#added new code for also deriving quantile scaling factors from the NARCLIM data
tinoheimhuber
6 years ago
parent
0cd9a32a19
commit
ddf2ee38a3
@ -0,0 +1,520 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
#==========================================================#
|
||||||
|
#Last Updated - June 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')
|
||||||
|
|
||||||
|
# import own modules
|
||||||
|
# Set working direcotry (where postprocessed NARClIM data is located)
|
||||||
|
os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Analysis/Code')
|
||||||
|
import climdata_fcts as fct
|
||||||
|
import silo as sil
|
||||||
|
|
||||||
|
ALPHA_figs = 0
|
||||||
|
font = {'family' : 'sans-serif',
|
||||||
|
'weight' : 'normal',
|
||||||
|
'size' : 14}
|
||||||
|
matplotlib.rc('font', **font)
|
||||||
|
#==========================================================#
|
||||||
|
# Set working direcotry (where postprocessed NARClIM data is located)
|
||||||
|
os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/')
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
#set input parameters
|
||||||
|
Case_Study_Name = 'CASESTUDY2'
|
||||||
|
Version = 'V5' #V4 is latest for all model runs #V5 is just for the Hunter catchment climatology
|
||||||
|
|
||||||
|
Estuaries = ['HUNTER', 'RICHMOND', 'NADGEE', 'SHOALHAVEN', 'GEORGES','CATHIE']
|
||||||
|
Estuaries = ['HUNTER'] #,'HUNTER','RICHMOND']
|
||||||
|
#Estuaries = ['SHOALHAVEN']
|
||||||
|
for Est in Estuaries:
|
||||||
|
#Estuary = 'HUNTER' # 'Belongil'
|
||||||
|
Estuary = Est # 'Belongil'
|
||||||
|
print Estuary
|
||||||
|
#Clim_var_type = 'potevpmean' # '*' will create pdf for all variables in folder "pracc*|tasmax*"
|
||||||
|
Clim_var_types = ['pracc']
|
||||||
|
for climvar in Clim_var_types:
|
||||||
|
Clim_var_type = climvar
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
#set input preferences
|
||||||
|
#==========================================================#
|
||||||
|
plot_pdf = 'no'
|
||||||
|
plot_pngs = 'no'
|
||||||
|
delta_csv = 'yes'
|
||||||
|
Stats = 'mean' # 'maxdaily', 'mean', 'days_h_25'
|
||||||
|
DoMonthly = True
|
||||||
|
Perc_Vs_Abs = 'percent' #'percent' vs. 'absolute' deltas for near and far future
|
||||||
|
Location = 'Catchment'
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
#set directory path for output files
|
||||||
|
output_directory = 'Output/' + Case_Study_Name + '_' + Version + '/' + 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('-------------------------------------------')
|
||||||
|
|
||||||
|
#set directory path for individual png files
|
||||||
|
png_output_directory = 'Output/' + Case_Study_Name + '_' + Version + '/' + Estuary + '/NARCLIM_Key_Figs'
|
||||||
|
#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
|
||||||
|
if not os.path.exists(png_output_directory):
|
||||||
|
os.makedirs(png_output_directory)
|
||||||
|
print('-------------------------------------------')
|
||||||
|
print("output directory folder didn't exist and was generated")
|
||||||
|
print('-------------------------------------------')
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
Estuary_Folder = glob.glob('./Data/NARCLIM_Site_CSVs/'+ Case_Study_Name + '/' + Estuary + '*' )
|
||||||
|
if Location == 'Catchment':
|
||||||
|
Estuary_Folder = glob.glob('./Data/NARCLIM_Site_CSVs/'+ Case_Study_Name + '/' + Estuary + '*catch' )
|
||||||
|
Clim_Var_CSVs = glob.glob(Estuary_Folder[0] + '/' + Clim_var_type + '*')
|
||||||
|
Coordinates_foldername = os.path.basename(os.path.normpath(Estuary_Folder[0]))
|
||||||
|
Coordinates = Coordinates_foldername.split('_', 3)[1] + '_' +Coordinates_foldername.split('_', 3)[2]
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
#read CSV files and start analysis
|
||||||
|
#==========================================================#
|
||||||
|
#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']) #not part of all input csv files
|
||||||
|
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 in ['tasmean','tasmax','sstmean']:
|
||||||
|
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
|
||||||
|
if Clim_var_type in ['pr30maxtstep','pr10maxtstep','pr20maxtstep']:
|
||||||
|
Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*30 #int(Clim_var_type[2:4])
|
||||||
|
if Clim_var_type in ['pr1Hmaxtstep']:
|
||||||
|
Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60
|
||||||
|
if Clim_var_type in ['rsdsmean','rldsmean']:
|
||||||
|
Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60*24/1000000 #convert to unit used in SILO
|
||||||
|
Fdf_1900_2080 = Full_df
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
#Aggregate daily df to annual time series
|
||||||
|
if Clim_var_type in ['pracc' ,'evspsblmean' ,'potevpmean' ,'pr1Hmaxtstep' ,
|
||||||
|
'wss1Hmaxtstep', 'rsdsmean', 'rldsmean']:
|
||||||
|
if(Stats == 'maxdaily'):
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').max()
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
|
||||||
|
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').max() #seasonal means
|
||||||
|
Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
|
||||||
|
Fdf_Monthly_means = Fdf_1900_2080.resample('M').max() #seasonal means
|
||||||
|
Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
|
||||||
|
elif(Stats[:4] =='days'):
|
||||||
|
Threshold = int(Stats[-2:])
|
||||||
|
#agg = ('abobe_27_count', lambda x: x.gt(27).sum()), ('average', 'mean')
|
||||||
|
agg = ('>'+ str(Threshold) + '_count', lambda x: x.gt(Threshold).sum()),
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').agg(agg)
|
||||||
|
#Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
|
||||||
|
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').agg(agg) #seasonal means
|
||||||
|
#Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
|
||||||
|
Fdf_Monthly_means = Fdf_1900_2080.resample('M').agg(agg)
|
||||||
|
else:
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').sum()
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080_annual.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)
|
||||||
|
Fdf_Monthly_means = Fdf_1900_2080.resample('M').sum() #Monthlyonal means
|
||||||
|
Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
|
||||||
|
else:
|
||||||
|
if(Stats == 'maxdaily'):
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').max()
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
|
||||||
|
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').max() #seasonal means
|
||||||
|
Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
|
||||||
|
Fdf_Monthly_means = Fdf_1900_2080.resample('M').max() #Monthlyonal means
|
||||||
|
Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
|
||||||
|
elif(Stats == 'maxmonthly'):
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080.resample('M').max().resample('A').mean()
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
|
||||||
|
Fdf_Seas_means = Fdf_1900_2080.resample('M').max().resample('Q-NOV').mean() #seasonal means
|
||||||
|
Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
|
||||||
|
Fdf_Monthly_means = Fdf_1900_2080.resample('M').max().resample('Q-NOV').mean() #Monthlyonal means
|
||||||
|
Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
|
||||||
|
elif(Stats[:4] =='days'):
|
||||||
|
Threshold = int(Stats[-2:])
|
||||||
|
#agg = ('abobe_27_count', lambda x: x.gt(27).sum()), ('average', 'mean')
|
||||||
|
agg = ('>'+ str(Threshold) + '_count', lambda x: x.gt(Threshold).sum()),
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').agg(agg)
|
||||||
|
#Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
|
||||||
|
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').agg(agg) #seasonal means
|
||||||
|
#Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
|
||||||
|
Fdf_Monthly_means = Fdf_1900_2080.resample('M').agg(agg)
|
||||||
|
else:
|
||||||
|
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').mean()
|
||||||
|
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').mean() #seasonal means
|
||||||
|
Fdf_Monthly_means = Fdf_1900_2080.resample('M').mean() #seasonal means
|
||||||
|
Fdf_1900_2080_means = Fdf_1900_2080.mean()
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
# construction of quantile scaling method
|
||||||
|
#==========================================================#
|
||||||
|
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]
|
||||||
|
Quantile_change_df = quant_quant_scaling(Full_df, quantiles, False)
|
||||||
|
|
||||||
|
png_out_file_name = Clim_var_type + '_' + Stats + '_Deltas_Barplot_' + Version + '.png'
|
||||||
|
png_out_path = png_output_directory + '/' + png_out_file_name
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
#Select the 3 most representative models (min med and max difference betwen far future and present)
|
||||||
|
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)
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
#create a dataframe that has 1 column for each of the three representative models
|
||||||
|
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
|
||||||
|
#==========================================================#
|
||||||
|
delta_all_df = fct.calculate_deltas_NF_FF2(Fdf_1900_2080_annual, Fdf_Seas_means, Stats, Perc_Vs_Abs)
|
||||||
|
if DoMonthly ==True:
|
||||||
|
delta_all_df_monthly = fct.calculate_deltas_monthly(Fdf_Monthly_means, Stats, Perc_Vs_Abs)
|
||||||
|
delta_all_df_monthly = pd.concat([delta_all_df_monthly.filter(regex= 'near'),delta_all_df_monthly.filter(regex= 'far')], axis=1)[-3:]
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
if delta_csv == 'yes':
|
||||||
|
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_ensemble_changes_' + Perc_Vs_Abs + '_' + Location + '_' + Coordinates + '.csv'
|
||||||
|
delta_all_df.to_csv(output_directory + '/' + out_file_name)
|
||||||
|
#quantile scaling csv
|
||||||
|
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_quant_quant_' + Coordinates + '.csv'
|
||||||
|
Quantile_change_df.to_csv(output_directory + '/NARCLIM_Changes_Hunter/' + out_file_name)
|
||||||
|
if delta_csv == 'yes' and DoMonthly ==True:
|
||||||
|
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_ensemble_changes_monthly_' + Perc_Vs_Abs + '_' + Location + '_' + Coordinates + '.csv'
|
||||||
|
delta_all_df_monthly.to_csv(output_directory + '/NARCLIM_Changes_Hunter/' + out_file_name)
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
pd.qcut(range(5), 4)
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
#create a dataframe that has a single column for present day, near and far future for the (3 selected models)
|
||||||
|
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']
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
#generate colour schemes for plotting
|
||||||
|
#==========================================================#
|
||||||
|
plotcolours36 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal',
|
||||||
|
'darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal',
|
||||||
|
'darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal']
|
||||||
|
plotcolours36b = ['tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ,
|
||||||
|
'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ,
|
||||||
|
'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ]
|
||||||
|
plotcolours12 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal']
|
||||||
|
plotcolours15 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal', 'lightgreen','lightpink','slateblue']
|
||||||
|
color_dict = {'CCCMA3.1_R1': 'darkolivegreen', 'CCCMA3.1_R2': 'turquoise', 'CCCMA3.1_R3': 'lightgreen', 'CSIRO-MK3.0_R2': 'darkgreen',
|
||||||
|
'CSIRO-MK3.0_R1':'lightpink', 'CSIRO-MK3.0_R3':'slateblue','ECHAM5_R1':'slategray', 'ECHAM5_R2': 'orange', 'ECHAM5_R3': 'tomato',
|
||||||
|
'MIROC3.2_R1': 'peru', 'MIROC3.2_R2': 'navy' ,'MIROC3.2_R3': 'teal', '10th':'lightgreen','median':'lightpink','90th':'slateblue'}
|
||||||
|
#==========================================================#
|
||||||
|
|
||||||
|
|
||||||
|
#==========================================================#
|
||||||
|
#output crucial summary plots into individual png files
|
||||||
|
#==========================================================#
|
||||||
|
if plot_pngs == 'yes':
|
||||||
|
#=========#
|
||||||
|
#Barplot of near and far future deltas
|
||||||
|
#=========#
|
||||||
|
#out name
|
||||||
|
png_out_file_name = Clim_var_type + '_' + Stats + '_Deltas_Barplot_' + Version + '.png'
|
||||||
|
png_out_path = png_output_directory + '/' + png_out_file_name
|
||||||
|
#prepare data frame for plot
|
||||||
|
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)
|
||||||
|
#
|
||||||
|
fig = plt.figure(figsize=(5,6))
|
||||||
|
ax=plt.subplot(2,1,1)
|
||||||
|
plt.title(Clim_var_type + ' - ' + Stats + ' - deltas - near')
|
||||||
|
neardeltadf=delta_all_df['near']
|
||||||
|
neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
|
||||||
|
plt.xticks([])
|
||||||
|
ax=plt.subplot(2,1,2)
|
||||||
|
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()
|
||||||
Binary file not shown.
Loading…
Reference in New Issue