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#This commit comprises a major update to the

Browse Source 
NARCLIM interrogation set of scripts.
#Many of the plots have been updated to
to be used in the OEH technical guide.
#Several improvements have been made.
Development1
tinoheimhuber 7 years ago
parent 8a771b65b6
commit d907d640f8
4 changed files with 167 additions and 73 deletions
Show Stats Download Patch File Download Diff File

45
Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py
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@ -22,16 +22,18 @@ matplotlib.style.use('ggplot')
#plt.rcParams.update(plt.rcParamsDefault) #plt.rcParams.update(plt.rcParamsDefault)
# #
# Set working direcotry (where postprocessed NARClIM data is located) # Set working direcotry (where postprocessed NARClIM data is located)
os.chdir('C:/Users/z5025317/WRL_Postdoc/Projects/Paper#1/') os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/')
# #
#####################################---------------------------------- #####################################----------------------------------
#set input parameters #set input parameters
Base_period_start = '1986-01-01' Base_period_start = '1986-01-01'
Base_period_end = '2005-01-01' #use last day that's not included in period as < is used for subsetting Base_period_end = '2005-01-01' #use last day that's not included in period as < is used for subsetting
Estuary = 'Terrigal' # 'Belongil' Estuary = 'Nadgee' # 'Belongil'
Clim_var_type = "*" # '*' will create pdf for all variables in folder Clim_var_type = "*" # '*' will create pdf for all variables in folder
Clim_var_type = "wssmean*" # '*' will create pdf for all variables in folder Clim_var_type = "tasmean*" # '*' will create pdf for all variables in folder
Present_Day_Clim_Var = 'Wind' #MaxT, MinT, Rainfall, ET Present_Day_Clim_Var = 'Wind' #MaxT, MinT, Rainfall, ET
present_day_plot = 'yes'
Version = "V1"
#####################################---------------------------------- #####################################----------------------------------
#set directory path for output files #set directory path for output files
@ -117,6 +119,7 @@ fig = plt.figure(figsize=(14,8))
delta_all_df = pd.DataFrame() delta_all_df = pd.DataFrame()
i=1 i=1
for temp in times: for temp in times:
temp = 'annual'
#subset the ensemble dataframe for the period used: #subset the ensemble dataframe for the period used:
if temp == 'annual': if temp == 'annual':
Ensemble_Delta_df = Ensemble_Delta_full_df.iloc[:,range(0,2)] Ensemble_Delta_df = Ensemble_Delta_full_df.iloc[:,range(0,2)]
@ -193,19 +196,53 @@ for temp in times:
for tick in ax.get_xticklabels(): for tick in ax.get_xticklabels():
tick.set_rotation(0) tick.set_rotation(0)
fig.tight_layout() fig.tight_layout()
fig.patch.set_alpha(0)
#reset i to i+1 for next step #reset i to i+1 for next step
if temp == 'MAM': if temp == 'MAM':
i=i+2 i=i+2
else: else:
i=i+1 i=i+1
print(i) print(i)
plt.show() plt.show()
out_file_name = Estuary + '_' + Clim_var_type + '_CC_prio_plot.png' out_file_name = Estuary + '_' + Clim_var_type + '_CC_prio_plot' + Version + '.png'
out_path = output_directory + '/' + out_file_name out_path = output_directory + '/' + out_file_name
fig.savefig(out_path) fig.savefig(out_path)
if present_day_plot == 'yes':
#print present day climate data
