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199 lines
9.8 KiB
Python
199 lines
9.8 KiB
Python
7 years ago
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# -*- coding: utf-8 -*-
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#####################################----------------------------------
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#Last Updated - March 2018
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#@author: z5025317 Valentin Heimhuber
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#code for creating climate prioritization plots for NARCLIM variables.
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#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
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#####################################----------------------------------
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#Load packages
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#####################################----------------------------------
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import numpy as np
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import os
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import pandas as pd
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import glob
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import matplotlib
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import matplotlib.pyplot as plt
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from datetime import datetime
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from datetime import timedelta
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from matplotlib.backends.backend_pdf import PdfPages
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matplotlib.style.use('ggplot')
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#
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# Set working direcotry (where postprocessed NARClIM data is located)
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os.chdir('C:/Users/z5025317/WRL_Postdoc/Projects/Paper#1/NARCLIM/')
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#
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#
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#####################################----------------------------------
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#set input parameters
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Base_period_start = '1990-01-01'
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Base_period_end = '2080-01-01' #use last day that's not included in period as < is used for subsetting
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#Clim_var_type = 'tasmean' will create pdf for all variables in folder
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#####################################----------------------------------
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#set directory path for output files
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output_directory = 'Output'
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#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
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if not os.path.exists(output_directory):
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os.makedirs(output_directory)
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print("output directory folder didn't exist and was generated")
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Clim_Var_CSVs = glob.glob('./Site_CSVs/*')
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#Clim_Var_CSV = glob.glob('./Site_CSVs/' + Clim_var_type + '*' )
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#read CSV file
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for clim_var_csv_path in Clim_Var_CSVs:
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Filename = os.path.basename(os.path.normpath(clim_var_csv_path))
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Clim_var_type = Filename.split('_', 1)[0]
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print(clim_var_csv_path)
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Full_df = pd.read_csv(clim_var_csv_path, index_col=0, parse_dates = True)
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#pandas datestamp index to period (we don't need the 12 pm info in the index (daily periods are enough))
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Full_df.index = Full_df.index.to_period('D')
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#check data types of columns
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#Full_df.dtypes
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#substract a constant from all values (temp)
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if Clim_var_type == 'tasmean':
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Full_df = Full_df.iloc[:,0:26]-273.15
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#index the narclim periods if needed
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#Full_df.loc[(Full_df.index >= '1990-01-01') & (Full_df.index < '2010-01-01'), 'period']= '1990-2009'
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#Full_df.loc[(Full_df.index >= '2020-01-01') & (Full_df.index < '2040-01-01'), 'period']= '2020-2039'
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#Full_df.loc[(Full_df.index >= '2060-01-01') & (Full_df.index < '2080-01-01'), 'period']= '2060-2079'
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#Subset the data to the minimum base period and above (used to set the lenght of the present day climate period)
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Fdf_1900_2080 = Full_df.loc[(Full_df.index >= Base_period_start) & (Full_df.index < Base_period_end)]
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#Aggregate daily df to annual time series
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if (Clim_var_type == 'pracc' or Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean'
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or Clim_var_type == 'pr1Hmaxtstep' or Clim_var_type == 'wss1Hmaxtstep'):
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Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').sum()
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Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
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Fdf_1900_2080_monthly = Fdf_1900_2080.resample('M').sum()
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Fdf_1900_2080_monthly = Fdf_1900_2080_monthly.replace(0, np.nan)
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Fdf_1900_2080_weekly = Fdf_1900_2080.resample('W').sum()
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Fdf_1900_2080_weekly = Fdf_1900_2080_weekly.replace(0, np.nan)
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Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').sum() #seasonal means
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Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
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else:
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Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').mean()
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Fdf_1900_2080_monthly = Fdf_1900_2080.resample('M').mean()
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Fdf_1900_2080_weekly = Fdf_1900_2080.resample('W').mean()
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Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').mean() #seasonal means
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#plot the mean of all model runs
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print('-------------------------------------------')
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print('mean of all models for climate variable: ' + Clim_var_type)
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Fdf_1900_2080_means = Fdf_1900_2080.mean()
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Fdf_1900_2080_means.plot(kind='bar', ylim=(16,22)).figure
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print('-------------------------------------------')
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#time series plots:
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#ranks = Fdf_1900_2080_means[3:].rank(axis=0)
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df3 = Fdf_1900_2080_annual
