#refined the NARCLIM plotting

Analysis/Code/P1_NARCliM_plots_Windows.py

@@ -16,7 +16,9 @@ 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')
+
 #
 # Set working direcotry (where postprocessed NARClIM data is located)
 os.chdir('C:/Users/z5025317/WRL_Postdoc/Projects/Paper#1/')
@@ -27,7 +29,7 @@ os.chdir('C:/Users/z5025317/WRL_Postdoc/Projects/Paper#1/')
 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  = 'tasmean' will create pdf for all variables in folder           
+Clim_var_type  = "*"   #will create pdf for all variables in folder           
 #####################################----------------------------------
 #set directory path for output files
 output_directory = 'Output/'+ Estuary
@@ -38,33 +40,62 @@ if not os.path.exists(output_directory):
     print("output directory folder didn't exist and was generated")
     print('-------------------------------------------')
     print('-------------------')
-Clim_Var_CSVs = glob.glob('./Data/NARCLIM_Site_CSVs/' + Estuary + '/*')
+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]
+    #clim_var_csv_path = Clim_Var_CSVs[3]
     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 (temp)
+    #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
-    
+    Fdf_1900_2080 = Full_df
-    #index the narclim periods if needed
-    #Full_df.loc[(Full_df.index >= '1990-01-01') & (Full_df.index < '2010-01-01'), 'period']= '1990-2009'
-    #Full_df.loc[(Full_df.index >= '2020-01-01') & (Full_df.index < '2040-01-01'), 'period']= '2020-2039'
-    #Full_df.loc[(Full_df.index >= '2060-01-01') & (Full_df.index < '2080-01-01'), 'period']= '2060-2079'
     
     #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.drop(columns=['period'])
+
-    Ncols_df = len(Fdf_1900_2080)
+    #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
+    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'):
@@ -81,6 +112,7 @@ for clim_var_csv_path in Clim_Var_CSVs:
         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)
@@ -88,60 +120,39 @@ for clim_var_csv_path in Clim_Var_CSVs:
     Fdf_1900_2080_means.plot(kind='bar').figure
     print('-------------------------------------------')
     
-    #time series plots:
+    #create a dataframe that has a single column for present day, near and far future for the (3 selected models)
-    #ranks = Fdf_1900_2080_means[3:].rank(axis=0)
-    df3 = Fdf_1900_2080_annual
-    Fdf_annual_sorted = df3.reindex_axis(df3.mean().sort_values().index, axis=1)
-    Fdf_annual_sorted_subset = Fdf_annual_sorted.iloc[:,[0,1,2,3,5,7,13,15,18, 22, 24,25,30,33]]
-    
-    #write the key plots to a single pdf document
-    pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary4.pdf'
-    pdf_out_path = output_directory +'/' + pdf_out_file_name
-    #open pdf and add the plots
-    
-    #developement
-    #I want to create a simple density plot for all values in present day, near future and far future
-    #this should be an easy indicator of change
-    #subset present day simulations
     len(Fdf_1900_2080.columns)
-    Full_current_df = Fdf_1900_2080.iloc[:,range(0,12)]
+    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(12,24)]
+    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(24,len(Fdf_1900_2080.columns))]
+    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 = ['presentday', 'nearfuture', 'farfuture']
-    plt.title(Clim_var_type + ' -  density comparison - full period')
-    Summarized_df.plot.kde()
-    Summarized_df.boxplot(rot=90)
-        
     
-    #getting to more refined and meaningful plots
+    #create a dataframe that has 1 column for each of the three representative models
-    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
     
-    df = df.reset_index()
+    Full_df.loc[(Full_df.index > '1990-01-01') & (Full_df.index < '2009-01-01'), 'period']= '1990-2009'
-    df= df[df.index % 3 != 1]
+    Full_df.loc[(Full_df.index > '2020-01-01') & (Full_df.index < '2039-01-01'), 'period']= '2020-2039'
-    df['C'] = df[0].diff()
+    Full_df.loc[(Full_df.index > '2060-01-01') & (Full_df.index < '2079-01-01'), 'period']= '2060-2079'
-    a= df[df.index == df['C'].argmax(skipna=True)]
-    a['index']
-    df.iloc[[df['C'].argmax(skipna=True)]]
-    df['C'].argmax(skipna=True)
-    df['C'].argmin(skipna=True)
-    df['C'].argmean(skipna=True)
-    df[df['C']==df['C'].median()]
-    max(df['C'])
 
+    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)
     
-    Fdf_1900_2080_sorted_means.plot(kind='bar').figure
+    #write the key plots to a single pdf document
-    MIROC_R2_df = Fdf_1900_2080.iloc[:,[1,13,28]]
+    pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary2.pdf'
-    #end of developement
+    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')
@@ -151,88 +162,89 @@ for clim_var_csv_path in Clim_Var_CSVs:
         pdf.savefig(bbox_inches='tight', pad_inches=0.4)
         plt.close()
         #full period density comparison
-        plt.title(Clim_var_type + ' -  density comparison - full period - one model')
+        plt.title(Clim_var_type + ' -  density comparison - full period - all models')
-        MIROC_R2_df.plot.kde()
+        Summarized_df.plot.kde()
         pdf.savefig(bbox_inches='tight', pad_inches=0.4)
         plt.close()
         #annual box
-        plt.title(Clim_var_type + ' -  Annual means for one model')
+        plt.title(Clim_var_type + ' -  Annual means/sums for max diff model')
-        Fdf_1900_2080_annual.iloc[:,[1,13,28]].boxplot(rot=90)
+        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 + ' -  Annual means for one model B')
+        plt.title(Clim_var_type + ' -  Monthly means/sums for min diff model')
-        Fdf_1900_2080_annual.iloc[:,[3,18,30]].boxplot(rot=90)
+        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 + ' -  Annual means for one model C')
+        plt.title(Clim_var_type + ' -  Monthly means/sums for median diff model')
-        Fdf_1900_2080_annual.iloc[:,[8,17,26]].boxplot(rot=90)
+        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()
-        #monthly box
+        #annual box
-        plt.title(Clim_var_type + ' -  Monthly means')
+        plt.title(Clim_var_type + ' -  Monthly means/sums for max diff model')
-        Fdf_1900_2080_monthly.boxplot(rot=90)
+        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')
+        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')
+        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')
-        Fdf_1900_2080_annual.plot(legend=False)
-        pdf.savefig(bbox_inches='tight', pad_inches=0.4)
-        plt.close()
-        #
         plt.title(Clim_var_type + ' -  Time series - representative models')
-        Fdf_annual_sorted_subset.plot(legend=False)
+        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')
+        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')
+        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')
+        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')
+        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')
+        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')
+        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')
+        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')
+        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()