fig = plt.figure(figsize=(5,4))
ax = fig.add_subplot(1, 1, 1)
if temp == 'annual':
xmin = int(min(Plot_in_df.min(axis=1))-minplotDelta)
xmax = int(max(Plot_in_df.max(axis=1))+maxplotDelta)
else:
xmin = int(min(Plot_in_df.min(axis=1))-minplotDelta)
xmax = int(max(Plot_in_df.max(axis=1))+maxplotDelta)
Present_Day_ref_df.plot(legend=False, ax=ax)
z = plt.axhline(float(Present_Day_Mean-2*Present_Day_SD), linestyle='-', color='black', alpha=.5)
z.set_zorder(-1)
z = plt.axhline(float(Present_Day_Mean+2*Present_Day_SD), linestyle='-', color='black', alpha=.5)
z.set_zorder(-1)
z = plt.axhline(float(Present_Day_Mean-Present_Day_SD), linestyle='--', color='black', alpha=.5)
z.set_zorder(-1)
z = plt.axhline(float(Present_Day_Mean+Present_Day_SD), linestyle='--', color='black', alpha=.5)
z.set_zorder(-1)
z = plt.axhline(float(Present_Day_Mean), linestyle='--', color='red', alpha=.5)
z.set_zorder(-1)
#fig.patch.set_facecolor('deepskyblue')
fig.patch.set_alpha(0)
plt.ylim(13, xmax)
plt.show()
out_file_name = 'C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/OEH_Coastal_Node_Deliverables/Technical_Report_1/Figures/tasmean_present_day_manual_backgroundTP.png'
out_path = output_directory + '/' + out_file_name
fig.savefig(out_file_name)
# use transparent=True if you want the whole figure with a transparent background

27
Analysis/Code/P1_NARCliM_NC_to_CSV_CCRC_SS.py
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@ -1,6 +1,7 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
from netCDF4 import * from netCDF4 import *
import numpy as np import numpy as np
from numpy import *
import os import os
import pandas as pd import pandas as pd
import glob import glob
@ -33,22 +34,16 @@ if __name__ == "__main__":
Timestep = args.timestep Timestep = args.timestep
print("Extracting all NARCLIM time series for variable: ", Clim_var_type, " for lat lon: ", mylat, mylon, "domain", NC_Domain, "timestep ", Timestep) print("Extracting all NARCLIM time series for variable: ", Clim_var_type, " for lat lon: ", mylat, mylon, "domain", NC_Domain, "timestep ", Timestep)
#set directory path for output files #set directory path for output files
output_directory = '/srv/ccrc/data02/z5025317/NARCliM_out' output_directory = '/srv/ccrc/data02/z5025317/NARCliM_out/' + str(abs(round(mylat,3))) + '_' + str(round(mylon, 3)) + '/'
#output_directory = 'J:\Project wrl2016032\NARCLIM_Raw_Data\Extracted' #output_directory = 'J:\Project wrl2016032\NARCLIM_Raw_Data\Extracted'
if not os.path.exists(output_directory): if not os.path.exists(output_directory):
os.makedirs(output_directory) os.makedirs(output_directory)
print("output directory folder didn't exist and was generated here:") print("output directory folder didn't exist and was generated here:")
print(output_directory) print(output_directory)
# #
time.sleep(10) #time.sleep(10)
#manual input via script
#mylat= -33.9608578
#mylon= 151.1339882
#Clim_var_type = 'pr1Hmaxtstep'
#NC_Domain = 'd02'
#
#set up the loop variables for interrogating the entire NARCLIM raw data #set up the loop variables for interrogating the entire NARCLIM raw data
NC_Periods = ('1950-2009','2020-2039','2060-2079') NC_Periods = ('1990-2009','2020-2039','2060-2079')
# #
#Define empty pandas data frames #Define empty pandas data frames
Full_df = pd.DataFrame() Full_df = pd.DataFrame()
