CC_Est_NARC/Analysis/Code/P1_NARCliM_plots_Windows.py

# -*- coding: utf-8 -*-
#####################################----------------------------------
#Last Updated - March 2018
#@author: z5025317 Valentin Heimhuber
#code for creating climate prioritization plots for NARCLIM variables. 
#Inputs: Uses CSV files that contain all 12 NARCLIM model runs time series for 1 grid cell created with: P1_NARCliM_NC_to_CSV_CCRC_SS.py
#####################################----------------------------------
#Load packages
#####################################----------------------------------
import numpy as np
import os
import pandas as pd
import glob
import matplotlib
import matplotlib.pyplot as plt
from datetime import datetime
from datetime import timedelta
from matplotlib.backends.backend_pdf import PdfPages
from ggplot import *
matplotlib.style.use('ggplot')
#
# Set working direcotry (where postprocessed NARClIM data is located)
os.chdir('C:/Users/z5025317/WRL_Postdoc/Projects/Paper#1/')
#
#####################################----------------------------------
#set input parameters
Base_period_start = '1990-01-01'
Base_period_end = '2080-01-01' #use last day that's not included in period as < is used for subsetting
Estuary = 'Terrigal' # 'Belongil'
Clim_var_type  = "wssmean*"   #  '*' will create pdf for all variables in folder  "pracc*|tasmax*"
subset_ensemble = 'yes' # is yes, only the model with the lowest, median and max difference between present day and far future are selected         
plot_pdf = 'no'
#####################################----------------------------------
#
#set directory path for output files
output_directory = 'Output/'+ Estuary
#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
if not os.path.exists(output_directory):
    os.makedirs(output_directory)
    print('-------------------------------------------')
    print("output directory folder didn't exist and was generated")
    print('-------------------------------------------')
    print('-------------------')
Clim_Var_CSVs = glob.glob('./Data/NARCLIM_Site_CSVs/' + Estuary + '/' + Clim_var_type)
#Clim_Var_CSV = glob.glob('./Site_CSVs/' + Clim_var_type + '*' )
#read CSV file
for clim_var_csv_path in Clim_Var_CSVs:
    #clim_var_csv_path = Clim_Var_CSVs[0]
    Filename = os.path.basename(os.path.normpath(clim_var_csv_path))
    Clim_var_type = Filename.split('_', 1)[0]
    print(clim_var_csv_path)
    Full_df = pd.read_csv(clim_var_csv_path, index_col=0, parse_dates = True)
    #pandas datestamp index to period (we don't need the 12 pm info in the index (daily periods are enough))
    Full_df.index = Full_df.index.to_period('D')
    Full_df = Full_df.drop(columns=['period'])
    Ncols_df = len(Full_df)
    #check data types of columns
    #Full_df.dtypes
    
    #substract a constant from all values to convert from kelvin to celcius (temp)
    if Clim_var_type == 'tasmean' or Clim_var_type == 'tasmax':
        Full_df = Full_df.iloc[:,0:(Ncols_df-1)]-273.15
    if Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean':
        Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60*24
    Fdf_1900_2080 = Full_df

    #Subset the data to the minimum base period and above (used to set the lenght of the present day climate period)
    #Fdf_1900_2080 = Full_df.loc[(Full_df.index >= Base_period_start) & (Full_df.index < Base_period_end)] # not necessary if not using reanalysis models for base period
    
    #Aggregate daily df to annual time series
    if (Clim_var_type == 'pracc' or Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean' 
    or Clim_var_type == 'pr1Hmaxtstep' or Clim_var_type == 'wss1Hmaxtstep'):
        Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').sum()
        Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
        Fdf_1900_2080_monthly = Fdf_1900_2080.resample('M').sum()
        Fdf_1900_2080_monthly = Fdf_1900_2080_monthly.replace(0, np.nan)
        Fdf_1900_2080_weekly = Fdf_1900_2080.resample('W').sum()
        Fdf_1900_2080_weekly = Fdf_1900_2080_weekly.replace(0, np.nan)
        Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').sum() #seasonal means
        Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
    else:    
        Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').mean()
        Fdf_1900_2080_monthly = Fdf_1900_2080.resample('M').mean()
        Fdf_1900_2080_weekly = Fdf_1900_2080.resample('W').mean()
        Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').mean() #seasonal means
    
