#fully working set of codes for generating the NARCLIM

Delta CSVs to then generate the climate deviation plots by combining with observational time seris
7 years ago · 8a771b65b6
parent b2fc7135d9
commit 8a771b65b6
3 changed files with 213 additions and 147 deletions
--- a/Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py
+++ b/Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py
@ -2,8 +2,9 @@
 #####################################----------------------------------
 #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
+#code for creating future climate variability deviation plots for NARCLIM variables. 
+#Inputs: Uses CSV files that containthe deltas of all 12 NARCLIM models for 1 grid cell at the site of interest, generated with P1_NARCLIM_plots_Windows.py
+#This code is used only for the NARCLIM variables - a separate code is used for ocean variables etc that are not part of the NARCLIM ensemble
 #####################################----------------------------------
 #Load packages
 #####################################----------------------------------
@ -27,10 +28,10 @@ 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'
+Estuary = 'Terrigal' # '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'    
+Clim_var_type  = "wssmean*"   #  '*' will create pdf for all variables in folder  
+Present_Day_Clim_Var = 'Wind'    #MaxT, MinT, Rainfall, ET
 #####################################----------------------------------

 #set directory path for output files
@ -44,134 +45,164 @@ if not os.path.exists(output_directory):
    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
+clim_var_csv_path = Clim_Var_CSVs[0]
+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
+if Clim_var_type == 'evspsblmean': #ET time series that we have is not in the same format as the other variables, hence the different treatment
    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)
+    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)
+    Present_day_df.index = Dates
+    [minplotDelta, maxplotDelta]=[50,50]
+#for tasmean, observed min and max T need to be converted into mean T
+elif Clim_var_type == 'tasmean':
+    Present_day_Var_CSV = glob.glob('./Data/Wheather_Station_Data/**/' + Estuary + '_MaxT*csv')
+    Present_day_df = pd.read_csv(Present_day_Var_CSV[0])
+    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_MaxT_df = Present_day_df.iloc[:,5]
+    Present_day_Var_CSV = glob.glob('./Data/Wheather_Station_Data/**/' + Estuary + '_MinT*csv')
+    Present_day_df = pd.read_csv(Present_day_Var_CSV[0])
+    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_MinT_df = Present_day_df.iloc[:,5]
+    Present_day_df = (Present_day_MaxT_df + Present_day_MinT_df)/2
+    [minplotDelta, maxplotDelta]=[1,2]
+elif Clim_var_type == 'tasmax':
+    [minplotDelta, maxplotDelta]=[1,2]  
+elif Clim_var_type == 'wssmean' or  Clim_var_type == 'wss1Hmaxtstep':
+    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])
+    Present_day_df.index = Present_day_df[['Year', 'Month', 'Day', 'Hour']].apply(lambda s : datetime(*s),axis = 1)
+    Present_day_df = Present_day_df.filter(regex= 'm/s') 
+    Present_day_df = Present_day_df.replace(r'\s+', np.nan, regex=True)
+    Present_day_df['Wind speed measured in m/s'] = Present_day_df['Wind speed measured in m/s'].convert_objects(convert_numeric=True)
+    [minplotDelta, maxplotDelta]=[1, 1.5]  
+else:   
+    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])
+    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]
+    [minplotDelta, maxplotDelta]=[50,50]
+#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)]
        #
-        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
+        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':
-            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)
+            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))-minplotDelta)
+        xmax = int(max(Plot_in_df.max(axis=1))+maxplotDelta)
+    else:
+        xmin = int(min(Plot_in_df.min(axis=1))-minplotDelta)
+        xmax = int(max(Plot_in_df.max(axis=1))+maxplotDelta)
+    #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)
+    z = plt.axhline(float(Present_Day_Mean), linestyle='--', color='red', 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)
    
    
    
--- a/Analysis/Code/P1_NARCliM_NC_to_CSV_CCRC_SS_BASH_script_readme.txt
+++ b/Analysis/Code/P1_NARCliM_NC_to_CSV_CCRC_SS_BASH_script_readme.txt
@ -1,13 +1,16 @@
-Variables:
+Variables available from NARCLIM (output):

-'evspsblmean'   	water_evaporation flux (actual ET)
-'potevpmean'    	potential ET
+'evspsblmean'   	water_evaporation flux (actual ET) long_name: Surface evaporation standard_name: water_evaporation_flux units: kg m-2 s-1
+'potevpmean'    	potential ET water_potential_evaporation_flux kg m-2 s-1
 'tasmean’               mean near surface temperature
 ‘tasmax’		maximum near surface temperature
 ‘pracc’ 		precipitation daily precipitation sum (sum of convective prcacc and stratiform prncacc precip) 
 ‘pr1Hmaxtstep’     	maximum 1 hour interval rainfall in a one day period
-‘wssmean’     		mean daily windspeed 
-'wss1Hmaxtstep'		maximum 1 hour interval mean windspeeds in a one day period
+'pr1Hmaxtstep' 		Max. 1-hour time-window moving averaged precipitation rate units: kg m-2 s-1 maximum 1-hour time-window moving averaged values from point values 60.0 second
+'wss1Hmaxtstep'		Max. 1-hour time-window moving averaged surface wind speed units: m s-1 maximum 1-hour time-window moving averaged values from point values 60.0 second
+'wssmax' 		Surface wind speed standard_name: air_velocity units: m s-1 height: 10 m
+'wssmean'		Surface wind speed standard_name: air_velocity units: m s-1
+

 Sites:
 Northern NSW:
@ -25,15 +28,47 @@ Towamba River: -37.1, 149.91
 Nadgee Lake: -37.47, 149.97

 Bash-Code for netcdf interrogation:
-latitude=-35.76
-longitude = 150.25
-python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat $latitude  --lon $longitude --varName 'tasmean' --domain 'd02' --timestep 'DAY';
-python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat -32.843 --lon 151.706 --varName 'tasmax' --domain 'd02' --timestep 'DAY';
-python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat -32.843 --lon 151.706 --varName 'pr1Hmaxtstep' --domain 'd02' --timestep 'DAY';
-python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat -32.843 --lon 151.706 --varName 'wssmean' --domain 'd02' --timestep 'DAY';
-python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat -32.843 --lon 151.706 --varName 'pracc' --domain 'd02' --timestep 'DAY';
-python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat -32.843 --lon 151.706 --varName 'wss1Hmaxtstep' --domain 'd02' --timestep 'DAY';
-python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat -32.843 --lon 151.706 --varName 'evspsblmean' --domain 'd02' --timestep 'DAY';
+1st step: log into storm servers: Putty: hurricane.ccrc.unsw.edu.au or typhoon.ccrc.unsw.edu.au or cyclone.ccrc.unsw.edu.au + UNSW credentials (zID)
+In BASH:
+module load python 
+latitude=-28.17
+longitude=153.56
+python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat $latitude --lon $longitude --varName 'tasmean' --domain 'd02' --timestep 'DAY';
+python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat $latitude --lon $longitude --varName 'tasmax' --domain 'd02' --timestep 'DAY';
+python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat $latitude --lon $longitude --varName 'pr1Hmaxtstep' --domain 'd02' --timestep 'DAY';
+python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat $latitude --lon $longitude --varName 'wssmean' --domain 'd02' --timestep 'DAY';
+python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat $latitude --lon $longitude --varName 'pracc' --domain 'd02' --timestep 'DAY';
+python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat $latitude --lon $longitude --varName 'wss1Hmaxtstep' --domain 'd02' --timestep 'DAY';
+python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat $latitude --lon $longitude --varName 'evspsblmean' --domain 'd02' --timestep 'DAY';
+python /srv/ccrc/data02/z5025317/Code_execution/P1_NARCliM_NC_to_CSV_CCRC_SS.py --lat $latitude --lon $longitude --varName 'potevpmean' --domain 'd02' --timestep 'DAY'
+
+#1 The above code extracts time series from the full model ensemble over a single model grid cell (based on lat lon input) for the above variables of interest and stores into CSV files.
+Example of output name = evspsblmean_35.76_150.25_NARCliM_summary.csv
+
+#2 The "P1_NARCliM_plots_Windows" code takes these CSV files as input and creates a) a pdf wiht a number of time series and box plots and b) another CSV file containing the Deltas between present day, near and far future
+for each model in the ensemble. Output: C:\Users\z5025317\WRL_Postdoc\Projects\Paper#1\Output\Nadgee\Nadgee_tasmax_NARCliM_ensemble_changes.csv
+
+#3 The "P1_NARCliM_First_Pass_variab_deviation_plots" code takes those Delta CSV files as input and generates the future climate deviation plots that were originally developed by Duncan Rayner. 
+Run the code with different constellations of Estuary (study sites) and climate variables: 
+e.g. Clim_var_type  = "tasmax*"   #  '*' will create pdf for all variables in folder  
+Present_Day_Clim_Var = 'MaxT'    #MaxT, MinT, Rainfall, (the name for present day clim var refers to the name of the observed climate data that's used for the baseline period variability. 
+
+#!!!# Important!
+#For present day variability data, only rainfall and temperature data actually correspond to the study sites. ET and Wind are taken from the existing project folder and hence, are for a Hunter weather station
+#e.g. Terrigal_Wind and Terrigal_ET are actually Hunter in reality. This is because we don't have ET for other sites than Hunter at this stage. 
+
+
+##PROBLEM: Without changing anything, the P1_NARCliM_NC_to_CSV_CCRC_SS.py stopped working properly on the CCRC storm servers. It's not giving an error but loading the nc files with Dataset(nc) just takes unlimited time.
+It used to take only a few seconds. NOT solved yet as of 7th of May 2018.
+
+
+
+
+
+
+
+
+

 deactivate –
 conda env create --name EEenv -- file ‘C:\Users\z5025317\WRL_Postdoc\Software\EE\ee-jupyter-examples-master\kilian_env’
--- a/Analysis/Code/P1_NARCliM_plots_Windows.py
+++ b/Analysis/Code/P1_NARCliM_plots_Windows.py
@ -18,22 +18,20 @@ 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/')
 #
-#
 #####################################----------------------------------
 #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 = 'Terrigal' # 'Belongil'
-Clim_var_type  = "pracc*|tasmax*"   #  '*' will create pdf for all variables in folder 
+Clim_var_type  = "wssmean*"   #  '*' will create pdf for all variables in folder  "pracc*|tasmax*"
 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
 output_directory = 'Output/'+ Estuary
 #output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
@ -62,6 +60,8 @@ for clim_var_csv_path in Clim_Var_CSVs:
    #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
+    if Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean':
+        Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60*24
    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)