#added a couple of plotting abilities to the existing code and all is working.

the only problem remaining is that currently, the present day plot for extreme indices isn't working properly
it's showing the annual sums or averages

Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py

@@ -48,21 +48,25 @@ for Est in Estuaries:
     Base_period_start = '1970-01-01'        #Start of interval for base period of climate variability
     Base_period_DELTA_start = '1990-01-01'   #Start of interval used for calculating mean and SD for base period (Delta base period)
     Base_period_end = '2009-01-01'          #use last day that's not included in period as < is used for subsetting
-    Clim_var_type  = 'tasmean'            #Name of climate variable in NARCLIM models  '*' will create pdf for all variables in folder
+    Clim_var_type  = 'pracc'            #Name of climate variable in NARCLIM models  '*' will create pdf for all variables in folder
-      
+    Stats = 'maxdaily'                  #'maxdaily' #maximum takes the annual max Precipitation instead of the sum
+    
     PD_Datasource = 'SILO'                  #Source for present day climate data (historic time series) can be either: 'Station' or 'SILO'
-    SILO_Clim_var = ['max_temp']         #select the SILO clim variable to be used for base period. - see silo.py for detailed descriptions
+    SILO_Clim_var = ['daily_rain']         #select the SILO clim variable to be used for base period. - see silo.py for detailed descriptions
     Location = 'Estuary'                 # pick locaiton for extracting the SILO data can be: Estuary, Catchment, or Ocean
     
     presentdaybar = False               #include a bar for present day variability in the plots?
-    present_day_plot = 'no'             #save a time series of present day        
+    present_day_plot = 'yes'             #save a time series of present day  
-    Version = "V1"
+    Allin1_delta_plot = 'no'      
-    Stats = 'mean'                  #'maxdaily' #maximum takes the annual max Precipitation instead of the sum
+    Version = "V4"
     ALPHA_figs = 0                     #Set alpha of figure background (0 makes everything around the plot panel transparent)
+    font = {'family' : 'sans-serif',        
+        'weight' : 'normal',
+        'size'   : 14}
+    matplotlib.rc('font', **font)       #size of axis labels
     #==========================================================#
     
     
-    
     #==========================================================#
     #set directory path for output files
     output_directory = 'Output/' + Case_Study_Name + '/' + Estuary + '/' + '/Clim_Deviation_Plots/'
@@ -190,6 +194,8 @@ for Est in Estuaries:
     times = ['annual', 'DJF', 'MAM', 'JJA','SON']
     fig = plt.figure(figsize=(14,8))
     delta_all_df = pd.DataFrame()
+    Combined_Delta_df = pd.DataFrame()
+    Combined_Med_df = pd.DataFrame()
     i=1
     for temp in times:
         #temp = 'annual'
@@ -197,20 +203,22 @@ for Est in Estuaries:
         if temp == 'annual':
             Ensemble_Delta_df = Ensemble_Delta_full_df.iloc[:,range(0,2)]
             Present_Day_ref_df = Present_day_df_annual
+            Column_names = ['near', 'far']
         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']
+                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']
+                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']
+                Column_names = ['JJA_near', 'JJA_far']
             if temp == 'SON':
                 Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==4]
+                Column_names = ['SON_near', 'SON_far']
             Present_Day_ref_df = Mean_df
         #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)])
@@ -290,7 +298,7 @@ for Est in Estuaries:
             tick.set_rotation(0)
         fig.tight_layout()
         fig.patch.set_alpha(ALPHA_figs)
-        plt.set_facecolor('g')
+        #plt.set_facecolor('g')
                  
         if temp == 'annual':
             ax=plt.subplot(2,2,1) 
@@ -315,15 +323,47 @@ for Est in Estuaries:
             i=i+4
         else:
             i=i+1
-       
+        #create a data frame that contains all of the delta bars
+        Plot_in_df_tp.index = Column_names
+        Plot_in_df_tp['-2std'] = Plot_in_df_tp['-2std'] - Present_Day_Mean
+        Combined_Delta_df = pd.concat([Combined_Delta_df, Plot_in_df_tp], axis=0)
+        
     #plt.show()   
     if presentdaybar == False:
-        out_file_name = Estuary  + '_' + Clim_var_type + '_' + Stats + '_'  + PD_Datasource +  '_'  + SILO_Clim_var[0] +  Version +  '_'  + '_NPDB.png'
+        out_file_name = Estuary  + '_' + Clim_var_type + '_' + Stats + '_'  + PD_Datasource +  '_'  + SILO_Clim_var[0] +  '_' +  Version +  '_'  + '_NPDB.png'
     else:
-        out_file_name = Estuary  + '_' + Clim_var_type + '_' + Stats + '_'  + PD_Datasource +  '_'  + SILO_Clim_var[0] +  Version +  '_'  + '2.png'
+        out_file_name = Estuary  + '_' + Clim_var_type + '_' + Stats + '_'  + PD_Datasource +  '_'  + SILO_Clim_var[0] +  '_' +  Version +  '_'  + '2.png'
     out_path = output_directory + '/' + out_file_name
     fig.savefig(out_path)
         
+    
+    if Allin1_delta_plot == 'yes': 
+        fig = plt.figure(figsize=(14,8))
+        ax = fig.add_subplot(2, 2, 1)
+        Plot_in_df_tp = pd.DataFrame(Combined_Delta_df)
+        Bottom = int(Plot_in_df_tp.stack().min(axis=None,skipna=True)) - 1
+        Plot_in_df_tp = Plot_in_df_tp - Bottom
+        Plot_in_df = Plot_in_df_tp.transpose()
+        uni_colors = ['none', 'cornflowerblue', 'cornflowerblue','cornflowerblue','cornflowerblue']
+        Plot_in_df_tp.plot.bar(stacked=True, color=uni_colors, edgecolor='none', legend=False, width=0.5,ax=ax, bottom=Bottom)  
+        plt.plot(Plot_in_df_tp['Med'], linestyle="", markersize=23, 
+             marker="_", color='darkblue', label="Median")
+        z = plt.axhline(float(0), linestyle='--', color='red', alpha=.5)
+        z.set_zorder(-1)
+        #plt.ylim(xmin, xmax)
+        plt.title(Clim_var_type + ' All Delstas ' + Stats)
+        ax.grid(False)
+        for tick in ax.get_xticklabels():
+            tick.set_rotation(90)
+        fig.tight_layout()
+        fig.patch.set_alpha(ALPHA_figs)
+        #plt.show()
+        #export plot to png
+        out_file_name = Estuary  + '_' + Clim_var_type + '_' + Stats + '_'  + Base_period_start +  '_'  + Base_period_end +  '_'  +  Version + '_All_Deltas_In1.png'
+        out_path = output_directory + '/' + out_file_name
+        fig.savefig(out_path)
+    
+    
     if present_day_plot == 'yes':  
         #print present day climate data
         fig = plt.figure(figsize=(5,4))
@@ -350,7 +390,7 @@ for Est in Estuaries:
         fig.patch.set_alpha(0)
         plt.ylim(13, xmax)
         #export plot to png
-        out_file_name = Estuary  + '_' + Clim_var_type + '_' + Stats + '_'  + Base_period_start +  '_'  + Base_period_end +  Version + 'Present_Day_Period.png'
+        out_file_name = Estuary  + '_' + Clim_var_type + '_' + Stats + '_'  + Base_period_start +  '_'  + Base_period_end +  '_'  +  Version + 'Present_Day_Period.png'
         out_path = output_directory + '/' + out_file_name
         fig.savefig(out_path)
         # use transparent=True if you want the whole figure with a transparent background

Analysis/Code/P1_NARCliM_plots_Windows.py

@@ -26,6 +26,10 @@ os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdo
 import climdata_fcts as fct
 import silo as sil
 ALPHA_figs = 0 
+font = {'family' : 'sans-serif',
+        'weight' : 'normal',
+        'size'   : 14}
+matplotlib.rc('font', **font)
 #==========================================================#
 # 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/')
@@ -44,14 +48,14 @@ for Est in Estuaries:
     Estuary = Est # 'Belongil'
     print Estuary
     #Clim_var_type  = 'potevpmean'    #  '*' will create pdf for all variables in folder  "pracc*|tasmax*"   
-    Clim_var_types  = ['tasmean']
+    Clim_var_types  = ['pracc']
     for climvar in Clim_var_types:
         Clim_var_type  = climvar 
-        plot_pdf = 'yes'
+        plot_pdf = 'no'
         plot_pngs = 'yes'
-        delta_csv = 'yes'
+        delta_csv = 'no'
         Stats = 'mean'   # 'maxdaily', 'mean'
-        Version = 'V1'
+        Version = 'V2'
         #==========================================================#
         
         
@@ -299,7 +303,7 @@ for Est in Estuaries:
         #output some summary plot into pdf
         #==========================================================#
         if plot_pdf == 'yes':
- #plt.cm.Paired(np.arange(len(Fdf_1900_2080_means)))
+            #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 + '_NARCliM_summary_' + Version + '.pdf'
             pdf_out_path = output_directory +'/' + pdf_out_file_name