@ -61,16 +56,29 @@ for NC_Period in NC_Periods:
Period_short = NC_Period[:4] Period_short = NC_Period[:4]
GCMs = os.listdir('./'+ NC_Period) GCMs = os.listdir('./'+ NC_Period)
for GCM in GCMs: for GCM in GCMs:
print GCM
Warf_runs = os.listdir('./' + NC_Period + '/' + GCM + '/') Warf_runs = os.listdir('./' + NC_Period + '/' + GCM + '/')
for Warf_run in Warf_runs: for Warf_run in Warf_runs:
Current_input_dir = './' + NC_Period + '/' + GCM + '/' + Warf_run + '/' + NC_Domain + '/' Current_input_dir = './' + NC_Period + '/' + GCM + '/' + Warf_run + '/' + NC_Domain + '/'
print Current_input_dir print Current_input_dir
Climvar_ptrn = '*' + Timestep + '_*' + Clim_var_type + '.nc' Climvar_ptrn = '*' + Timestep + '_*' + Clim_var_type + '.nc'
Climvar_NCs = glob.glob(Current_input_dir + Climvar_ptrn) Climvar_NCs = glob.glob(Current_input_dir + Climvar_ptrn)
print "test"
print Climvar_NCs[1]
#Climvar_NCs = Climvar_NCs[0:2] #Climvar_NCs = Climvar_NCs[0:2]
#print(Climvar_NCs) #print(Climvar_NCs)
for netcdf in Climvar_NCs: for netcdf in Climvar_NCs:
print "test2"
f=Dataset(netcdf) f=Dataset(netcdf)
# This section print on the screen information contained in the headings of the file
print '---------------------------------------------------------'
print f.ncattrs()
print f.title
print f.variables
print
for varname in f.variables:
print varname,' -> ',shape(f.variables[varname])
print '---------------------------------------------------------'
# Based on the desired inputs, this finds the nearest grid centerpoint index (x,y) in the *.nc file # Based on the desired inputs, this finds the nearest grid centerpoint index (x,y) in the *.nc file
dist_x=np.abs(f.variables['lon'][:,:]-float(mylon)) dist_x=np.abs(f.variables['lon'][:,:]-float(mylon))
dist_y=np.abs(f.variables['lat'][:,:]-float(mylat)) dist_y=np.abs(f.variables['lat'][:,:]-float(mylat))
@ -95,6 +103,7 @@ for NC_Period in NC_Periods:
df1=pd.DataFrame(d, index=timestamp_dates) df1=pd.DataFrame(d, index=timestamp_dates)
f.close() f.close()
print 'closing '+ os.path.basename(os.path.normpath(netcdf)) + ' moving to next netcdf file' print 'closing '+ os.path.basename(os.path.normpath(netcdf)) + ' moving to next netcdf file'
#print f
print '---------------------------------------------------------' print '---------------------------------------------------------'
#append in time direction each new time series to the data frame #append in time direction each new time series to the data frame
MultiNC_df = MultiNC_df.append(df1) MultiNC_df = MultiNC_df.append(df1)

4
Analysis/Code/P1_NARCliM_NC_to_CSV_CCRC_SS_BASH_script_readme.txt
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@ -61,8 +61,8 @@ Present_Day_Clim_Var = 'MaxT' #MaxT, MinT, Rainfall, (the name for present da
##PROBLEM: Without changing anything, the P1_NARCliM_NC_to_CSV_CCRC_SS.py stopped working properly on the CCRC storm servers. It's not giving an error but loading the nc files with Dataset(nc) just takes unlimited time. ##PROBLEM: Without changing anything, the P1_NARCliM_NC_to_CSV_CCRC_SS.py stopped working properly on the CCRC storm servers. It's not giving an error but loading the nc files with Dataset(nc) just takes unlimited time.
It used to take only a few seconds. NOT solved yet as of 7th of May 2018. It used to take only a few seconds. NOT solved yet as of 7th of May 2018.
running a simple netcdf info script
python /srv/ccrc/data02/z5025317/Code_execution/P1_Basic_NETCDF_Interrogation.py

164
Analysis/Code/P1_NARCliM_plots_Windows.py
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@ -20,16 +20,16 @@ from ggplot import *
matplotlib.style.use('ggplot') matplotlib.style.use('ggplot')
# #
# Set working direcotry (where postprocessed NARClIM data is located) # Set working direcotry (where postprocessed NARClIM data is located)
os.chdir('C:/Users/z5025317/WRL_Postdoc/Projects/Paper#1/') os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/')
# #
#####################################---------------------------------- #####################################----------------------------------
#set input parameters #set input parameters
Base_period_start = '1990-01-01' 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 Base_period_end = '2080-01-01' #use last day that's not included in period as < is used for subsetting
Estuary = 'Terrigal' # 'Belongil' Estuary = 'Nadgee' # 'Belongil'
Clim_var_type = "wssmean*" # '*' will create pdf for all variables in folder "pracc*|tasmax*" Clim_var_type = "tasmean*" # '*' will create pdf for all variables in folder "pracc*|tasmax*"
subset_ensemble = 'yes' # is yes, only the model with the lowest, median and max difference between present day and far future are selected plot_pdf = 'yes'
plot_pdf = 'no' delta_csv = 'no'
#####################################---------------------------------- #####################################----------------------------------
# #
#set directory path for output files #set directory path for output files
@ -63,7 +63,6 @@ for clim_var_csv_path in Clim_Var_CSVs:
if Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean': if Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean':
Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60*24 Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60*24
Fdf_1900_2080 = Full_df Fdf_1900_2080 = Full_df
#Subset the data to the minimum base period and above (used to set the lenght of the present day climate period) #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.loc[(Full_df.index >= Base_period_start) & (Full_df.index < Base_period_end)] # not necessary if not using reanalysis models for base period
@ -88,52 +87,52 @@ for clim_var_csv_path in Clim_Var_CSVs:
print('-------------------------------------------') print('-------------------------------------------')
print('mean of all models for climate variable: ' + Clim_var_type) print('mean of all models for climate variable: ' + Clim_var_type)
Fdf_1900_2080_means = Fdf_1900_2080.mean() Fdf_1900_2080_means = Fdf_1900_2080.mean()
Fdf_1900_2080_means.plot(kind='bar').figure #Fdf_1900_2080_means.columns = ['Mean']
#Fdf_1900_2080_means.plot(kind='bar').figure
print('-------------------------------------------') print('-------------------------------------------')
if subset_ensemble == 'yes': #Select the 3 most representative models (min med and max difference betwen far future and present)
#Select the 3 most representative models (min med and max difference betwen far future and present) Fdf_1900_2080_sorted = Fdf_1900_2080.reindex_axis(sorted(Fdf_1900_2080.columns), axis=1)
Fdf_1900_2080_sorted = Fdf_1900_2080.reindex_axis(sorted(Fdf_1900_2080.columns), axis=1) Fdf_1900_2080_sorted_means = pd.DataFrame(Fdf_1900_2080_sorted.mean())
Fdf_1900_2080_sorted_means = pd.DataFrame(Fdf_1900_2080_sorted.mean()) df = Fdf_1900_2080_sorted_means
df = Fdf_1900_2080_sorted_means #add a simple increasing integer index
#add a simple increasing integer index df = df.reset_index()
df = df.reset_index() df= df[df.index % 3 != 1]
df= df[df.index % 3 != 1] df['C'] = df[0].diff()
df['C'] = df[0].diff() df = df.reset_index()
df = df.reset_index() df= df[df.index % 2 != 0]
df= df[df.index % 2 != 0] #get max difference model (difference between far future and prsent day)
#get max difference model (difference between far future and prsent day) a = df[df.index == df['C'].argmax(skipna=True)]
a = df[df.index == df['C'].argmax(skipna=True)] Max_dif_mod_name = a.iloc[0]['index']
Max_dif_mod_name = a.iloc[0]['index'] #get min difference model
#get min difference model a = df[df.index == df['C'].argmin(skipna=True)]
a = df[df.index == df['C'].argmin(skipna=True)] Min_dif_mod_name = a.iloc[0]['index']
Min_dif_mod_name = a.iloc[0]['index'] #get the model which difference is closest to the median difference
#get the model which difference is closest to the median difference df['D'] = abs(df['C']- df['C'].median())
df['D'] = abs(df['C']- df['C'].median()) a = df[df.index == df['D'].argmin(skipna=True)]
a = df[df.index == df['D'].argmin(skipna=True)] Med_dif_mod_name = a.iloc[0]['index']
Med_dif_mod_name = a.iloc[0]['index'] #data frame with min med and max difference model
#data frame with min med and max difference model df2 = Fdf_1900_2080.filter(regex= Min_dif_mod_name[:-5] + '|' + Med_dif_mod_name[:-5] + '|' + Max_dif_mod_name[:-5] )
df2 = Fdf_1900_2080.filter(regex= Min_dif_mod_name[:-5] + '|' + Med_dif_mod_name[:-5] + '|' + Max_dif_mod_name[:-5] ) dfall = df2.reindex_axis(sorted(df2.columns), axis=1)
dfall = df2.reindex_axis(sorted(df2.columns), axis=1) #data frame with individual models
#data frame with individual models dfmin = Fdf_1900_2080.filter(regex= Min_dif_mod_name[:-5])
dfmin = Fdf_1900_2080.filter(regex= Min_dif_mod_name[:-5]) dfmax = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5])
dfmax = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]) dfmed = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5])
dfmed = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]) # use only the 3 representative models for the analysis
# use only the 3 representative models for the analysis Fdf_1900_2080_all_mods = Fdf_1900_2080
Fdf_1900_2080_all_mods = Fdf_1900_2080 #create a dataframe that has 1 column for each of the three representative models
#create a dataframe that has 1 column for each of the three representative models
# Full_df.loc[(Full_df.index > '1990-01-01') & (Full_df.index < '2009-01-01'), 'period']= '1990-2009' # Full_df.loc[(Full_df.index > '1990-01-01') & (Full_df.index < '2009-01-01'), 'period']= '1990-2009'
# Full_df.loc[(Full_df.index > '2020-01-01') & (Full_df.index < '2039-01-01'), 'period']= '2020-2039' # Full_df.loc[(Full_df.index > '2020-01-01') & (Full_df.index < '2039-01-01'), 'period']= '2020-2039'
# Full_df.loc[(Full_df.index > '2060-01-01') & (Full_df.index < '2079-01-01'), 'period']= '2060-2079' # Full_df.loc[(Full_df.index > '2060-01-01') & (Full_df.index < '2079-01-01'), 'period']= '2060-2079'
dfa = Fdf_1900_2080_annual.iloc[:,[0]] 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 = 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]] 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 = 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]] 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 = 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]] dfa3.columns = [Min_dif_mod_name[:-5], Med_dif_mod_name[:-5], Max_dif_mod_name[:-5]]
dfall_annual = dfa1.append(dfa2).append(dfa3) dfall_annual = dfa1.append(dfa2).append(dfa3)
#Create Deltas of average change for annual and seasonal basis #Create Deltas of average change for annual and seasonal basis
times = ['annual', 'DJF', 'MAM', 'JJA','SON'] times = ['annual', 'DJF', 'MAM', 'JJA','SON']
delta_all_df = pd.DataFrame() delta_all_df = pd.DataFrame()
@ -183,9 +182,10 @@ for clim_var_csv_path in Clim_Var_CSVs:
#append df to overall df #append df to overall df
delta_all_df = pd.concat([delta_all_df, delta_df], axis=1) delta_all_df = pd.concat([delta_all_df, delta_df], axis=1)
out_file_name = Estuary + '_' + Clim_var_type + '_NARCliM_ensemble_changes.csv' if delta_csv == 'yes':
out_path = output_directory + '/' + out_file_name out_file_name = Estuary + '_' + Clim_var_type + '_NARCliM_ensemble_changes.csv'
delta_all_df.to_csv(out_path) out_path = output_directory + '/' + out_file_name
delta_all_df.to_csv(out_path)
#create a dataframe that has a single column for present day, near and far future for the (3 selected models) #create a dataframe that has a single column for present day, near and far future for the (3 selected models)
len(Fdf_1900_2080.columns) len(Fdf_1900_2080.columns)
@ -203,23 +203,57 @@ for clim_var_csv_path in Clim_Var_CSVs:
#output some summary plot into pdf #output some summary plot into pdf
if plot_pdf == 'yes': if plot_pdf == 'yes':
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']
#plt.cm.Paired(np.arange(len(Fdf_1900_2080_means)))
#write the key plots to a single pdf document #write the key plots to a single pdf document
pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary_3.pdf' pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary_9.pdf'
pdf_out_path = output_directory +'/' + pdf_out_file_name pdf_out_path = output_directory +'/' + pdf_out_file_name
#open pdf and add the plots #open pdf and add the plots
with PdfPages(pdf_out_path) as pdf: with PdfPages(pdf_out_path) as pdf:
#barplot of model means #barplot of model means
plt.title(Clim_var_type + ' - model means - full period') plt.title(Clim_var_type + ' - model means - full period')
ymin = min(Fdf_1900_2080_means) ymin = min(Fdf_1900_2080_means)
ymax = max(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)) Fdf_1900_2080_means.plot(kind='bar', ylim=(ymin,ymax), color=plotcolours36)
pdf.savefig(bbox_inches='tight', pad_inches=0.4) pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close() plt.close()