    #plot the mean of all model runs
    print('-------------------------------------------')
    print('mean of all models for climate variable:  ' + Clim_var_type)
    Fdf_1900_2080_means = Fdf_1900_2080.mean()
    Fdf_1900_2080_means.plot(kind='bar').figure
    print('-------------------------------------------')
    
    if subset_ensemble == 'yes':
        #Select the 3 most representative models (min med and max difference betwen far future and present)
        Fdf_1900_2080_sorted = Fdf_1900_2080.reindex_axis(sorted(Fdf_1900_2080.columns), axis=1)
        Fdf_1900_2080_sorted_means = pd.DataFrame(Fdf_1900_2080_sorted.mean())
        df = Fdf_1900_2080_sorted_means
        #add a simple increasing integer index 
        df = df.reset_index()
        df= df[df.index % 3 != 1]
        df['C'] = df[0].diff()
        df = df.reset_index()
        df= df[df.index % 2 != 0]
        #get max difference model (difference between far future and prsent day)
        a = df[df.index == df['C'].argmax(skipna=True)]
        Max_dif_mod_name = a.iloc[0]['index']
        #get min difference model
        a = df[df.index == df['C'].argmin(skipna=True)]
        Min_dif_mod_name = a.iloc[0]['index']
        #get the model which difference is closest to the median difference
        df['D'] = abs(df['C']- df['C'].median())
        a = df[df.index == df['D'].argmin(skipna=True)]
        Med_dif_mod_name = a.iloc[0]['index']
        #data frame with min med and max difference model
        df2 = Fdf_1900_2080.filter(regex= Min_dif_mod_name[:-5] + '|' +  Med_dif_mod_name[:-5] + '|' +  Max_dif_mod_name[:-5] )
        dfall = df2.reindex_axis(sorted(df2.columns), axis=1)
        #data frame with individual models
        dfmin = Fdf_1900_2080.filter(regex= Min_dif_mod_name[:-5])
        dfmax = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5])
        dfmed = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5])
        # use only the 3 representative models for the analysis
        Fdf_1900_2080_all_mods = Fdf_1900_2080
        #create a dataframe that has 1 column for each of the three representative models
#        Full_df.loc[(Full_df.index > '1990-01-01') & (Full_df.index < '2009-01-01'), 'period']= '1990-2009'
#        Full_df.loc[(Full_df.index > '2020-01-01') & (Full_df.index < '2039-01-01'), 'period']= '2020-2039'
#        Full_df.loc[(Full_df.index > '2060-01-01') & (Full_df.index < '2079-01-01'), 'period']= '2060-2079'
        dfa = Fdf_1900_2080_annual.iloc[:,[0]]
        dfa1 = Fdf_1900_2080_annual.iloc[:,[0,3,6]].loc[(Fdf_1900_2080_annual.index >= '1990') & (Fdf_1900_2080_annual.index <= '2009')]
        dfa1.columns = [Min_dif_mod_name[:-5], Med_dif_mod_name[:-5], Max_dif_mod_name[:-5]]
        dfa2 = Fdf_1900_2080_annual.iloc[:,[1,4,7]].loc[(Fdf_1900_2080_annual.index >= '2020') & (Fdf_1900_2080_annual.index <= '2039')]
        dfa2.columns = [Min_dif_mod_name[:-5], Med_dif_mod_name[:-5], Max_dif_mod_name[:-5]]
        dfa3 = Fdf_1900_2080_annual.iloc[:,[2,5,8]].loc[(Fdf_1900_2080_annual.index >= '2060') & (Fdf_1900_2080_annual.index <= '2079')]
        dfa3.columns = [Min_dif_mod_name[:-5], Med_dif_mod_name[:-5], Max_dif_mod_name[:-5]]
        dfall_annual = dfa1.append(dfa2).append(dfa3)
        
    #Create Deltas of average change for annual and seasonal basis
    times = ['annual', 'DJF', 'MAM', 'JJA','SON']
    delta_all_df = pd.DataFrame()
    for temp in times:
        if temp == 'annual':
            Mean_df = Fdf_1900_2080_annual.mean()
            Column_names = ['near', 'far']
        if temp == 'DJF':
            Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean()
            Column_names = ['DJF_near', 'DJF_far']
        if temp == 'MAM':
            Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean()
            Column_names = ['MAM_near', 'MAM_far']
        if temp == 'JJA':
            Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean()
            Column_names = ['JJA_near', 'JJA_far']
        if temp == 'SON':
            Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean()
            Column_names = ['SON_near', 'SON_far']
        models = list(Fdf_1900_2080_means.index)
        newmodel = []
        type(newmodel)
        for each in models:
            newmodel.append(each[:-5])
        unique_models = set(newmodel)
        # calculate diff for each unique model
        delta_NF_ensemble = []
        delta_FF_ensemble = []
        for unique_model in unique_models:
            dfdiff = Mean_df.filter(regex= unique_model)
            type(dfdiff)
            delta_NF = dfdiff[1] - dfdiff[0]
            delta_NF_ensemble.append(delta_NF)
            delta_FF = dfdiff[2] - dfdiff[1]
            delta_FF_ensemble.append(delta_FF)
    
        delta_df1=pd.DataFrame(delta_NF_ensemble, index=unique_models)
        delta_df2=pd.DataFrame(delta_FF_ensemble, index=unique_models)
        delta_df= pd.concat([delta_df1, delta_df2], axis=1)
        
        #rename columns
        delta_df.columns = Column_names
        #add a row with medians and 10 and 90th percentiles
        delta_df.loc['10th'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 10), Column_names[1]:np.percentile(delta_df[Column_names[1]], 10)})
        delta_df.loc['median'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 50), Column_names[1]:np.percentile(delta_df[Column_names[1]], 50)})
        delta_df.loc['90th'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 90), Column_names[1]:np.percentile(delta_df[Column_names[1]], 90)})
        #append df to overall df
        delta_all_df = pd.concat([delta_all_df, delta_df], axis=1)

    out_file_name = Estuary  + '_' + Clim_var_type + '_NARCliM_ensemble_changes.csv'
    out_path = output_directory + '/' + out_file_name
    delta_all_df.to_csv(out_path)

    #create a dataframe that has a single column for present day, near and far future for the (3 selected models)
    len(Fdf_1900_2080.columns)
    Full_current_df = Fdf_1900_2080.iloc[:,range(0,3)]
    Full_current_df = Full_current_df.stack()
    #nearfuture
    Full_nearfuture_df = Fdf_1900_2080.iloc[:,range(3,6)]
    Full_nearfuture_df = Full_nearfuture_df.stack()
    #farfuture
    Full_farfuture_df = Fdf_1900_2080.iloc[:,range(6,len(Fdf_1900_2080.columns))]
    Full_farfuture_df = Full_farfuture_df.stack()
    Summarized_df = pd.concat([Full_current_df, Full_nearfuture_df], axis=1, ignore_index=True)
    Summarized_df = pd.concat([Summarized_df, Full_farfuture_df], axis=1, ignore_index=True)
    Summarized_df.columns = ['present', 'near', 'far']
    
    #output some summary plot into pdf
    if plot_pdf == 'yes':
        #write the key plots to a single pdf document
        pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary_3.pdf'
        pdf_out_path = output_directory +'/' + pdf_out_file_name
        #open pdf and add the plots
        with PdfPages(pdf_out_path) as pdf:
            #barplot of model means
            plt.title(Clim_var_type + ' -  model means - full period')
            ymin = min(Fdf_1900_2080_means) 
            ymax = max(Fdf_1900_2080_means) 
            Fdf_1900_2080_means.plot(kind='bar', ylim=(ymin,ymax))
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            #full period density comparison
            plt.title(Clim_var_type + ' -  density comparison - full period - all models')
            Summarized_df.plot.kde()
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            #annual box
            plt.title(Clim_var_type + ' -  Annual means/sums for max diff model')
            Fdf_1900_2080_annual.boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            #monthly box
            plt.title(Clim_var_type + ' -  Monthly means/sums')
            Fdf_1900_2080_monthly.boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            #annual box
            plt.title(Clim_var_type + ' -  Monthly means/sums for min diff model')
            Fdf_1900_2080_monthly.filter(regex= Min_dif_mod_name[:-5]).boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            #annual box
            plt.title(Clim_var_type + ' -  Monthly means/sums for median diff model')
            Fdf_1900_2080_monthly.filter(regex= Med_dif_mod_name[:-5]).boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            #annual box
            plt.title(Clim_var_type + ' -  Monthly means/sums for max diff model')
            Fdf_1900_2080_monthly.filter(regex= Max_dif_mod_name[:-5]).boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            #weekly box
            plt.title(Clim_var_type + ' -  Weekly means/sums')
            Fdf_1900_2080_weekly.boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            #daily box
            plt.title(Clim_var_type + ' -  Daily means/sums')
            Fdf_1900_2080.boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            # time series plot annual ALL models
            plt.title(Clim_var_type + ' -  Time series - representative models')
            dfall_annual.plot(legend=False)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            
            # seasonal mean boxplots
            ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean()) 
            ymax = max(Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean()) 
            plt.title(Clim_var_type + ' -  DJF Summer means/sums')
            Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean().plot(kind='bar', ylim=(ymin,ymax))
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()  
            plt.title(Clim_var_type + ' -  DJF Summer means/sums')
            Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean()) 
            ymax = max(Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean())
            plt.title(Clim_var_type + ' -  MAM Autumn means/sums')
            Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean().plot(kind='bar', ylim=(ymin,ymax))
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close() 
            plt.title(Clim_var_type + ' -  MAM Autumn means/sums')
            Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean()) 
            ymax = max(Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean())
            plt.title(Clim_var_type + ' -  JJA Winter means/sums')
            Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean().plot(kind='bar', ylim=(ymin,ymax))
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close() 
            plt.title(Clim_var_type + ' -  JJA Winter means/sums')
            Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()
            ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean()) 
            ymax = max(Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean())
            plt.title(Clim_var_type + ' -  SON Spring means/sums')
            Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean().plot(kind='bar', ylim=(ymin,ymax))
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close() 
            plt.title(Clim_var_type + ' -  SON Spring means/sums')
            Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].boxplot(rot=90)
            pdf.savefig(bbox_inches='tight', pad_inches=0.4)
            plt.close()