#added new code for also deriving quantile scaling factors from the NARCLIM data
tinoheimhuber
6 years ago
parent
0cd9a32a19
commit
ddf2ee38a3
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# -*- coding: utf-8 -*-
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#==========================================================#
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#Last Updated - June 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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from ggplot import *
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matplotlib.style.use('ggplot')
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# import own modules
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# Set working direcotry (where postprocessed NARClIM data is located)
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os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Analysis/Code')
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import climdata_fcts as fct
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import silo as sil
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ALPHA_figs = 0
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font = {'family' : 'sans-serif',
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'weight' : 'normal',
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'size' : 14}
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matplotlib.rc('font', **font)
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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/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/')
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#==========================================================#
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#==========================================================#
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#set input parameters
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Case_Study_Name = 'CASESTUDY2'
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Version = 'V5' #V4 is latest for all model runs #V5 is just for the Hunter catchment climatology
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Estuaries = ['HUNTER', 'RICHMOND', 'NADGEE', 'SHOALHAVEN', 'GEORGES','CATHIE']
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Estuaries = ['HUNTER'] #,'HUNTER','RICHMOND']
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#Estuaries = ['SHOALHAVEN']
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for Est in Estuaries:
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#Estuary = 'HUNTER' # 'Belongil'
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Estuary = Est # 'Belongil'
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print Estuary
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#Clim_var_type = 'potevpmean' # '*' will create pdf for all variables in folder "pracc*|tasmax*"
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Clim_var_types = ['pracc']
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for climvar in Clim_var_types:
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Clim_var_type = climvar
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#==========================================================#
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#set input preferences
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#==========================================================#
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plot_pdf = 'no'
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plot_pngs = 'no'
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delta_csv = 'yes'
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Stats = 'mean' # 'maxdaily', 'mean', 'days_h_25'
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DoMonthly = True
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Perc_Vs_Abs = 'percent' #'percent' vs. 'absolute' deltas for near and far future
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Location = 'Catchment'
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#==========================================================#
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#==========================================================#
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#set directory path for output files
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output_directory = 'Output/' + Case_Study_Name + '_' + Version + '/' + Estuary
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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('-------------------------------------------')
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print("output directory folder didn't exist and was generated")
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print('-------------------------------------------')
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#set directory path for individual png files
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png_output_directory = 'Output/' + Case_Study_Name + '_' + Version + '/' + Estuary + '/NARCLIM_Key_Figs'
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#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
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if not os.path.exists(png_output_directory):
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os.makedirs(png_output_directory)
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print('-------------------------------------------')
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print("output directory folder didn't exist and was generated")
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print('-------------------------------------------')
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#==========================================================#
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#==========================================================#
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Estuary_Folder = glob.glob('./Data/NARCLIM_Site_CSVs/'+ Case_Study_Name + '/' + Estuary + '*' )
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if Location == 'Catchment':
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Estuary_Folder = glob.glob('./Data/NARCLIM_Site_CSVs/'+ Case_Study_Name + '/' + Estuary + '*catch' )
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Clim_Var_CSVs = glob.glob(Estuary_Folder[0] + '/' + Clim_var_type + '*')
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Coordinates_foldername = os.path.basename(os.path.normpath(Estuary_Folder[0]))
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Coordinates = Coordinates_foldername.split('_', 3)[1] + '_' +Coordinates_foldername.split('_', 3)[2]
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#==========================================================#
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#==========================================================#
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#read CSV files and start analysis
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#==========================================================#
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#for clim_var_csv_path in Clim_Var_CSVs:
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clim_var_csv_path = Clim_Var_CSVs[0]
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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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#Full_df = Full_df.drop(columns=['period']) #not part of all input csv files
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Ncols_df = len(Full_df)
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#check data types of columns
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#Full_df.dtypes
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#==========================================================#
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#==========================================================#
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#substract a constant from all values to convert from kelvin to celcius (temp)
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if Clim_var_type in ['tasmean','tasmax','sstmean']:
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Full_df = Full_df.iloc[:,0:(Ncols_df-1)]-273.15
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if Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean':
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Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60*24
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if Clim_var_type in ['pr30maxtstep','pr10maxtstep','pr20maxtstep']:
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Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*30 #int(Clim_var_type[2:4])
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if Clim_var_type in ['pr1Hmaxtstep']:
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Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60
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if Clim_var_type in ['rsdsmean','rldsmean']:
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Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60*24/1000000 #convert to unit used in SILO
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Fdf_1900_2080 = Full_df
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#==========================================================#
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#==========================================================#
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#Aggregate daily df to annual time series
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if Clim_var_type in ['pracc' ,'evspsblmean' ,'potevpmean' ,'pr1Hmaxtstep' ,
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'wss1Hmaxtstep', 'rsdsmean', 'rldsmean']:
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if(Stats == 'maxdaily'):
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Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').max()
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Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
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Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').max() #seasonal means
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Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
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Fdf_Monthly_means = Fdf_1900_2080.resample('M').max() #seasonal means
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Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
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elif(Stats[:4] =='days'):
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Threshold = int(Stats[-2:])
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#agg = ('abobe_27_count', lambda x: x.gt(27).sum()), ('average', 'mean')
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agg = ('>'+ str(Threshold) + '_count', lambda x: x.gt(Threshold).sum()),
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Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').agg(agg)
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#Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
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Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').agg(agg) #seasonal means
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#Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
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Fdf_Monthly_means = Fdf_1900_2080.resample('M').agg(agg)
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else:
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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_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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Fdf_Monthly_means = Fdf_1900_2080.resample('M').sum() #Monthlyonal means
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Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
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else:
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if(Stats == 'maxdaily'):
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Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').max()
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Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
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Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').max() #seasonal means
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Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
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Fdf_Monthly_means = Fdf_1900_2080.resample('M').max() #Monthlyonal means
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Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
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elif(Stats == 'maxmonthly'):
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Fdf_1900_2080_annual = Fdf_1900_2080.resample('M').max().resample('A').mean()
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Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
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Fdf_Seas_means = Fdf_1900_2080.resample('M').max().resample('Q-NOV').mean() #seasonal means
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Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
