diff --git a/Analysis/Code/P1_NARCliM_plots_Windows.py b/Analysis/Code/P1_NARCliM_plots_Windows.py index e71abf1..c9e852f 100644 --- a/Analysis/Code/P1_NARCliM_plots_Windows.py +++ b/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': - Full_df = Full_df.iloc[:,0:26]-273.15 + if Clim_var_type == 'tasmean' or Clim_var_type == 'tasmax': + 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') - Summarized_df.plot.kde() + plt.title(Clim_var_type + ' - density comparison - full period - one model') + 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') - Fdf_1900_2080_annual.boxplot(rot=90) + plt.title(Clim_var_type + ' - Annual means for one model C') + Fdf_1900_2080_annual.iloc[:,[8,17,26]].boxplot(rot=90) pdf.savefig(bbox_inches='tight', pad_inches=0.4) plt.close() #monthly box