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
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 = 'Bateman' # 'Belongil'
#Clim_var_type  = 'tasmean' will create pdf for all variables in folder           
#####################################----------------------------------
#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_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')
    #check data types of columns
    #Full_df.dtypes
    
    #substract a constant from all values (temp)
    if Clim_var_type == 'tasmean' or Clim_var_type == 'tasmax':
        Full_df = Full_df.iloc[:,0:(Ncols_df-1)]-273.15
    
    #index the narclim periods if needed
    #Full_df.loc[(Full_df.index >= '1990-01-01') & (Full_df.index < '2010-01-01'), 'period']= '1990-2009'
    #Full_df.loc[(Full_df.index >= '2020-01-01') & (Full_df.index < '2040-01-01'), 'period']= '2020-2039'
    #Full_df.loc[(Full_df.index >= '2060-01-01') & (Full_df.index < '2080-01-01'), 'period']= '2060-2079'
    
    #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
    Fdf_1900_2080 = Full_df.drop(columns=['period'])
    Ncols_df = len(Fdf_1900_2080)
    #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('-------------------------------------------')
    
    #time series plots:
    #ranks = Fdf_1900_2080_means[3:].rank(axis=0)
    df3 = Fdf_1900_2080_annual
    Fdf_annual_sorted = df3.reindex_axis(df3.mean().sort_values().index, axis=1)
    Fdf_annual_sorted_subset = Fdf_annual_sorted.iloc[:,[0,1,2,3,5,7,13,15,18, 22, 24,25,30,33]]
    
    #write the key plots to a single pdf document
    pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary4.pdf'
    pdf_out_path = output_directory +'/' + pdf_out_file_name
    #open pdf and add the plots
    
    #developement
    #I want to create a simple density plot for all values in present day, near future and far future
    #this should be an easy indicator of change
    #subset present day simulations
    len(Fdf_1900_2080.columns)
    Full_current_df = Fdf_1900_2080.iloc[:,range(0,12)]
    Full_current_df = Full_current_df.stack()
    #nearfuture
    Full_nearfuture_df = Fdf_1900_2080.iloc[:,range(12,24)]
    Full_nearfuture_df = Full_nearfuture_df.stack()
    #farfuture
    Full_farfuture_df = Fdf_1900_2080.iloc[:,range(24,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 = ['presentday', 'nearfuture', 'farfuture']
    plt.title(Clim_var_type + ' -  density comparison - full period')
    Summarized_df.plot.kde()
    Summarized_df.boxplot(rot=90)
        
    
    #getting to more refined and meaningful plots
    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
    
    df = df.reset_index()
    df= df[df.index % 3 != 1]
    df['C'] = df[0].diff()
    a= df[df.index == df['C'].argmax(skipna=True)]
    a['index']
    df.iloc[[df['C'].argmax(skipna=True)]]
    df['C'].argmax(skipna=True)
    df['C'].argmin(skipna=True)
    df['C'].argmean(skipna=True)
    df[df['C']==df['C'].median()]
    max(df['C'])
    
    
    Fdf_1900_2080_sorted_means.plot(kind='bar').figure
    MIROC_R2_df = Fdf_1900_2080.iloc[:,[1,13,28]]
    #end of developement
    
    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 - one model')
        MIROC_R2_df.plot.kde()
        pdf.savefig(bbox_inches='tight', pad_inches=0.4)
        plt.close()
        #annual box
        plt.title(Clim_var_type + ' -  Annual means for one model')
        Fdf_1900_2080_annual.iloc[:,[1,13,28]].boxplot(rot=90)
        pdf.savefig(bbox_inches='tight', pad_inches=0.4)
        plt.close()
        #annual box
        plt.title(Clim_var_type + ' -  Annual means for one model B')
        Fdf_1900_2080_annual.iloc[:,[3,18,30]].boxplot(rot=90)
        pdf.savefig(bbox_inches='tight', pad_inches=0.4)
        plt.close()
        #annual box
        plt.title(Clim_var_type + ' -  Annual means for one model C')
        Fdf_1900_2080_annual.iloc[:,[8,17,26]].boxplot(rot=90)
        pdf.savefig(bbox_inches='tight', pad_inches=0.4)
        plt.close()
        #monthly box
        plt.title(Clim_var_type + ' -  Monthly means')
        Fdf_1900_2080_monthly.boxplot(rot=90)
        pdf.savefig(bbox_inches='tight', pad_inches=0.4)
        plt.close()
        #weekly box
        plt.title(Clim_var_type + ' -  Weekly means')
        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')
        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 - all models')
        Fdf_1900_2080_annual.plot(legend=False)
        pdf.savefig(bbox_inches='tight', pad_inches=0.4)
        plt.close()
        #
        plt.title(Clim_var_type + ' -  Time series - representative models')
        Fdf_annual_sorted_subset.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')
        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')
        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')
        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')
        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')
        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')
        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')
        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')
        Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].boxplot(rot=90)
        pdf.savefig(bbox_inches='tight', pad_inches=0.4)
        plt.close()

#plots not used  
#Fdf_annual_sorted_subset.plot(legend=False, subplots=True)