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#started with more sophisticated NARCLIM plots

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tinoheimhuber 7 years ago
parent 3ca11831d8
commit bbec9f1891
1 changed files with 59 additions and 16 deletions
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75
Analysis/Code/P1_NARCliM_plots_Windows.py
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@ -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

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