diff --git a/Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py b/Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py index 6952ff0..6bb8770 100644 --- a/Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py +++ b/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) diff --git a/Analysis/Code/P1_NARCliM_NC_to_CSV_CCRC_SS.py b/Analysis/Code/P1_NARCliM_NC_to_CSV_CCRC_SS.py index aa5c268..d55690b 100644 --- a/Analysis/Code/P1_NARCliM_NC_to_CSV_CCRC_SS.py +++ b/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)) diff --git a/Analysis/Code/P1_NARCliM_plots_Windows.py b/Analysis/Code/P1_NARCliM_plots_Windows.py index 78414f0..30511a4 100644 --- a/Analysis/Code/P1_NARCliM_plots_Windows.py +++ b/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: