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#First successfull recreation of the existing method

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after first meeting with Alejandro di Luca. We
are not comparing present day variability with
near and far future ensemble model deltas (changes
in 20-year average climate). These changes are added
to the present day median climate for the plots.
Development1
tinoheimhuber 7 years ago
parent fd19552a5f
commit b2dd285534
1 changed files with 70 additions and 33 deletions
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103
Analysis/Code/P1_NARCliM_plots_Windows.py
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@ -29,8 +29,10 @@ os.chdir('C:/Users/z5025317/WRL_Postdoc/Projects/Paper#1/')
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 = "*" #will create pdf for all variables in folder
Clim_var_type = "*" #will create pdf for all variables in folder
subset_ensemble = 'no' # is yes, only the model with the lowest, median and max difference between present day and far future are selected
#####################################----------------------------------
#set directory path for output files
output_directory = 'Output/'+ Estuary
#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
@ -64,37 +66,37 @@ for clim_var_csv_path in Clim_Var_CSVs:
#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)] # not necessary if not using reanalysis models for base period
#Select the 3 most representative models (min med and max difference betwen far future and present)
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
#add a simple increasing integer index
df = df.reset_index()
df= df[df.index % 3 != 1]
df['C'] = df[0].diff()
df = df.reset_index()
df= df[df.index % 2 != 0]
#get max difference model (difference between far future and prsent day)
a = df[df.index == df['C'].argmax(skipna=True)]
Max_dif_mod_name = a.iloc[0]['index']
#get min difference model
a = df[df.index == df['C'].argmin(skipna=True)]
Min_dif_mod_name = a.iloc[0]['index']
#get the model which difference is closest to the median difference
df['D'] = abs(df['C']- df['C'].median())
a = df[df.index == df['D'].argmin(skipna=True)]
Med_dif_mod_name = a.iloc[0]['index']
#data frame with min med and max difference model
df2 = Fdf_1900_2080.filter(regex= Min_dif_mod_name[:-5] + '|' + Med_dif_mod_name[:-5] + '|' + Max_dif_mod_name[:-5] )
dfall = df2.reindex_axis(sorted(df2.columns), axis=1)
#data frame with individual models
dfmin = Fdf_1900_2080.filter(regex= Min_dif_mod_name[:-5])
dfmax = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5])
dfmed = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5])
# use only the 3 representative models for the analysis
Fdf_1900_2080_all_mods = Fdf_1900_2080
Fdf_1900_2080 = dfall
if subset_ensemble == 'yes':
#Select the 3 most representative models (min med and max difference betwen far future and present)
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
#add a simple increasing integer index
df = df.reset_index()
df= df[df.index % 3 != 1]
df['C'] = df[0].diff()
df = df.reset_index()
df= df[df.index % 2 != 0]
#get max difference model (difference between far future and prsent day)
a = df[df.index == df['C'].argmax(skipna=True)]
Max_dif_mod_name = a.iloc[0]['index']
#get min difference model
a = df[df.index == df['C'].argmin(skipna=True)]
Min_dif_mod_name = a.iloc[0]['index']
#get the model which difference is closest to the median difference
df['D'] = abs(df['C']- df['C'].median())
a = df[df.index == df['D'].argmin(skipna=True)]
Med_dif_mod_name = a.iloc[0]['index']
#data frame with min med and max difference model
df2 = Fdf_1900_2080.filter(regex= Min_dif_mod_name[:-5] + '|' + Med_dif_mod_name[:-5] + '|' + Max_dif_mod_name[:-5] )
dfall = df2.reindex_axis(sorted(df2.columns), axis=1)
#data frame with individual models
dfmin = Fdf_1900_2080.filter(regex= Min_dif_mod_name[:-5])
dfmax = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5])
dfmed = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5])
# use only the 3 representative models for the analysis
Fdf_1900_2080_all_mods = Fdf_1900_2080
Fdf_1900_2080 = dfall
#Aggregate daily df to annual time series
if (Clim_var_type == 'pracc' or Clim_var_type == 'evspsblmean' or Clim_var_type == 'potevpmean'
@ -120,6 +122,41 @@ for clim_var_csv_path in Clim_Var_CSVs:
Fdf_1900_2080_means.plot(kind='bar').figure
print('-------------------------------------------')
#Create Deltas of average change
models = list(Fdf_1900_2080_means.index)
newmodel = []
type(newmodel)
for each in models:
newmodel.append(each[:-5])
unique_models = set(newmodel)
# calculate diff for each unique model
delta_NF_ensemble = []
delta_FF_ensemble = []
for unique_model in unique_models:
dfdiff = Fdf_1900_2080_means.filter(regex= unique_model)
type(dfdiff)
delta_NF = dfdiff[1] - dfdiff[0]
delta_NF_ensemble.append(delta_NF)
delta_FF = dfdiff[2] - dfdiff[1]
delta_FF_ensemble.append(delta_FF)
np.percentile(delta_NF, 50)
delta_df
delta_df1=pd.DataFrame(delta_NF_ensemble, index=unique_models)
delta_df2=pd.DataFrame(delta_FF_ensemble, index=unique_models)
delta_df=pd.concat([delta_df1, delta_df2], axis=1)
delta_df.plot(kind='box').figure
pd.DataFrame()
concat([Full_df, GCM_df], axis=1)
delta_df ensemble.plot(kind='bar')
dfmax = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5])
dfmed = Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5])
#create a dataframe that has a single column for present day, near and far future for the (3 selected models)
len(Fdf_1900_2080.columns)
Full_current_df = Fdf_1900_2080.iloc[:,range(0,3)]
@ -149,7 +186,7 @@ for clim_var_csv_path in Clim_Var_CSVs:
dfall = dfa1.append(dfa2).append(dfa3)
#write the key plots to a single pdf document
pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary2.pdf'
pdf_out_file_name = Clim_var_type + '_start_' + Base_period_start + '_NARCliM_summary_B.pdf'
pdf_out_path = output_directory +'/' + pdf_out_file_name
#open pdf and add the plots

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