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Fdf_Monthly_means = Fdf_1900_2080.resample('M').max().resample('Q-NOV').mean() #Monthlyonal means
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Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
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elif(Stats[:4] =='days'):
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Threshold = int(Stats[-2:])
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#agg = ('abobe_27_count', lambda x: x.gt(27).sum()), ('average', 'mean')
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agg = ('>'+ str(Threshold) + '_count', lambda x: x.gt(Threshold).sum()),
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Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').agg(agg)
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#Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
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Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').agg(agg) #seasonal means
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#Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
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Fdf_Monthly_means = Fdf_1900_2080.resample('M').agg(agg)
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else:
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Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').mean()
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Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').mean() #seasonal means
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Fdf_Monthly_means = Fdf_1900_2080.resample('M').mean() #seasonal means
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Fdf_1900_2080_means = Fdf_1900_2080.mean()
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#==========================================================#
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#==========================================================#
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# construction of quantile scaling method
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#==========================================================#
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quantiles = [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.91,0.92,0.93,0.94,0.95,0.96,0.97,0.98,0.99,1]
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Quantile_change_df = quant_quant_scaling(Full_df, quantiles, False)
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png_out_file_name = Clim_var_type + '_' + Stats + '_Deltas_Barplot_' + Version + '.png'
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png_out_path = png_output_directory + '/' + png_out_file_name
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#==========================================================#
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#Select the 3 most representative models (min med and max difference betwen far future and present)
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dfall, dfmin, dfmax, dfmed, Min_dif_mod_name, Med_dif_mod_name, Max_dif_mod_name = fct.select_min_med_max_dif_model(Fdf_1900_2080)
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#==========================================================#
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#==========================================================#
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#create a dataframe that has 1 column for each of the three representative models
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dfa = Fdf_1900_2080_annual.iloc[:,[0]]
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dfa1 = Fdf_1900_2080_annual.iloc[:,[0,3,6]].loc[(Fdf_1900_2080_annual.index >= '1990') & (Fdf_1900_2080_annual.index <= '2009')]
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dfa1.columns = [Min_dif_mod_name[:-5], Med_dif_mod_name[:-5], Max_dif_mod_name[:-5]]
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dfa2 = Fdf_1900_2080_annual.iloc[:,[1,4,7]].loc[(Fdf_1900_2080_annual.index >= '2020') & (Fdf_1900_2080_annual.index <= '2039')]
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dfa2.columns = [Min_dif_mod_name[:-5], Med_dif_mod_name[:-5], Max_dif_mod_name[:-5]]
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dfa3 = Fdf_1900_2080_annual.iloc[:,[2,5,8]].loc[(Fdf_1900_2080_annual.index >= '2060') & (Fdf_1900_2080_annual.index <= '2079')]
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dfa3.columns = [Min_dif_mod_name[:-5], Med_dif_mod_name[:-5], Max_dif_mod_name[:-5]]
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dfall_annual = dfa1.append(dfa2).append(dfa3)
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#==========================================================#
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#==========================================================#
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#Create Deltas of average change for annual and seasonal basis
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#==========================================================#
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delta_all_df = fct.calculate_deltas_NF_FF2(Fdf_1900_2080_annual, Fdf_Seas_means, Stats, Perc_Vs_Abs)
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if DoMonthly ==True:
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delta_all_df_monthly = fct.calculate_deltas_monthly(Fdf_Monthly_means, Stats, Perc_Vs_Abs)
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delta_all_df_monthly = pd.concat([delta_all_df_monthly.filter(regex= 'near'),delta_all_df_monthly.filter(regex= 'far')], axis=1)[-3:]
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#==========================================================#
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if delta_csv == 'yes':
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out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_ensemble_changes_' + Perc_Vs_Abs + '_' + Location + '_' + Coordinates + '.csv'
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delta_all_df.to_csv(output_directory + '/' + out_file_name)
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#quantile scaling csv
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out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_quant_quant_' + Coordinates + '.csv'
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Quantile_change_df.to_csv(output_directory + '/NARCLIM_Changes_Hunter/' + out_file_name)
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if delta_csv == 'yes' and DoMonthly ==True:
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out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_ensemble_changes_monthly_' + Perc_Vs_Abs + '_' + Location + '_' + Coordinates + '.csv'
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delta_all_df_monthly.to_csv(output_directory + '/NARCLIM_Changes_Hunter/' + out_file_name)
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#==========================================================#
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#==========================================================#
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pd.qcut(range(5), 4)
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#==========================================================#
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#==========================================================#
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#create a dataframe that has a single column for present day, near and far future for the (3 selected models)
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Full_current_df = Fdf_1900_2080.iloc[:,range(0,3)]
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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(3,6)]
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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(6,len(Fdf_1900_2080.columns))]
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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 = ['present', 'near', 'far']
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#==========================================================#
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#==========================================================#
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#generate colour schemes for plotting
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#==========================================================#
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plotcolours36 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal',
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'darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal',
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'darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal']
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plotcolours36b = ['tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ,
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'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ,
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'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ]
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plotcolours12 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal']
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plotcolours15 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal', 'lightgreen','lightpink','slateblue']
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color_dict = {'CCCMA3.1_R1': 'darkolivegreen', 'CCCMA3.1_R2': 'turquoise', 'CCCMA3.1_R3': 'lightgreen', 'CSIRO-MK3.0_R2': 'darkgreen',
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'CSIRO-MK3.0_R1':'lightpink', 'CSIRO-MK3.0_R3':'slateblue','ECHAM5_R1':'slategray', 'ECHAM5_R2': 'orange', 'ECHAM5_R3': 'tomato',
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'MIROC3.2_R1': 'peru', 'MIROC3.2_R2': 'navy' ,'MIROC3.2_R3': 'teal', '10th':'lightgreen','median':'lightpink','90th':'slateblue'}
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#==========================================================#
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#==========================================================#
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#output crucial summary plots into individual png files
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#==========================================================#
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if plot_pngs == 'yes':
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#=========#
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#Barplot of near and far future deltas
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#=========#
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#out name
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png_out_file_name = Clim_var_type + '_' + Stats + '_Deltas_Barplot_' + Version + '.png'
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png_out_path = png_output_directory + '/' + png_out_file_name
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#prepare data frame for plot
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neardeltadf=delta_all_df['near']
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ymin = min(neardeltadf) + 0.1 *min(neardeltadf)
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ymax = max(neardeltadf) + 0.1 * max(neardeltadf)
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neardeltadf=delta_all_df['far']
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ymin2 = min(neardeltadf) + 0.1 *min(neardeltadf)
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ymax2 = max(neardeltadf) + 0.1 * max(neardeltadf)
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ymin = min(ymin, ymin2)
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if (Clim_var_type == 'tasmax' or Clim_var_type == 'tasmean'):
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ymin = 0
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ymax = max(ymax, ymax2)
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#
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fig = plt.figure(figsize=(5,6))
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ax=plt.subplot(2,1,1)
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plt.title(Clim_var_type + ' - ' + Stats + ' - deltas - near')
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neardeltadf=delta_all_df['near']
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neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
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plt.xticks([])
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ax=plt.subplot(2,1,2)
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plt.title(Clim_var_type + ' - ' + Stats + ' - deltas - far')
|
||||
neardeltadf=delta_all_df['far']
|
||||
neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
|
||||
ax.xaxis.grid(False)
|
||||
#ax.patch.set_alpha(ALPHA_figs)
|
||||
fig.patch.set_alpha(ALPHA_figs)
|
||||
fig.tight_layout()
|
||||
fig.savefig(png_out_path)
|
||||
plt.close()
|
||||
#=========#
|
||||
|
||||
#=========#
|
||||
#full period density comparison
|
||||
#=========#
|
||||
#out name
|
||||
png_out_file_name = Clim_var_type + '_' + Stats + '_MaxDeltaMod_Histogram_' + Version + '.png'
|
||||
png_out_path = png_output_directory + '/' + png_out_file_name
|
||||
plt.title(Clim_var_type + ' - ' + Stats + ' - hist - full period - max delta model')
