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#cleaned out the code and separated big functions from the analysis code

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the functions are in climdata_fcts.py
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Valentin Heimhuber 7 years ago
parent 1154e4eb1c
commit 42ab0af07a
3 changed files with 409 additions and 338 deletions
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33
Analysis/Code/NARCLIM_Download/R_Preparing_BASH_script_for_NARCLIM_batch_download.R
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@ -5,16 +5,27 @@
#evspsblmean water_evaporation flux (actual ET) long_name: Surface evaporation standard_name: water_evaporation_flux units: kg m-2 s-1
#tasmean mean near surface temperature
#pracc precipitation daily precipitation sum (sum of convective prcacc and stratiform prncacc precip)
# 'evspsblmean' water_evaporation flux (actual ET) long_name: Surface evaporation standard_name: water_evaporation_flux units: kg m-2 s-1
# 'potevpmean' potential ET water_potential_evaporation_flux kg m-2 s-1
# 'tasmean' mean near surface temperature
# 'tasmax' maximum near surface temperature
# 'pracc' precipitation daily precipitation sum (sum of convective prcacc and stratiform prncacc precip)
# 'pr1Hmaxtstep' maximum 1 hour interval rainfall in a one day period
# 'pr1Hmaxtstep' Max. 1-hour time-window moving averaged precipitation rate units: kg m-2 s-1 maximum 1-hour time-window moving averaged values from point values 60.0 second
# 'wss1Hmaxtstep' Max. 1-hour time-window moving averaged surface wind speed units: m s-1 maximum 1-hour time-window moving averaged values from point values 60.0 second
# 'wssmax' Surface wind speed standard_name: air_velocity units: m s-1 height: 10 m
# 'wssmean' Surface wind speed standard_name: air_velocity units: m s-1
Clim_Var <- 'evspsblmean'
Clim_Var <- 'wssmax'
Datatype <- 'T_GCMS' #T_GCMS for GCM forcing, T_NNRP for reanalysis (only 1950-2009)
Biasboolean <- 'False' #use bias corrected data?
Directory <- 'C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Data/NARCLIM_Site_CSVs'
Directory <- 'C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Data/NARCLIM_Site_CSVs/'
Filename <- 'NARCLIM_Point_Sites.csv'
#Load CSV with location names and lat lon coordinates
Location.df <- data.frame(read.csv(paste(Directory, Filename, sep=""), header=T))
Location.df <- data.frame(read.csv(paste(Directory, Filename, sep=""), header=T, fileEncoding="UTF-8-BOM"))
#create empty vector for storing the command line text and open file
Vector.for.command.line.txt <- c()
@ -23,13 +34,13 @@ text1 <- c(paste("Datatype='",Datatype,"'", sep=""),
paste("Bias_corrected='",Biasboolean,"'", sep=""), paste("ClimVarName='",Clim_Var,"'", sep=""))
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, text1)
for (i in 1:(length(Location.df$Name))){
#name<-as.character(Location.df$Name[i])
name<-as.character(Location.df$Name[i])
#name<-gsub('([[:punct:]])|\\s+','_',name)
if(i<10){
name<-paste('Catchment_0', as.character(i), sep="")
}else{
name<-paste('Catchment_', as.character(i), sep="")
}
# if(i<10){
# name<-paste('Catchment_0', as.character(i), sep="")
# }else{
# name<-paste('Catchment_', as.character(i), sep="")
# }
latitude=round(as.numeric(Location.df$Lat[i]),3)
longitude=round(as.numeric(Location.df$Long[i]),3)
text <- c(paste("latitude=",latitude,"", sep=""), paste("longitude=",longitude,"", sep=""),
@ -39,9 +50,9 @@ P1_NARCliM_NC_to_CSV_CCRC_SS.py \\
--lat $latitude --lon $longitude --varName $ClimVarName --domain 'd02' --timestep \\
'DAY' --LocationName $name --Datatype $Datatype --BiasBool $Bias_corrected")
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, text)
if(i==10|i==20|i==31){
if(i==10|i==20|i==31|i==length(Location.df$Name)){
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, " ")
text.file.name <- paste('C:/Users/z5025317/OneDrive - UNSW/Hunter_CC_Modeling/07_Modelling/01_Input/BC_Generation/Code/NARCLIM_Download_and_Processing/',Clim_Var, "_", Datatype, "_", Biasboolean,substring(as.character(i), 1,1), ".txt", sep="")
text.file.name <- paste(Directory ,'/',Clim_Var, "_", Datatype, "_", Biasboolean,substring(as.character(i), 1,1), "_", ".txt", sep="")
#open and fill text file
fileConn <- file(text.file.name)
writeLines(Vector.for.command.line.txt, fileConn)

643
Analysis/Code/P1_NARCliM_plots_Windows.py
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@ -1,12 +1,13 @@
# -*- coding: utf-8 -*-
#####################################----------------------------------
#Last Updated - March 2018
#==========================================================#
#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
@ -18,346 +19,334 @@ 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
#==========================================================#
# 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
Base_period_start = '1990-01-01'
Base_period_end = '2080-01-01' #use last day that's not included in period as < is used for subsetting
Estuary = 'Nadgee' # 'Belongil'
Clim_var_type = "pracc*" # '*' will create pdf for all variables in folder "pracc*|tasmax*"
Estuary = 'HUNTER' # 'Belongil'
Clim_var_type = "pracc" # '*' will create pdf for all variables in folder "pracc*|tasmax*"
plot_pdf = 'yes'
delta_csv = 'yes'
Stats = 'dailymax'
#####################################----------------------------------
#
Version = 'V4'
#==========================================================#
#==========================================================#
#set directory path for output files
output_directory = 'Output/'+ Estuary
output_directory = 'Output/Case_Study_1/'+ Estuary
#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
if not os.path.exists(output_directory):
os.makedirs(output_directory)
print('-------------------------------------------')
print("output directory folder didn't exist and was generated")
print('-------------------------------------------')
print('-------------------')
Clim_Var_CSVs = glob.glob('./Data/NARCLIM_Site_CSVs/' + Estuary + '/' + Clim_var_type)
#Clim_Var_CSV = glob.glob('./Site_CSVs/' + Clim_var_type + '*' )
#read CSV file
for clim_var_csv_path in Clim_Var_CSVs:
#clim_var_csv_path = Clim_Var_CSVs[0]
Filename = os.path.basename(os.path.normpath(clim_var_csv_path))
Clim_var_type = Filename.split('_', 1)[0]
print(clim_var_csv_path)
Full_df = pd.read_csv(clim_var_csv_path, index_col=0, parse_dates = True)
#pandas datestamp index to period (we don't need the 12 pm info in the index (daily periods are enough))
Full_df.index = Full_df.index.to_period('D')
Full_df = Full_df.drop(columns=['period'])
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 == 'tasmean' or Clim_var_type == 'tasmax':
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
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)
#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
#Aggregate daily df to annual time series
if (Clim_var_type == 'pracc' or Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean'
or Clim_var_type == 'pr1Hmaxtstep' or Clim_var_type == 'wss1Hmaxtstep'):
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_1900_2080_monthly = Fdf_1900_2080.resample('M').max()
Fdf_1900_2080_monthly = Fdf_1900_2080_monthly.replace(0, np.nan)
Fdf_1900_2080_weekly = Fdf_1900_2080.resample('W').max()
Fdf_1900_2080_weekly = Fdf_1900_2080_weekly.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)
else:
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').sum()
Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
Fdf_1900_2080_monthly = Fdf_1900_2080.resample('M').sum()
Fdf_1900_2080_monthly = Fdf_1900_2080_monthly.replace(0, np.nan)
Fdf_1900_2080_weekly = Fdf_1900_2080.resample('W').sum()
Fdf_1900_2080_weekly = Fdf_1900_2080_weekly.replace(0, np.nan)
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').sum() #seasonal means
Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
else:
#==========================================================#
#==========================================================#
Estuary_Folder = glob.glob('./Data/NARCLIM_Site_CSVs/' + Estuary + '*' )
Clim_Var_CSVs = glob.glob(Estuary_Folder[0] + '/' + Clim_var_type + '*')
#==========================================================#
#==========================================================#
#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'])
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 == 'tasmean' or Clim_var_type == 'tasmax':
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
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)
#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
#==========================================================#
#Aggregate daily df to annual time series
if (Clim_var_type == 'pracc' or Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean'
or Clim_var_type == 'pr1Hmaxtstep' or Clim_var_type == 'wss1Hmaxtstep'):
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)
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)
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)
else:
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').mean()
Fdf_1900_2080_monthly = Fdf_1900_2080.resample('M').mean()
Fdf_1900_2080_weekly = Fdf_1900_2080.resample('W').mean()
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').mean() #seasonal means
#plot the mean of all model runs
print('-------------------------------------------')
print('mean of all models for climate variable: ' + Clim_var_type)
Fdf_1900_2080_means = Fdf_1900_2080.mean()
#Fdf_1900_2080_means.columns = ['Mean']
#Fdf_1900_2080_means.plot(kind='bar').figure
print('-------------------------------------------')
#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_means = pd.DataFrame(Fdf_1900_2080_sorted.mean())
df = Fdf_1900_2080_sorted_means
#add a simple increasing integer index
df = df.reset_index()
df= df[df.index % 3 != 1]
df['C'] = df[0].diff()
df = df.reset_index()
df= df[df.index % 2 != 0]
#get max difference model (difference between far future and prsent day)
a = df[df.index == df['C'].argmax(skipna=True)]
Max_dif_mod_name = a.iloc[0]['index']
#get min difference model
a = df[df.index == df['C'].argmin(skipna=True)]
Min_dif_mod_name = a.iloc[0]['index']
#get the model which difference is closest to the median difference
df['D'] = abs(df['C']- df['C'].median())
a = df[df.index == df['D'].argmin(skipna=True)]
Med_dif_mod_name = a.iloc[0]['index']
#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] )
dfall = df2.reindex_axis(sorted(df2.columns), axis=1)
#data frame with individual models
dfmin = Fdf_1900_2080.filter(regex= Min_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])
# use only the 3 representative models for the analysis
Fdf_1900_2080_all_mods = Fdf_1900_2080
#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 > '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'
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
times = ['annual', 'DJF', 'MAM', 'JJA','SON']
delta_all_df = pd.DataFrame()
for temp in times:
if temp == 'annual':
Mean_df = Fdf_1900_2080_annual.mean()
Column_names = ['near', 'far']
if temp == 'DJF':
Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean()
Column_names = ['DJF_near', 'DJF_far']
if temp == 'MAM':
Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean()
Column_names = ['MAM_near', 'MAM_far']
if temp == 'JJA':
Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean()
Column_names = ['JJA_near', 'JJA_far']
if temp == 'SON':
Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean()
Column_names = ['SON_near', 'SON_far']
models = list(Fdf_1900_2080_means.index)
newmodel = []
type(newmodel)
for each in models:
newmodel.append(each[:-5])
unique_models = set(newmodel)
# calculate diff for each unique model
delta_NF_ensemble = []
delta_FF_ensemble = []
for unique_model in unique_models:
dfdiff = Mean_df.filter(regex= unique_model)
type(dfdiff)
delta_NF = dfdiff[1] - dfdiff[0]
delta_NF_ensemble.append(delta_NF)
delta_FF = dfdiff[2] - dfdiff[1]
delta_FF_ensemble.append(delta_FF)
delta_df1=pd.DataFrame(delta_NF_ensemble, index=unique_models)
delta_df2=pd.DataFrame(delta_FF_ensemble, index=unique_models)
delta_df= pd.concat([delta_df1, delta_df2], axis=1)
#rename columns
delta_df.columns = Column_names
#add a row with medians and 10 and 90th percentiles
delta_df.loc['10th'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 10), Column_names[1]:np.percentile(delta_df[Column_names[1]], 10)})
delta_df.loc['median'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 50), Column_names[1]:np.percentile(delta_df[Column_names[1]], 50)})
delta_df.loc['90th'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 90), Column_names[1]:np.percentile(delta_df[Column_names[1]], 90)})
#append df to overall df
delta_all_df = pd.concat([delta_all_df, delta_df], axis=1)
Fdf_1900_2080_means = Fdf_1900_2080.mean()
#==========================================================#
#==========================================================#
#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)
#==========================================================#
if delta_csv == 'yes':
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_ensemble_changes.csv'
out_path = output_directory + '/' + out_file_name
delta_all_df.to_csv(out_path)
#==========================================================#
#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 a dataframe that has a single column for present day, near and far future for the (3 selected models)
len(Fdf_1900_2080.columns)
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']
#==========================================================#
#Create Deltas of average change for annual and seasonal basis
#==========================================================#
times = ['annual', 'DJF', 'MAM', 'JJA','SON']
delta_all_df = pd.DataFrame()
for temp in times:
if temp == 'annual':
Mean_df = Fdf_1900_2080_annual.mean()
Column_names = ['near', 'far']
if temp == 'DJF':
Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==1].mean()
Column_names = ['DJF_near', 'DJF_far']
if temp == 'MAM':
Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==2].mean()
Column_names = ['MAM_near', 'MAM_far']
if temp == 'JJA':
Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==3].mean()
Column_names = ['JJA_near', 'JJA_far']
if temp == 'SON':
Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==4].mean()
Column_names = ['SON_near', 'SON_far']
models = list(Fdf_1900_2080_means.index)
newmodel = []
type(newmodel)
for each in models:
newmodel.append(each[:-5])
unique_models = set(newmodel)
# calculate diff for each unique model
delta_NF_ensemble = []
delta_FF_ensemble = []
for unique_model in unique_models:
dfdiff = Mean_df.filter(regex= unique_model)
type(dfdiff)
delta_NF = dfdiff[1] - dfdiff[0]
delta_NF_ensemble.append(delta_NF)
delta_FF = dfdiff[2] - dfdiff[1]
delta_FF_ensemble.append(delta_FF)
delta_df1=pd.DataFrame(delta_NF_ensemble, index=unique_models)
delta_df2=pd.DataFrame(delta_FF_ensemble, index=unique_models)
delta_df= pd.concat([delta_df1, delta_df2], axis=1)
#output some summary plot into pdf
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
pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary_10.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()
#
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
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)
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()
#monthly box
plt.title(Clim_var_type + ' - Monthly means/sums')
Fdf_1900_2080_monthly.boxplot(rot=90)
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close()
#annual box
plt.title(Clim_var_type + ' - Monthly means/sums for min diff model')
Fdf_1900_2080_monthly.filter(regex= Min_dif_mod_name[:-5]).boxplot(rot=90)
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close()
#annual box
plt.title(Clim_var_type + ' - Monthly means/sums for median diff model')
Fdf_1900_2080_monthly.filter(regex= Med_dif_mod_name[:-5]).boxplot(rot=90)
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close()
#annual box
plt.title(Clim_var_type + ' - Monthly means/sums for max diff model')
Fdf_1900_2080_monthly.filter(regex= Max_dif_mod_name[:-5]).boxplot(rot=90)
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close()
#weekly box
plt.title(Clim_var_type + ' - Weekly means/sums')
Fdf_1900_2080_weekly.boxplot(rot=90)
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
plt.close()
#daily box
plt.title(Clim_var_type + ' - Daily means/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]
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')
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()
#rename columns
delta_df.columns = Column_names
#add a row with medians and 10 and 90th percentiles
delta_df.loc['10th'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 10), Column_names[1]:np.percentile(delta_df[Column_names[1]], 10)})
delta_df.loc['median'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 50), Column_names[1]:np.percentile(delta_df[Column_names[1]], 50)})
delta_df.loc['90th'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 90), Column_names[1]:np.percentile(delta_df[Column_names[1]], 90)})
#append df to overall df
delta_all_df = pd.concat([delta_all_df, delta_df], axis=1)
#==========================================================#
if delta_csv == 'yes':
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_ensemble_changes.csv'
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)
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']
#==========================================================#
#==========================================================#
#output some summary plot into pdf
#==========================================================#
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
pdf_out_file_name = Clim_var_type + '_' + Stats + '_start_' + Base_period_start + '_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)
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']
neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
ax.xaxis.grid(False)
#fig.patch.set_alpha(0)
#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)
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]
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')
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()

71
Analysis/Code/climdata_fcts.py
Unescape Escape View File

@ -0,0 +1,71 @@
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 14 16:32:01 2018
@author: z5025317
"""
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
def compare_images(im1, im2):
"""plots 2 images next to each other, sharing the axis"""
plt.figure()
ax1 = plt.subplot(121)
plt.imshow(im1, cmap='gray')
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
plt.imshow(im2, cmap='gray')
plt.show()
def reject_outliers(data, m=2):
"rejects outliers in a numpy array"
return data[abs(data - np.mean(data)) < m * np.std(data)]
def duplicates_dict(lst):
"return duplicates and indices"
# nested function
def duplicates(lst, item):
return [i for i, x in enumerate(lst) if x == item]
return dict((x, duplicates(lst, x)) for x in set(lst) if lst.count(x) > 1)
def datenum2datetime(datenum):
"convert datenum to datetime"
#takes in datenum and outputs python datetime
time = [datetime.fromordinal(int(dn)) + timedelta(days=float(dn)%1) - timedelta(days = 366) for dn in datenum]
return time
def select_min_med_max_dif_model(NARCLIM_df):
#Select the 3 most representative models (min med and max difference betwen far future and present)
Fdf_1900_2080_sorted = NARCLIM_df.reindex_axis(sorted(NARCLIM_df.columns), axis=1)
Fdf_1900_2080_sorted_means = pd.DataFrame(Fdf_1900_2080_sorted.mean())
df = Fdf_1900_2080_sorted_means
#add a simple increasing integer index
df = df.reset_index()
df= df[df.index % 3 != 1]
df['C'] = df[0].diff()
df = df.reset_index()
df= df[df.index % 2 != 0]
#get max difference model (difference between far future and prsent day)
a = df[df.index == df['C'].argmax(skipna=True)]
Max_dif_mod_name = a.iloc[0]['index']
#get min difference model
a = df[df.index == df['C'].argmin(skipna=True)]
Min_dif_mod_name = a.iloc[0]['index']
#get the model which difference is closest to the median difference
df['D'] = abs(df['C']- df['C'].median())
a = df[df.index == df['D'].argmin(skipna=True)]
Med_dif_mod_name = a.iloc[0]['index']
#data frame with min med and max difference model
df2 = NARCLIM_df.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)
#data frame with individual models
dfmin = NARCLIM_df.filter(regex= Min_dif_mod_name[:-5])
dfmax = NARCLIM_df.filter(regex= Max_dif_mod_name[:-5])
dfmed = NARCLIM_df.filter(regex= Max_dif_mod_name[:-5])
return dfall , dfmin, dfmed, dfmax, Min_dif_mod_name,Med_dif_mod_name, Max_dif_mod_name
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