From b2fc7135d9ccbabb94c94fdda0f4b5bce7487981 Mon Sep 17 00:00:00 2001 From: tinoheimhuber Date: Thu, 3 May 2018 18:16:26 +1000 Subject: [PATCH] #next step for NACRLIM cc deviation plots --- ...RCliM_First_Pass_variab_deviation_plots.py | 187 +++++++++ Analysis/Code/P1_NARCliM_plots_Windows.py | 378 +++++++++--------- 2 files changed, 383 insertions(+), 182 deletions(-) create mode 100644 Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py diff --git a/Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py b/Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py new file mode 100644 index 0000000..a451f73 --- /dev/null +++ b/Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py @@ -0,0 +1,187 @@ +# -*- coding: utf-8 -*- +#####################################---------------------------------- +#Last Updated - March 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') +#plt.rcParams.update(plt.rcParamsDefault) +# +# Set working direcotry (where postprocessed NARClIM data is located) +os.chdir('C:/Users/z5025317/WRL_Postdoc/Projects/Paper#1/') +# +#####################################---------------------------------- +#set input parameters +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 +Estuary = 'Tweed' # 'Belongil' +Clim_var_type = "*" # '*' will create pdf for all variables in folder +Clim_var_type = "tasmax*" # '*' will create pdf for all variables in folder +Present_Day_Clim_Var = 'Rainfall' +#####################################---------------------------------- + +#set directory path for output files +output_directory = 'Output/Clim_Deviation_Plots/'+ 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('./Output/' + Estuary + '/' + Estuary + '_' + Clim_var_type + '*') +#read CSV file +for clim_var_csv_path in Clim_Var_CSVs: + Filename = os.path.basename(os.path.normpath(clim_var_csv_path)) + Clim_var_type = Filename.split('_', 2)[1] + print(Clim_var_type) + Ensemble_Delta_full_df = pd.read_csv(clim_var_csv_path, index_col=0, parse_dates = True) + #Ensemble_Delta_full_df = pd.to_numeric(Ensemble_Delta_full_df) + + #load present day climate data for the same variable + Present_day_Var_CSV = glob.glob('./Data/Wheather_Station_Data/**/' + Estuary + '_' + Present_Day_Clim_Var + '*csv') + Present_day_df = pd.read_csv(Present_day_Var_CSV[0]) + if Clim_var_type == 'evspsblmean': + Dates = pd.to_datetime(Present_day_df.Date) + Present_day_df.index = Dates + Present_day_df = Present_day_df.iloc[:,1] + Present_day_df = Present_day_df.replace(r'\s+', np.nan, regex=True) + Present_day_df = pd.to_numeric(Present_day_df) + + else: + Dates = pd.to_datetime(Present_day_df.Year*10000+Present_day_df.Month*100+Present_day_df.Day,format='%Y%m%d') + Present_day_df.index = Dates + Present_day_df = Present_day_df.iloc[:,5] + #create seasonal sums etc. + 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'): + Present_day_df_annual = Present_day_df.resample('A').sum() + Present_day_df_annual = Present_day_df_annual.replace(0, np.nan) + Present_day_df_monthly = Present_day_df.resample('M').sum() + Present_day_df_monthly = Present_day_df_monthly.replace(0, np.nan) + Present_day_df_weekly = Present_day_df.resample('W').sum() + Present_day_df_weekly = Present_day_df_weekly.replace(0, np.nan) + Fdf_Seas_means = Present_day_df.resample('Q-NOV').sum() #seasonal means + Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan) + else: + Present_day_df_annual = Present_day_df.resample('A').mean() + Present_day_df_monthly = Present_day_df.resample('M').mean() + Present_day_df_weekly = Present_day_df.resample('W').mean() + Fdf_Seas_means = Present_day_df.resample('Q-NOV').mean() #seasonal means + + #Loop through annual and seasons and create a deviation plot for each. + times = ['annual', 'DJF', 'MAM', 'JJA','SON'] + fig = plt.figure(figsize=(14,8)) + delta_all_df = pd.DataFrame() + i=1 + for temp in times: + #subset the ensemble dataframe for the period used: + if temp == 'annual': + Ensemble_Delta_df = Ensemble_Delta_full_df.iloc[:,range(0,2)] + # + Present_Day_ref_df = Present_day_df_annual + else: + Ensemble_Delta_df = Ensemble_Delta_full_df.filter(regex= temp) + Ensemble_Delta_df.columns = ['near', 'far'] + if temp == 'DJF': + Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==1] + Column_names = ['DJF_near', 'DJF_far'] + if temp == 'MAM': + Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==2] + Column_names = ['MAM_near', 'MAM_far'] + if temp == 'JJA': + Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==3] + Column_names = ['JJA_near', 'JJA_far'] + if temp == 'SON': + Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==4] + Present_Day_ref_df = Mean_df + print(Ensemble_Delta_df.columns.values) + #Subset to present day variability period + Present_Day_ref_df = pd.DataFrame(Present_Day_ref_df.loc[(Present_Day_ref_df.index >= Base_period_start) & (Present_Day_ref_df.index <= Base_period_end)]) + Present_Day_Mean = np.percentile(Present_Day_ref_df, 50) + Present_Day_SD = np.std(Present_Day_ref_df) + #create data frame for floating stacked barplots + index=['-2std', '-1std', 'Med', '1std', '2std'] + columns = ['present','near future', 'far future'] + Plot_in_df = pd.DataFrame(index=index, columns =columns) + # + Plot_in_df['present'] = [float(Present_Day_Mean-2*Present_Day_SD),float(Present_Day_Mean-Present_Day_SD), float(Present_Day_Mean), + float(Present_Day_Mean+Present_Day_SD), float(Present_Day_Mean+2*Present_Day_SD)] + Plot_in_df['near future'] = [float(Present_Day_Mean + Ensemble_Delta_df.near[-3:][0]),np.NaN, float(Present_Day_Mean + Ensemble_Delta_df.near[-3:][1]), + np.NaN, float(Present_Day_Mean + Ensemble_Delta_df.near[-3:][2])] + Plot_in_df['far future'] = [float(Present_Day_Mean + Ensemble_Delta_df.far[-3:][0]),np.NaN, float(Present_Day_Mean + Ensemble_Delta_df.far[-3:][1]), + np.NaN, float(Present_Day_Mean + Ensemble_Delta_df.far[-3:][2])] + #Create a second data frame that has the values in a way that they can be stacked up in bars with the correct absolute values + Plot_in_df2 = pd.DataFrame(index=index, columns =columns ) + + Plot_in_df2['present'] = [float(Present_Day_Mean-2*Present_Day_SD),float(Present_Day_SD), float(Present_Day_SD), + float(Present_Day_SD), float(Present_Day_SD)] + Plot_in_df2['near future'] = [float(Present_Day_Mean + Ensemble_Delta_df.near[-3:][0]),np.NaN, float(Ensemble_Delta_df.near[-3:][1]-Ensemble_Delta_df.near[-3:][0]), + np.NaN, float(Ensemble_Delta_df.near[-3:][2]-Ensemble_Delta_df.near[-3:][1])] + Plot_in_df2['far future'] = [float(Present_Day_Mean + Ensemble_Delta_df.far[-3:][0]),np.NaN, float(Ensemble_Delta_df.far[-3:][1]-Ensemble_Delta_df.far[-3:][0]), + np.NaN, float(Ensemble_Delta_df.far[-3:][2]-Ensemble_Delta_df.far[-3:][1])] + #transpose the data frame + Plot_in_df_tp = Plot_in_df2.transpose() + #do the individual plots + if temp == 'annual': + xmin = int(min(Plot_in_df.min(axis=1))-1) + xmax = int(max(Plot_in_df.max(axis=1))+2) + else: + xmin = int(min(Plot_in_df.min(axis=1))-1) + xmax = int(max(Plot_in_df.max(axis=1))+2) + #define colour scheme + #likert_colors = ['none', 'firebrick','firebrick','lightcoral','lightcoral'] + likert_colors = ['none', 'darkblue', 'darkblue','cornflowerblue','cornflowerblue'] + #plot the stacked barplot + ax=plt.subplot(2,3,i) + Plot_in_df_tp.plot.bar(stacked=True, color=likert_colors, edgecolor='none', 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) + plt.ylim(xmin, xmax) + plt.title(Clim_var_type + ' ' + temp ) + ax.grid(False) + for tick in ax.get_xticklabels(): + tick.set_rotation(0) + fig.tight_layout() + #reset i to i+1 for next step + if temp == 'MAM': + i=i+2 + else: + i=i+1 + print(i) + plt.show() + out_file_name = Estuary + '_' + Clim_var_type + '_CC_prio_plot.png' + out_path = output_directory + '/' + out_file_name + fig.savefig(out_path) + + + + + + + + + + + + + \ No newline at end of file diff --git a/Analysis/Code/P1_NARCliM_plots_Windows.py b/Analysis/Code/P1_NARCliM_plots_Windows.py index 7530c1e..40f8638 100644 --- a/Analysis/Code/P1_NARCliM_plots_Windows.py +++ b/Analysis/Code/P1_NARCliM_plots_Windows.py @@ -28,9 +28,10 @@ os.chdir('C:/Users/z5025317/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 = 'Bateman' # 'Belongil' -Clim_var_type = "*" #will create pdf for all variables in folder -subset_ensemble = 'no' # is yes, only the model with the lowest, median and max difference between present day and far future are selected +Estuary = 'Terrigal' # 'Belongil' +Clim_var_type = "pracc*|tasmax*" # '*' will create pdf for all variables in folder +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 = 'no' #####################################---------------------------------- #set directory path for output files @@ -46,7 +47,7 @@ Clim_Var_CSVs = glob.glob('./Data/NARCLIM_Site_CSVs/' + Estuary + '/' + Clim_var #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[3] + #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) @@ -62,10 +63,34 @@ for clim_var_csv_path in Clim_Var_CSVs: if Clim_var_type == 'tasmean' or Clim_var_type == 'tasmax': Full_df = Full_df.iloc[:,0:(Ncols_df-1)]-273.15 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'): + 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: + 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.plot(kind='bar').figure + print('-------------------------------------------') + if subset_ensemble == 'yes': #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) @@ -96,67 +121,72 @@ for clim_var_csv_path in Clim_Var_CSVs: 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 - Fdf_1900_2080 = dfall - - #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'): - 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: - 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.plot(kind='bar').figure - print('-------------------------------------------') - - #Create Deltas of average change - 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 = Fdf_1900_2080_means.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) - - np.percentile(delta_NF, 50) - delta_df - 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) - - delta_df.plot(kind='box').figure - - pd.DataFrame() - concat([Full_df, GCM_df], axis=1) + #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) - delta_df ensemble.plot(kind='bar') + #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) - dfmax = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]) - dfmed = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]) + #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) + + out_file_name = Estuary + '_' + Clim_var_type + '_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) len(Fdf_1900_2080.columns) Full_current_df = Fdf_1900_2080.iloc[:,range(0,3)] @@ -169,122 +199,106 @@ for clim_var_csv_path in Clim_Var_CSVs: 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 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 = dfa1.append(dfa2).append(dfa3) - - #write the key plots to a single pdf document - pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary_B.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) - Fdf_1900_2080_means.plot(kind='bar', ylim=(ymin,ymax)) - pdf.savefig(bbox_inches='tight', 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() - #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 - representative models') - dfall.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') - 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') - 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') - 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') - 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() - -#plots not used -#Fdf_annual_sorted_subset.plot(legend=False, subplots=True) + #output some summary plot into pdf + if plot_pdf == 'yes': + #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_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) + Fdf_1900_2080_means.plot(kind='bar', ylim=(ymin,ymax)) + pdf.savefig(bbox_inches='tight', 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() + #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 - 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') + 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') + 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') + 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') + 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() \ No newline at end of file