|
||||
#prepare data
|
||||
xmin = float(max(np.nanpercentile(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]),50) - 4 * np.std(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]))))
|
||||
xmax = float(max(np.nanpercentile(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]),50) + 4 * np.std(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]))))
|
||||
fig = plt.figure(figsize=(5,5))
|
||||
ax=plt.subplot(2,1,1)
|
||||
Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]).plot.kde(xlim=(xmin,xmax), ax=ax)
|
||||
plt.legend(loc=9, bbox_to_anchor=(0.5, -0.3))
|
||||
#ax.xaxis.grid(False)
|
||||
ax.yaxis.grid(False)
|
||||
fig.patch.set_alpha(ALPHA_figs)
|
||||
fig.tight_layout()
|
||||
fig.savefig(png_out_path)
|
||||
plt.close()
|
||||
#=========#
|
||||
|
||||
#=========#
|
||||
# time series plot annual ALL models
|
||||
#=========#
|
||||
png_out_file_name = Clim_var_type + '_' + Stats + '_TimeSeries_AllMods_' + Version + '3.png'
|
||||
png_out_path = png_output_directory + '/' + png_out_file_name
|
||||
plt.title(Clim_var_type + ' - ' + Stats + ' - Time series - representative models')
|
||||
#prepare data
|
||||
# Mod_order = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,19,20,21,16,17,18,22,23,24,31,32,33,25,26,27,28,29,30,34,35,36]
|
||||
# test = Fdf_1900_2080_annual
|
||||
# Mod_Names = test.columns
|
||||
# New_Mod_Name = []
|
||||
# for i in range(0,len(Mod_Names)):
|
||||
# if(Stats[:4] =='days'):
|
||||
# New_Mod_Name.append(str(Mod_order[i]+10) + '_' + Mod_Names[i][0])
|
||||
# else:
|
||||
# New_Mod_Name.append(str(Mod_order[i]+10) + '_' + Mod_Names[i])
|
||||
# test.columns = New_Mod_Name
|
||||
# test_sorted = test.reindex_axis(sorted(test.columns), axis=1)
|
||||
# colnamest = test.columns
|
||||
# test_sorted.columns = [w[3:-5] for w in colnamest]
|
||||
|
||||
#plot
|
||||
fig = plt.figure(figsize=(8,7))
|
||||
ax=plt.subplot(2,1,1)
|
||||
#test_sorted.plot(ax=ax) #,color = plotcolours36)
|
||||
Fdf_1900_2080_annual_b = Fdf_1900_2080_annual
|
||||
New_Mod_Name = []
|
||||
if(Stats[:4] =='days'):
|
||||
for each in Fdf_1900_2080_annual_b.columns:
|
||||
New_Mod_Name.append(each[0][:-5])
|
||||
else:
|
||||
for each in Fdf_1900_2080_annual_b.columns:
|
||||
New_Mod_Name.append(each[:-5])
|
||||
Fdf_1900_2080_annual_b.columns = New_Mod_Name
|
||||
Fdf_1900_2080_annual_b.plot(ax=ax, color=[color_dict[x] for x in Fdf_1900_2080_annual_b.columns])
|
||||
plt.legend(loc=9, bbox_to_anchor=(0.5, -0.2))
|
||||
ax.xaxis.grid(False)
|
||||
fig.patch.set_alpha(ALPHA_figs)
|
||||
fig.tight_layout()
|
||||
fig.savefig(png_out_path)
|
||||
plt.close()
|
||||
#=========#
|
||||
|
||||
#==========================================================#
|
||||
#output some summary plot into pdf
|
||||
#==========================================================#
|
||||
if plot_pdf == 'yes':
|
||||
#plt.cm.Paired(np.arange(len(Fdf_1900_2080_means)))
|
||||
#write the key plots to a single pdf document
|
||||
pdf_out_file_name = Clim_var_type + '_' + Stats + '_NARCliM_summary_' + Version + '.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) + 0.008 *min(Fdf_1900_2080_means)
|
||||
Fdf_1900_2080_means.plot(kind='bar', ylim=(ymin,ymax), color=plotcolours36)
|
||||
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
|
||||
plt.close()
|
||||
#
|
||||
neardeltadf=delta_all_df['near']
|
||||
ymin = min(neardeltadf) + 0.1 *min(neardeltadf)
|
||||
ymax = max(neardeltadf) + 0.1 * max(neardeltadf)
|
||||
neardeltadf=delta_all_df['far']
|
||||
ymin2 = min(neardeltadf) + 0.1 *min(neardeltadf)
|
||||
ymax2 = max(neardeltadf) + 0.1 * max(neardeltadf)
|
||||
ymin = min(ymin, ymin2)
|
||||
if (Clim_var_type == 'tasmax' or Clim_var_type == 'tasmean'):
|
||||
ymin = 0
|
||||
ymax = max(ymax, ymax2)
|
||||
#
|
||||
# delta barplot for report 1#################################
|
||||
ax=plt.subplot(2,1,1)
|
||||
plt.title(Clim_var_type + ' - model deltas - near-present')
|
||||
neardeltadf=delta_all_df['near']
|
||||
neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
|
||||
ax.patch.set_alpha(ALPHA_figs)
|
||||
plt.xticks([])
|
||||
#ax.xaxis.set_ticklabels([])
|
||||
#pdf.savefig(bbox_inches='tight', ylim=(ymin,ymax), pad_inches=0.4)
|
||||
#plt.close()
|
||||
#
|
||||
ax=plt.subplot(2,1,2)
|
||||
plt.title(Clim_var_type + ' - model deltas - far-present')
|
||||
neardeltadf=delta_all_df['far']
|
||||
fig = neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
|
||||
ax.xaxis.grid(False)
|
||||
ax.patch.set_alpha(ALPHA_figs)
|
||||
#fig.patch.set_alpha(ALPHA_figs)
|
||||
#plt.show()
|
||||
pdf.savefig(bbox_inches='tight', ylim=(ymin,ymax), pad_inches=0.4)
|
||||
plt.close()
|
||||
# end delta barplot for report 1#################################
|
||||
#
|
||||
#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()
|
||||
#full period density comparison
|
||||
plt.title(Clim_var_type + ' - density comparison - full period - max delta model')
|
||||
xmin = float(max(np.nanpercentile(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]),50) - 4 * np.std(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]))))
|
||||
xmax = float(max(np.nanpercentile(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]),50) + 4 * np.std(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]))))
|
||||
Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]).plot.kde(xlim=(xmin,xmax))
|
||||
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
|
||||
fig.patch.set_alpha(ALPHA_figs)
|
||||
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()
|
||||
#
|
||||
#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 - all models')
|
||||
Mod_order = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,19,20,21,16,17,18,22,23,24,31,32,33,25,26,27,28,29,30,34,35,36]
|
||||
test = Fdf_1900_2080_annual
|
||||
Mod_Names = test.columns
|
||||
New_Mod_Name = []
|
||||
for i in range(0,len(Mod_Names)):
|
||||
New_Mod_Name.append(str(Mod_order[i]+10) + '_' + Mod_Names[i])
|
||||
test.columns = New_Mod_Name
|
||||
test_sorted = test.reindex_axis(sorted(test.columns), axis=1)
|
||||
colnamest = test.columns
|
||||
test_sorted.columns = [w[3:-5] for w in colnamest]
|
||||
fig = test_sorted.plot(legend=False, color = plotcolours36)
|
||||
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
|
||||
fig.patch.set_alpha(ALPHA_figs)
|
||||
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')
|
||||
pd.DataFrame(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')
|
||||
pd.DataFrame(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')
|
||||
pd.DataFrame(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')
|
||||
pd.DataFrame(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()
|
||||
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Reference in New Issue