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Fdf_annual_sorted = df3.reindex_axis(df3.mean().sort_values().index, axis=1)
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Fdf_annual_sorted_subset = Fdf_annual_sorted.iloc[:,[0,1,2,3,5,7,13,15,18, 22, 24,25]]
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#write the key plots to a single pdf document
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pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary4.pdf'
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pdf_out_path = output_directory +'/' + pdf_out_file_name
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#open pdf and add the plots
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#developement
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#I want to create a simple density plot for all values in present day, near future and far future
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#this should be an easy indicator of change
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#subset present day simulations
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Full_current_df = Fdf_1900_2080.iloc[:,[0,1,2]]
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Full_current_df = Full_current_df.stack()
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#nearfuture
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Full_nearfuture_df = Fdf_1900_2080.iloc[:,range(3,15)]
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Full_nearfuture_df = Full_nearfuture_df.stack()
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#farfuture
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Full_farfuture_df = Fdf_1900_2080.iloc[:,range(15,27)]
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Full_farfuture_df = Full_farfuture_df.stack()
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Summarized_df = pd.concat([Full_current_df, Full_nearfuture_df], axis=1, ignore_index=True)
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Summarized_df = pd.concat([Summarized_df, Full_farfuture_df], axis=1, ignore_index=True)
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Summarized_df.columns = ['presentday', 'nearfuture', 'farfuture']
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#end of developement
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with PdfPages(pdf_out_path) as pdf:
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#barplot of model means
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plt.title(Clim_var_type + ' - model means - full period')
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ymin = min(Fdf_1900_2080_means)
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ymax = max(Fdf_1900_2080_means)
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Fdf_1900_2080_means.plot(kind='bar', ylim=(ymin,ymax))
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Fdf_1900_2080_means.plot(kind='bar')
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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#full period density comparison
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plt.title(Clim_var_type + ' - density comparison - full period')
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Summarized_df.plot.kde()
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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#annual box
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plt.title(Clim_var_type + ' - Annual means')
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Fdf_1900_2080_annual.boxplot(rot=90)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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#monthly box
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plt.title(Clim_var_type + ' - Monthly means')
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Fdf_1900_2080_monthly.boxplot(rot=90)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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#weekly box
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plt.title(Clim_var_type + ' - Weekly means')
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Fdf_1900_2080_weekly.boxplot(rot=90)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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#daily box
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plt.title(Clim_var_type + ' - Daily means')
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Fdf_1900_2080.boxplot(rot=90)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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# time series plot annual ALL models
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plt.title(Clim_var_type + ' - Time series - all models')
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Fdf_1900_2080_annual.plot(legend=False)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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#
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plt.title(Clim_var_type + ' - Time series - representative models')
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Fdf_annual_sorted_subset.plot(legend=False)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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# seasonal mean boxplots
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ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean())
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ymax = max(Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean())
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plt.title(Clim_var_type + ' - DJF Summer means')
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Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean().plot(kind='bar', ylim=(ymin,ymax))
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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plt.title(Clim_var_type + ' - DJF Summer means')
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Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].boxplot(rot=90)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean())
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ymax = max(Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean())
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plt.title(Clim_var_type + ' - MAM Autumn means')
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Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean().plot(kind='bar', ylim=(ymin,ymax))
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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plt.title(Clim_var_type + ' - MAM Autumn means')
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Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].boxplot(rot=90)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean())
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ymax = max(Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean())
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plt.title(Clim_var_type + ' - JJA Winter means')
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Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean().plot(kind='bar', ylim=(ymin,ymax))
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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plt.title(Clim_var_type + ' - JJA Winter means')
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Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].boxplot(rot=90)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean())
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ymax = max(Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean())
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plt.title(Clim_var_type + ' - SON Spring means')
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Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean().plot(kind='bar', ylim=(ymin,ymax))
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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plt.title(Clim_var_type + ' - SON Spring means')
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Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].boxplot(rot=90)
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pdf.savefig(bbox_inches='tight', pad_inches=0.4)
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plt.close()
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#plots not used
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#Fdf_annual_sorted_subset.plot(legend=False, subplots=True)
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