#incorporated the option for a stats type such as daily max per year or season

#also fixed a bug with the server script

Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py

@@ -30,10 +30,11 @@ 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 = 'Nadgee' # 'Belongil'
 Clim_var_type  = "*"   #  '*' will create pdf for all variables in folder  
-Clim_var_type  = "tasmean*"   #  '*' will create pdf for all variables in folder  
-Present_Day_Clim_Var = 'Wind'    #MaxT, MinT, Rainfall, ET
+Clim_var_type  = "pracc*"   #  '*' will create pdf for all variables in folder  
+Present_Day_Clim_Var = 'Rainfall'    #MaxT, MinT, Rainfall, ET Wind
 present_day_plot = 'yes'
-Version = "V1"
+Version = "V2"
+Stats = 'dailymax' #maximum takes the annual max Precipitation instead of the sum
 #####################################----------------------------------
 
 #set directory path for output files
@@ -45,7 +46,7 @@ if not os.path.exists(output_directory):
     print("output directory folder didn't exist and was generated")
     print('-------------------------------------------')
     print('-------------------')
-Clim_Var_CSVs = glob.glob('./Output/' + Estuary + '/' + Estuary + '_' + Clim_var_type + '*')
+Clim_Var_CSVs = glob.glob('./Output/' + Estuary + '/' + Estuary + '_' + Clim_var_type[:-1] + '_' + Stats + '*')
 #read CSV file
 clim_var_csv_path = Clim_Var_CSVs[0]
 Filename = os.path.basename(os.path.normpath(clim_var_csv_path))
@@ -99,14 +100,14 @@ else:
 #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)
+    if Stats == 'dailymax':
+        Present_day_df_annual = Present_day_df.resample('A').max()
+        Present_day_df_annual = Present_day_df_annual.replace(0, np.nan)
+    else:
+        Present_day_df_annual = Present_day_df.resample('A').sum()
+        Present_day_df_annual = Present_day_df_annual.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()
@@ -205,7 +206,7 @@ for temp in times:
     print(i)
 
 plt.show()    
-out_file_name = Estuary  + '_' + Clim_var_type + '_CC_prio_plot' + Version + '.png'
+out_file_name = Estuary  + '_' + Clim_var_type + '_' + Stats + '_'  + '_CC_prio_plot' + Version + '.png'
 out_path = output_directory + '/' + out_file_name
 fig.savefig(out_path)
     

Analysis/Code/P1_NARCliM_NC_to_CSV_CCRC_SS.py

@@ -77,7 +77,7 @@ for NC_Period in NC_Periods:
                 print f.variables
                 print
                 for varname in f.variables:
-                	print varname,' -> ',shape(f.variables[varname])
+                	print varname,' -> ',np.shape(f.variables[varname])
                 print '---------------------------------------------------------'
                 # Based on the desired inputs, this finds the nearest grid centerpoint index (x,y) in the *.nc file
                 dist_x=np.abs(f.variables['lon'][:,:]-float(mylon))

Analysis/Code/P1_NARCliM_plots_Windows.py

@@ -27,9 +27,10 @@ os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdo
 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  = "tasmean*"   #  '*' will create pdf for all variables in folder  "pracc*|tasmax*"        
+Clim_var_type  = "pracc*"   #  '*' will create pdf for all variables in folder  "pracc*|tasmax*"        
 plot_pdf = 'yes'
-delta_csv = 'no'
+delta_csv = 'yes'
+Stats = 'dailymax'
 #####################################----------------------------------
 #
 #set directory path for output files
@@ -69,14 +70,24 @@ for clim_var_csv_path in Clim_Var_CSVs:
     #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)
+        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:    
         Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').mean()
         Fdf_1900_2080_monthly = Fdf_1900_2080.resample('M').mean()
@@ -183,7 +194,7 @@ for clim_var_csv_path in Clim_Var_CSVs:
         delta_all_df = pd.concat([delta_all_df, delta_df], axis=1)
 
     if delta_csv == 'yes':
-        out_file_name = Estuary  + '_' + Clim_var_type + '_NARCliM_ensemble_changes.csv'
+        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)
 
@@ -214,7 +225,7 @@ for clim_var_csv_path in Clim_Var_CSVs:
         
         #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_9.pdf'
+        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: