#started with more sophisticated NARCLIM plots

Analysis/Code/P1_NARCliM_plots_Windows.py

@@ -19,25 +19,30 @@ from matplotlib.backends.backend_pdf import PdfPages
 matplotlib.style.use('ggplot')
 #
 # Set working direcotry (where postprocessed NARClIM data is located)
-os.chdir('C:/Users/z5025317/WRL_Postdoc/Projects/Paper#1/NARCLIM/')
+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  = 'tasmean' will create pdf for all variables in folder           
 #####################################----------------------------------
 #set directory path for output files
-output_directory = 'Output'
+output_directory = 'Output/'+ 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")
-Clim_Var_CSVs = glob.glob('./Site_CSVs/*')
+    print('-------------------------------------------')
+    print('-------------------')
+Clim_Var_CSVs = glob.glob('./Data/NARCLIM_Site_CSVs/' + Estuary + '/*')
 #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)
@@ -48,8 +53,8 @@ for clim_var_csv_path in Clim_Var_CSVs:
     #Full_df.dtypes
     
     #substract a constant from all values (temp)
-    if Clim_var_type == 'tasmean':
+    if Clim_var_type == 'tasmean' or Clim_var_type == 'tasmax':
-        Full_df = Full_df.iloc[:,0:26]-273.15
+        Full_df = Full_df.iloc[:,0:(Ncols_df-1)]-273.15
     
     #index the narclim periods if needed
     #Full_df.loc[(Full_df.index >= '1990-01-01') & (Full_df.index < '2010-01-01'), 'period']= '1990-2009'
@@ -57,7 +62,9 @@ for clim_var_csv_path in Clim_Var_CSVs:
     #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)]
+    #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)
     #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'):
@@ -78,14 +85,14 @@ for clim_var_csv_path in Clim_Var_CSVs:
     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', ylim=(16,22)).figure
+    Fdf_1900_2080_means.plot(kind='bar').figure
     print('-------------------------------------------')
     
     #time series plots:
     #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]]
+    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'
@@ -96,35 +103,71 @@ for clim_var_csv_path in Clim_Var_CSVs:
     #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
-    Full_current_df = Fdf_1900_2080.iloc[:,[0,1,2]]
+    len(Fdf_1900_2080.columns)
+    Full_current_df = Fdf_1900_2080.iloc[:,range(0,12)]
     Full_current_df = Full_current_df.stack()
     #nearfuture
-    Full_nearfuture_df = Fdf_1900_2080.iloc[:,range(3,15)]
+    Full_nearfuture_df = Fdf_1900_2080.iloc[:,range(12,24)]
     Full_nearfuture_df = Full_nearfuture_df.stack()
     #farfuture
-    Full_farfuture_df = Fdf_1900_2080.iloc[:,range(15,27)]
+    Full_farfuture_df = Fdf_1900_2080.iloc[:,range(24,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
+    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()
+    df= df[df.index % 3 != 1]
+    df['C'] = df[0].diff()
+    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'])
+    
+    
+    Fdf_1900_2080_sorted_means.plot(kind='bar').figure
+    MIROC_R2_df = Fdf_1900_2080.iloc[:,[1,13,28]]
     #end of developement
+    
     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))
-        Fdf_1900_2080_means.plot(kind='bar')
         pdf.savefig(bbox_inches='tight', pad_inches=0.4)
         plt.close()
         #full period density comparison
-        plt.title(Clim_var_type + ' -  density comparison - full period')
+        plt.title(Clim_var_type + ' -  density comparison - full period - one model')
-        Summarized_df.plot.kde()
+        MIROC_R2_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')
+        Fdf_1900_2080_annual.iloc[:,[1,13,28]].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')
+        Fdf_1900_2080_annual.iloc[:,[3,18,30]].boxplot(rot=90)
         pdf.savefig(bbox_inches='tight', pad_inches=0.4)
         plt.close()
         #annual box
-        plt.title(Clim_var_type + ' -  Annual means')
+        plt.title(Clim_var_type + ' -  Annual means for one model C')
-        Fdf_1900_2080_annual.boxplot(rot=90)
+        Fdf_1900_2080_annual.iloc[:,[8,17,26]].boxplot(rot=90)
         pdf.savefig(bbox_inches='tight', pad_inches=0.4)
         plt.close()
         #monthly box