#
plt.title(Clim_var_type + ' - model deltas - far-present')
neardeltadf=delta_all_df['far']
ymin = 0 #min(neardeltadf) - 0.008 *min(neardeltadf)
ymax = max(neardeltadf) + 0.008 * max(neardeltadf)
neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax))
#fig.patch.set_alpha(0)
pdf.savefig(bbox_inches='tight', ylim=(ymin,ymax), pad_inches=0.4)
plt.close()
#
plt.title(Clim_var_type + ' - model deltas - near-present')
neardeltadf=delta_all_df['near']
#ymin = 0 #min(neardeltadf) - 0.008 *min(neardeltadf)
#ymax = max(neardeltadf) + 0.008 *max(neardeltadf)
neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax))
pdf.savefig(bbox_inches='tight', ylim=(ymin,ymax), pad_inches=0.4)
plt.close()
#full period density comparison #full period density comparison
plt.title(Clim_var_type + ' - density comparison - full period - all models') plt.title(Clim_var_type + ' - density comparison - full period - all models')
Summarized_df.plot.kde() Summarized_df.plot.kde()
pdf.savefig(bbox_inches='tight', pad_inches=0.4) pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close() 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)
plt.close()
#annual box #annual box
plt.title(Clim_var_type + ' - Annual means/sums for max diff model') plt.title(Clim_var_type + ' - Annual means/sums for max diff model')
Fdf_1900_2080_annual.boxplot(rot=90) Fdf_1900_2080_annual.boxplot(rot=90)
@ -256,16 +290,30 @@ for clim_var_csv_path in Clim_Var_CSVs:
pdf.savefig(bbox_inches='tight', pad_inches=0.4) pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close() plt.close()
# time series plot annual ALL models # 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]
test_sorted.plot(legend=False, color = plotcolours36)
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close()
# time series plot annual ALL models
plt.title(Clim_var_type + ' - Time series - representative models') plt.title(Clim_var_type + ' - Time series - representative models')
dfall_annual.plot(legend=False) dfall_annual.plot(legend=False)
pdf.savefig(bbox_inches='tight', pad_inches=0.4) pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close() plt.close()
# seasonal mean boxplots # seasonal mean boxplots
ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean()) ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean())
ymax = max(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') plt.title(Clim_var_type + ' - DJF Summer means/sums')
Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean().plot(kind='bar', ylim=(ymin,ymax)) 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) pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close() plt.close()
plt.title(Clim_var_type + ' - DJF Summer means/sums') plt.title(Clim_var_type + ' - DJF Summer means/sums')
@ -275,7 +323,7 @@ for clim_var_csv_path in Clim_Var_CSVs:
ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean()) ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean())
ymax = max(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') plt.title(Clim_var_type + ' - MAM Autumn means/sums')
Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean().plot(kind='bar', ylim=(ymin,ymax)) 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) pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close() plt.close()
plt.title(Clim_var_type + ' - MAM Autumn means/sums') plt.title(Clim_var_type + ' - MAM Autumn means/sums')
@ -285,7 +333,7 @@ for clim_var_csv_path in Clim_Var_CSVs:
ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean()) ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean())
ymax = max(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') plt.title(Clim_var_type + ' - JJA Winter means/sums')
Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean().plot(kind='bar', ylim=(ymin,ymax)) 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) pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close() plt.close()
plt.title(Clim_var_type + ' - JJA Winter means/sums') plt.title(Clim_var_type + ' - JJA Winter means/sums')
@ -295,7 +343,7 @@ for clim_var_csv_path in Clim_Var_CSVs:
ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean()) ymin = min(Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean())
ymax = max(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') plt.title(Clim_var_type + ' - SON Spring means/sums')
Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean().plot(kind='bar', ylim=(ymin,ymax)) 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) pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close() plt.close()
plt.title(Clim_var_type + ' - SON Spring means/sums') plt.title(Clim_var_type + ' - SON Spring means/sums')

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