medians = []
        for j in range(5):
            if all_data_considering_all_gcms[j]:
                maximum = np.max(all_data_considering_all_gcms[j])
                median = np.median(all_data_considering_all_gcms[j])
                mean = np.mean(all_data_considering_all_gcms[j])
                axes[i].text(j, maximum + 1, f'{median:.2f}', horizontalalignment='center', color='black', weight='semibold', fontsize=6, rotation=90)
                axes[i].text(j, maximum + 4, f'  |  {mean:.2f}', horizontalalignment='center', color='blue', weight='semibold', fontsize=6, rotation=90)

                
# =============================================================================
#             median = np.median(all_data_considering_all_gcms[j])
#             maximum = np.max(all_data_considering_all_gcms[j])
#             medians.append(median)
#             axes[i].text(j, maximum + 1, f'{median:.2f}', horizontalalignment='center', color='black', weight='semibold', fontsize=6, rotation=90)
# 
# =============================================================================
        # renaming event 1 with an acronym for plotting purposes
        event_1_name = {
            'River Floods': 'RF',
            'Droughts': 'DR',
            'Heatwaves': 'HW',
            'Crop Failures': 'CF',
            'Wildfires': 'WF',
            'Tropical Cyclones': 'TC'
        }[all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][1]]
        
        # renaming event 2 with an acronym for plotting purposes
        event_2_name = {
            'River Floods': 'RF',
            'Droughts': 'DR',
            'Heatwaves': 'HW',
            'Crop Failures': 'CF',
            'Wildfires': 'WF',
            'Tropical Cyclones': 'TC'
        }[all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events[i][2]]
        
        plot.set_xlabel('     {} & {}'.format(event_1_name, event_2_name), rotation=315, rotation_mode='anchor', labelpad=0.0, loc='left', fontsize=9)

        if 0 < i < 14:
            axes[i].spines.left.set_visible(False)  # To hide the lines
            axes[i].spines.right.set_visible(False)
            axes[i].tick_params(axis='y', which='both', length=0)
            xticks = axes[i].xaxis.get_major_ticks()
            xticks[0].set_visible(False)
            xticks[1].set_visible(False)
            xticks[3].set_visible(False)
            xticks[4].set_visible(False)
        
        if i == 0:
            axes[i].spines.right.set_visible(False)
            xticks = axes[i].xaxis.get_major_ticks()
            xticks[0].set_visible(False)
            xticks[1].set_visible(False)
            xticks[3].set_visible(False)
            xticks[4].set_visible(False)
        
        if i == 14:
            axes[i].spines.left.set_visible(False)  # To hide the lines
            axes[i].tick_params(axis='y', which='both', length=0)
            xticks = axes[i].xaxis.get_major_ticks()
            xticks[0].set_visible(False)
            xticks[1].set_visible(False)
            xticks[3].set_visible(False)
            xticks[4].set_visible(False)
        
    for ax in fig.get_axes():
        ax.tick_params(bottom=True, labelbottom=False)  # remove the x-axis labels
        
    for ax in axes.flat:
        ax.set(ylabel='Land area affected by hazards/impacts pair [%]')
        ax.label_outer()  # avoid repetition of y axis label on all sub plots
    
    legend_elements = [
        Patch(facecolor=(0.996, 0.89, 0.569), edgecolor=(0.996, 0.89, 0.569), label='Early-industrial'),
        Patch(facecolor=(0.996, 0.769, 0.31), edgecolor=(0.996, 0.769, 0.31), label='Present day'),
        Patch(facecolor=(0.996, 0.6, 0.001), edgecolor=(0.996, 0.6, 0.001), label='RCP2.6'),
        Patch(facecolor=(0.851, 0.373, 0.0549), edgecolor=(0.851, 0.373, 0.0549), label='RCP6.0'),
        Patch(facecolor=(0.6, 0.204, 0.016), edgecolor=(0.6, 0.204, 0.016), label='RCP8.5')
    ]
    fig.legend(handles=legend_elements, bbox_to_anchor=(0.90, 0.88), fontsize=10, frameon=False)
    
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/CR/Fraction of region affected by compound events demonstrated by multi-impact model ensembles.pdf', dpi=300)
    plt.show()
    
    
    
    return all_compound_event_combinations_and_gcms_timeseries_50_years_of_joint_occurrence_of_compound_events
#%% Function for plotting the dominant compound event in a location per scenario
def plot_dominant_compound_event_per_scenario_considering_all_gcms(scenarios_of_compound_events, compound_events):
    
    """ Plot a map showing the average co-occurrence ratio across cross category impact models driven by the same GCM such that a co-occurrence ratio > 1 means that there are more co-occurring extremes than isolated extremes, and a co-occurence ratio < 1 means that there are less co-occurring extremes than isolated ones.  
    
    Parameters
    ----------
    scenarios_of_compound_events : list of Xarray data array of the average probability of occurrence of 15 pairs of extreme events across cross category impact models driven by the same GCM. 
    
    Returns
    -------
    Plot (Figure) showing the dominant compound event per location per scenario
    """
    
    #average_cooccurrence_ratio_considering_cross_category_impact_models_for_all_extreme_events_for_condidering_all_scenarios_considering_all_gcms = [[], [], [], []] # In the order of gcms = ['gfdl-esm2m', 'hadgem2-es', 'ipsl-cm5a-lr', 'miroc5'], as stated at the beginning of this script
    
    #scenarios =[0, 1, 2, 3]
    
    # Setting the projection of the map to cylindrical / Mercator
    fig, axs = plt.subplots(2,2, figsize=(8, 6), subplot_kw = {'projection': ccrs.PlateCarree()})  # , constrained_layout=True
    
    # since axs is a 2 dimensional array of geozaxes, we have to flatten it into 1D; as explained on a similar example on this page: https://kpegion.github.io/Pangeo-at-AOES/examples/multi-panel-cartopy.html
    axs=axs.flatten()
    
    
    list_of_compound_event_acronyms = []
    for compound_event in compound_events:
        # renaming event 1 with an acronym for plotting purposes
        if compound_event[0] == 'floodedarea':
            event_1_name = 'RF'
        if compound_event[0] == 'driedarea':
            event_1_name = 'DR'
        if compound_event[0] == 'heatwavedarea':
            event_1_name = 'HW'
        if compound_event[0] == 'cropfailedarea':
            event_1_name = 'CF'
        if compound_event[0] =='burntarea':
            event_1_name = 'WF'
        if compound_event[0] == 'tropicalcyclonedarea':
            event_1_name ='TC'
        
        # renaming event 2 with an acronym for plotting purposes
        if compound_event[1] == 'floodedarea':
            event_2_name = 'RF'
        if compound_event[1] == 'driedarea':
            event_2_name = 'DR'
        if compound_event[1] == 'heatwavedarea':
            event_2_name = 'HW'
        if compound_event[1] == 'cropfailedarea':
            event_2_name = 'CF'
        if compound_event[1] =='burntarea':
            event_2_name = 'WF'
        if compound_event[1] == 'tropicalcyclonedarea':
            event_2_name ='TC'
        
        compound_event_acronyms = '{} & {}'.format(event_1_name, event_2_name)
        list_of_compound_event_acronyms.append(compound_event_acronyms)

# =============================================================================
#     # Define the number of unique colors
#     num_colors = 15
# 
#     # Choose a discrete colormap
#     cmap = plt.cm.get_cmap('tab20', num_colors)
# =============================================================================
        
    for scenario in range(len(scenarios_of_compound_events)):
        
        if scenario < 4:
            
            if scenario == 0:
                scenario_name = 'Early-industrial'
            if scenario == 1:
                scenario_name = 'Present-Day'
            if scenario == 2:
                scenario_name = 'RCP2.6'
            if scenario == 3:
                scenario_name = 'RCP6.0'
            
    
            # Concatenate the arrays along a new dimension to create a new xarray dataset
            stacked_arrays = xr.concat(scenarios_of_compound_events[scenario], dim='array')
            
            # Check if all values across different arrays at each grid point are equal to zero
            all_zeros_mask = (stacked_arrays == 0).all(dim='array')
            #stacked_arrays_masked = xr.where(all_zeros_mask, np.nan, stacked_arrays)
    
            # Find the index of the array with the maximum value at each grid point
            filled_data_array = stacked_arrays.fillna(-9999) # replace nan with -9999 because the xarray arg.max doesnt work with "All-NaN slice encountered"
            max_array_indices_unmasked = filled_data_array.argmax(dim='array', skipna=True) # determine index of array with max value : https://docs.xarray.dev/en/stable/generated/xarray.DataArray.argmax.html
            max_array_indices_unmasked_for_non_occurrences = xr.where(all_zeros_mask, np.nan, max_array_indices_unmasked)
            max_array_indices_masked = xr.where((filled_data_array[0] == -9999), np.nan, max_array_indices_unmasked_for_non_occurrences) # return Nan values in the previous positions
            
            
# =============================================================================
#             # Concatenate the arrays along a new dimension to create a new xarray dataset
#             stacked_arrays = xr.concat(scenarios_of_compound_events[scenario], dim='array')
#             
#     
#             # Find the index of the array with the maximum value at each grid point
#             filled_data_array = stacked_arrays.fillna(-9999) # replace nan with -9999 because the xarray arg.max doesnt work with "All-NaN slice encountered"
#             max_array_indices_unmasked = filled_data_array.argmax(dim='array', skipna=True) # determine index of array with max value : https://docs.xarray.dev/en/stable/generated/xarray.DataArray.argmax.html
#             max_array_indices_masked = xr.where((filled_data_array[0] == -9999), np.nan, max_array_indices_unmasked) # return Nan values in the previous positions            
# =============================================================================
            
            
            # Define the number of unique colors
            num_colors = 15

            # Choose a discrete colormap
            cmap = plt.cm.get_cmap('tab20', num_colors)
            
            # Plot the co-occurrence of an extreme event per GCM in a subplot
            plot = axs[scenario].imshow(max_array_indices_masked, origin = 'upper' , extent= map_extent, cmap = cmap, vmin=0, vmax=num_colors-1)
                
            # Add the background map to the plot  
            #ax.stock_img() 
                  
            # Set the extent of the plotn
            axs[scenario].set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
            
            # Plot the coastlines along the continents on the map
            axs[scenario].coastlines(color='dimgrey', linewidth=0.7)
            
            # Plot features: lakes, rivers and boarders
            #axs[scenario].add_feature(cfeature.LAKES, alpha =0.5)
            #axs[scenario].add_feature(cfeature.RIVERS)
            #axs[scenario].add_feature(cfeature.OCEAN)
            #axs[scenario].add_feature(cfeature.LAND, facecolor ='lightgrey')
            #ax.add_feature(cfeature.BORDERS, linestyle=':')
            
            
            # Coordinates longitude and latitude ticks...These can be manipulated depending on study area chosen
    # =============================================================================
    #         xticks = [-180, -120, -60, 0, 60, 120, 180] # Longitudes
    #         axs[scenario].set_xticks(xticks, crs=ccrs.PlateCarree()) 
    #         axs[scenario].set_xticklabels(xticks, fontsize = 8)
    #         
    #         yticks = [90, 60, 30, 0, -30, -60]
    #         axs[scenario].set_yticks(yticks, crs=ccrs.PlateCarree()) 
    #         axs[scenario].set_yticklabels(yticks, fontsize = 8)
    #         
    #         lon_formatter = LongitudeFormatter()
    #         lat_formatter = LatitudeFormatter()
    #         axs[scenario].xaxis.set_major_formatter(lon_formatter)
    #         axs[scenario].yaxis.set_major_formatter(lat_formatter)
    # =============================================================================
            # Remove borders
            axs[scenario].set_frame_on(False)
            
            # Subplot labels
            # label
            subplot_labels = ['a.','b.','c.','d.']
            axs[scenario].text(0, 1.06, subplot_labels[scenario], transform=axs[scenario].transAxes, fontsize=9, ha='left')
           
            axs[scenario].set_title(scenario_name, fontsize = 9, loc = 'right')
            
            
           
        # Since crop failures doesnt have RCP8.5, it would not be a fair comparison with the rest of the extreme events. Here we ignore RCP8.5
        if scenario == 4:
            #scenario_name = 'RCP8.5'
            print('No data available for the crop failures for the selected GCM scenario under RCP8.5')
    
    fig.subplots_adjust(wspace=0.1, hspace=0.00001)
    
    # Create a custom legend
    legend_patches = [Patch(color=cmap(i), label=list_of_compound_event_acronyms[i]) for i in range(num_colors)]
    plt.legend(handles=legend_patches, bbox_to_anchor=(1.23, 1.3), fontsize=5)        
    
    # Add the title and legend to the figure and show the figure
    fig.suptitle('Dominant co-occurring extreme event',fontsize=10) #Plot title     
    

    
    # Change this directory to save the plots to your desired directory
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Dominant_cooccurring_extremes.pdf'.format(scenario_name), dpi = 300)
    
    plt.show()
            
                #plt.close()
        
    return scenarios_of_compound_events


#%% Plotting most dominant compound event per scenario

def plot_dominant_compound_event_per_scenario(
    scenarios_of_compound_events, 
    compound_events
):
    """
    Plot the most dominant compound event per scenario in a 2x2 subplot layout.
    
