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#added a couple of plotting abilities to the existing code and all is working.

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the only problem remaining is that currently, the present day plot for extreme indices isn't working properly
it's showing the annual sums or averages
Development1
Valentin Heimhuber 6 years ago
parent 78fd10e93b
commit 00e63c6c29
2 changed files with 64 additions and 20 deletions
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66
Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py
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@ -48,21 +48,25 @@ for Est in Estuaries:
Base_period_start = '1970-01-01' #Start of interval for base period of climate variability
Base_period_DELTA_start = '1990-01-01' #Start of interval used for calculating mean and SD for base period (Delta base period)
Base_period_end = '2009-01-01' #use last day that's not included in period as < is used for subsetting
Clim_var_type = 'tasmean' #Name of climate variable in NARCLIM models '*' will create pdf for all variables in folder
Clim_var_type = 'pracc' #Name of climate variable in NARCLIM models '*' will create pdf for all variables in folder
Stats = 'maxdaily' #'maxdaily' #maximum takes the annual max Precipitation instead of the sum
PD_Datasource = 'SILO' #Source for present day climate data (historic time series) can be either: 'Station' or 'SILO'
SILO_Clim_var = ['max_temp'] #select the SILO clim variable to be used for base period. - see silo.py for detailed descriptions
SILO_Clim_var = ['daily_rain'] #select the SILO clim variable to be used for base period. - see silo.py for detailed descriptions
Location = 'Estuary' # pick locaiton for extracting the SILO data can be: Estuary, Catchment, or Ocean
presentdaybar = False #include a bar for present day variability in the plots?
present_day_plot = 'no' #save a time series of present day
Version = "V1"
Stats = 'mean' #'maxdaily' #maximum takes the annual max Precipitation instead of the sum
present_day_plot = 'yes' #save a time series of present day
Allin1_delta_plot = 'no'
Version = "V4"
ALPHA_figs = 0 #Set alpha of figure background (0 makes everything around the plot panel transparent)
font = {'family' : 'sans-serif',
'weight' : 'normal',
'size' : 14}
matplotlib.rc('font', **font) #size of axis labels
#==========================================================#
#==========================================================#
#set directory path for output files
output_directory = 'Output/' + Case_Study_Name + '/' + Estuary + '/' + '/Clim_Deviation_Plots/'
@ -190,6 +194,8 @@ for Est in Estuaries:
times = ['annual', 'DJF', 'MAM', 'JJA','SON']
fig = plt.figure(figsize=(14,8))
delta_all_df = pd.DataFrame()
Combined_Delta_df = pd.DataFrame()
Combined_Med_df = pd.DataFrame()
i=1
for temp in times:
#temp = 'annual'
@ -197,20 +203,22 @@ for Est in Estuaries:
if temp == 'annual':
Ensemble_Delta_df = Ensemble_Delta_full_df.iloc[:,range(0,2)]
Present_Day_ref_df = Present_day_df_annual
Column_names = ['near', 'far']
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']
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']
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']
Column_names = ['JJA_near', 'JJA_far']
if temp == 'SON':
Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==4]
Column_names = ['SON_near', 'SON_far']
Present_Day_ref_df = Mean_df
#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)])
@ -290,7 +298,7 @@ for Est in Estuaries:
tick.set_rotation(0)
fig.tight_layout()
fig.patch.set_alpha(ALPHA_figs)
plt.set_facecolor('g')
#plt.set_facecolor('g')
if temp == 'annual':
ax=plt.subplot(2,2,1)
@ -315,15 +323,47 @@ for Est in Estuaries:
i=i+4
else:
i=i+1
#create a data frame that contains all of the delta bars
Plot_in_df_tp.index = Column_names
Plot_in_df_tp['-2std'] = Plot_in_df_tp['-2std'] - Present_Day_Mean
Combined_Delta_df = pd.concat([Combined_Delta_df, Plot_in_df_tp], axis=0)
#plt.show()
if presentdaybar == False:
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + PD_Datasource + '_' + SILO_Clim_var[0] + Version + '_' + '_NPDB.png'
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + PD_Datasource + '_' + SILO_Clim_var[0] + '_' + Version + '_' + '_NPDB.png'
else:
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + PD_Datasource + '_' + SILO_Clim_var[0] + Version + '_' + '2.png'
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + PD_Datasource + '_' + SILO_Clim_var[0] + '_' + Version + '_' + '2.png'
out_path = output_directory + '/' + out_file_name
fig.savefig(out_path)
if Allin1_delta_plot == 'yes':
fig = plt.figure(figsize=(14,8))
ax = fig.add_subplot(2, 2, 1)
Plot_in_df_tp = pd.DataFrame(Combined_Delta_df)
Bottom = int(Plot_in_df_tp.stack().min(axis=None,skipna=True)) - 1
Plot_in_df_tp = Plot_in_df_tp - Bottom
Plot_in_df = Plot_in_df_tp.transpose()
uni_colors = ['none', 'cornflowerblue', 'cornflowerblue','cornflowerblue','cornflowerblue']
Plot_in_df_tp.plot.bar(stacked=True, color=uni_colors, edgecolor='none', legend=False, width=0.5,ax=ax, bottom=Bottom)
plt.plot(Plot_in_df_tp['Med'], linestyle="", markersize=23,
marker="_", color='darkblue', label="Median")
z = plt.axhline(float(0), linestyle='--', color='red', alpha=.5)
z.set_zorder(-1)
#plt.ylim(xmin, xmax)
plt.title(Clim_var_type + ' All Delstas ' + Stats)
ax.grid(False)
for tick in ax.get_xticklabels():
tick.set_rotation(90)
fig.tight_layout()
fig.patch.set_alpha(ALPHA_figs)
#plt.show()
#export plot to png
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + Base_period_start + '_' + Base_period_end + '_' + Version + '_All_Deltas_In1.png'
out_path = output_directory + '/' + out_file_name
fig.savefig(out_path)
if present_day_plot == 'yes':
#print present day climate data
fig = plt.figure(figsize=(5,4))
@ -350,7 +390,7 @@ for Est in Estuaries:
fig.patch.set_alpha(0)
plt.ylim(13, xmax)
#export plot to png
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + Base_period_start + '_' + Base_period_end + Version + 'Present_Day_Period.png'
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + Base_period_start + '_' + Base_period_end + '_' + Version + 'Present_Day_Period.png'
out_path = output_directory + '/' + out_file_name
fig.savefig(out_path)
# use transparent=True if you want the whole figure with a transparent background

14
Analysis/Code/P1_NARCliM_plots_Windows.py
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@ -26,6 +26,10 @@ os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdo
import climdata_fcts as fct
import silo as sil
ALPHA_figs = 0
font = {'family' : 'sans-serif',
'weight' : 'normal',
'size' : 14}
matplotlib.rc('font', **font)
#==========================================================#
# Set working direcotry (where postprocessed NARClIM data is located)
os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/')
@ -44,14 +48,14 @@ for Est in Estuaries:
Estuary = Est # 'Belongil'
print Estuary
#Clim_var_type = 'potevpmean' # '*' will create pdf for all variables in folder "pracc*|tasmax*"
Clim_var_types = ['tasmean']
Clim_var_types = ['pracc']
for climvar in Clim_var_types:
Clim_var_type = climvar
plot_pdf = 'yes'
plot_pdf = 'no'
plot_pngs = 'yes'
delta_csv = 'yes'
delta_csv = 'no'
Stats = 'mean' # 'maxdaily', 'mean'
Version = 'V1'
Version = 'V2'
#==========================================================#
@ -299,7 +303,7 @@ for Est in Estuaries:
#output some summary plot into pdf
#==========================================================#
if plot_pdf == 'yes':
#plt.cm.Paired(np.arange(len(Fdf_1900_2080_means)))
#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 + '_' + Stats + '_NARCliM_summary_' + Version + '.pdf'
pdf_out_path = output_directory +'/' + pdf_out_file_name

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