    Parameters
    ----------
    scenarios_of_compound_events : List of Xarray data arrays
        The data for each scenario containing the frequency of compound events.
    compound_events : List of tuples
        Names of the compound events.
    
    Returns
    -------
    Plot (Figure) showing the most dominant compound event per scenario.
    """
    
    # Define scenario names
    scenario_names = ['Early-industrial', 'Present day', 'RCP2.6', 'RCP6.0']

    # Mapping event names to acronyms
    event_acronyms = {
        'floodedarea': 'RF',
        'driedarea': 'DR',
        'heatwavedarea': 'HW',
        'cropfailedarea': 'CF',
        'burntarea': 'WF',
        'tropicalcyclonedarea': 'TC'
    }
    
    list_of_compound_event_acronyms = [
        f"{event_acronyms[ev1]} & {event_acronyms[ev2]}" 
        for ev1, ev2 in compound_events
    ]
    
    # Setting the projection of the map to cylindrical / Mercator
    fig, axs = plt.subplots(2, 2, figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})
    
    # Flatten the 2D array of axes into 1D for easy iteration
    axs = axs.flatten()
    
    num_colors = len(list_of_compound_event_acronyms)
    cmap = plt.cm.get_cmap('tab20', num_colors)

    for scenario in range(len(scenarios_of_compound_events)-1): # -1 ignores RCP8.5 scenario
        scenario_data = scenarios_of_compound_events[scenario]
        
        # Stack arrays along a new dimension
        stacked_arrays = xr.concat(scenario_data, dim='array')
        
        # Handle NaNs and find the most dominant event
        all_zeros_mask = (stacked_arrays == 0).all(dim='array')
        filled_data_array = stacked_arrays.fillna(-9999)
        max_array_indices_unmasked = filled_data_array.argmax(dim='array', skipna=True)
        #max_array_indices_masked = xr.where(all_zeros_mask, np.nan, max_array_indices_unmasked)
        
        max_array_indices_unmasked_for_non_occurrences = xr.where(all_zeros_mask, np.nan, max_array_indices_unmasked)
        max_array_indices_masked = xr.where((filled_data_array[0] == -9999), np.nan, max_array_indices_unmasked_for_non_occurrences) # return Nan values in the previous positions
        

        # Plotting each scenario in a subplot
        ax = axs[scenario]
        ax.set_extent([-180, 180, 90, -60], crs=ccrs.PlateCarree())
        ax.coastlines(color='dimgrey', linewidth=0.7)
        #ax.add_feature(cfeature.LAND, facecolor='lightgrey')
        ax.spines['geo'].set_visible(False)

        plot = ax.imshow(
            max_array_indices_masked, 
            origin='lower', 
            extent=[-180, 180, 90, -60], 
            cmap=cmap, 
            vmin=0, 
            vmax=num_colors-1
        )
        
        ax.text(0.02, 1.125, f"{chr(97+scenario)}.", transform=ax.transAxes, fontsize=10, va='top', ha='left')
        ax.set_title(scenario_names[scenario], fontsize=10, loc='right')
    
    fig.subplots_adjust(wspace=0.1, hspace=-0.55)
    
    # Create a custom legend
    legend_patches = [Patch(color=cmap(i), label=list_of_compound_event_acronyms[i]) for i in range(num_colors)]
    fig.legend(handles=legend_patches, loc='lower center', ncol=4, bbox_to_anchor=(0.52, 0.17), fontsize=8)
    
    # Add the title and show the figure
    fig.suptitle('Dominant Co-occurring Extreme Event', fontsize=12)
    plt.savefig('C:/Users/dmuheki/OneDrive - Vrije Universiteit Brussel/Concurrent_climate_extremes_global/Dominant_compound_events.pdf', dpi=300, bbox_inches='tight')
    plt.show()
    
    return scenarios_of_compound_events