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#added better png plotting for the tech guide plots

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Valentin Heimhuber 6 years ago
parent 9eda19058e
commit 78fd10e93b
3 changed files with 130 additions and 22 deletions
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9
Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py
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@ -48,7 +48,7 @@ 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 = 'tasmax' #Name of climate variable in NARCLIM models '*' will create pdf for all variables in folder
Clim_var_type = 'tasmean' #Name of climate variable in NARCLIM models '*' will create pdf for all variables in folder
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
@ -57,8 +57,8 @@ for Est in Estuaries:
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 = 'days_h_35' #'maxdaily' #maximum takes the annual max Precipitation instead of the sum
ALPHA_figs = 1 #Set alpha of figure background (0 makes everything around the plot panel transparent)
Stats = 'mean' #'maxdaily' #maximum takes the annual max Precipitation instead of the sum
ALPHA_figs = 0 #Set alpha of figure background (0 makes everything around the plot panel transparent)
#==========================================================#
@ -290,6 +290,7 @@ for Est in Estuaries:
tick.set_rotation(0)
fig.tight_layout()
fig.patch.set_alpha(ALPHA_figs)
plt.set_facecolor('g')
if temp == 'annual':
ax=plt.subplot(2,2,1)
@ -319,7 +320,7 @@ for Est in Estuaries:
if presentdaybar == False:
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 + '_' + '.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)

143
Analysis/Code/P1_NARCliM_plots_Windows.py
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@ -25,7 +25,7 @@ matplotlib.style.use('ggplot')
os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Analysis/Code')
import climdata_fcts as fct
import silo as sil
ALPHA_figs = 0
#==========================================================#
# 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/')
@ -44,12 +44,13 @@ 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 = ['tasmax']
Clim_var_types = ['tasmean']
for climvar in Clim_var_types:
Clim_var_type = climvar
plot_pdf = 'no'
plot_pdf = 'yes'
plot_pngs = 'yes'
delta_csv = 'yes'
Stats = 'days_h_35' # 'maxdaily', 'mean'
Stats = 'mean' # 'maxdaily', 'mean'
Version = 'V1'
#==========================================================#
@ -63,6 +64,15 @@ for Est in Estuaries:
print('-------------------------------------------')
print("output directory folder didn't exist and was generated")
print('-------------------------------------------')
#set directory path for individual png files
png_output_directory = 'Output/' + Case_Study_Name + '/' + Estuary + '/NARCLIM_Key_Figs'
#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
if not os.path.exists(png_output_directory):
os.makedirs(png_output_directory)
print('-------------------------------------------')
print("output directory folder didn't exist and was generated")
print('-------------------------------------------')
#==========================================================#
@ -182,23 +192,116 @@ for Est in Estuaries:
Summarized_df.columns = ['present', 'near', 'far']
#==========================================================#
#==========================================================#
#generate colour schemes for plotting
#==========================================================#
plotcolours36 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal',
'darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal',
'darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal']
plotcolours36b = ['tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ,
'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ,
'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ]
plotcolours12 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal']
plotcolours15 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal', 'lightgreen','lightpink','slateblue']
#==========================================================#
#==========================================================#
#output crucial summary plots into individual png files
#==========================================================#
if plot_pngs == 'yes':
#=========#
#Barplot of near and far future deltas
#=========#
#out name
png_out_file_name = Clim_var_type + '_' + Stats + '_Deltas_Barplot_' + Version + '.png'
png_out_path = png_output_directory + '/' + png_out_file_name
#prepare data frame for plot
neardeltadf=delta_all_df['near']
ymin = min(neardeltadf) + 0.1 *min(neardeltadf)
ymax = max(neardeltadf) + 0.1 * max(neardeltadf)
neardeltadf=delta_all_df['far']
ymin2 = min(neardeltadf) + 0.1 *min(neardeltadf)
ymax2 = max(neardeltadf) + 0.1 * max(neardeltadf)
ymin = min(ymin, ymin2)
if (Clim_var_type == 'tasmax' or Clim_var_type == 'tasmean'):
ymin = 0
ymax = max(ymax, ymax2)
#
fig = plt.figure(figsize=(5,6))
ax=plt.subplot(2,1,1)
plt.title(Clim_var_type + ' - ' + Stats + ' - deltas - near')
neardeltadf=delta_all_df['near']
neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
plt.xticks([])
ax=plt.subplot(2,1,2)
plt.title(Clim_var_type + ' - ' + Stats + ' - deltas - far')
neardeltadf=delta_all_df['far']
neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
ax.xaxis.grid(False)
#ax.patch.set_alpha(ALPHA_figs)
fig.patch.set_alpha(ALPHA_figs)
fig.tight_layout()
fig.savefig(png_out_path)
#=========#
#=========#
#full period density comparison
#=========#
#out name
png_out_file_name = Clim_var_type + '_' + Stats + '_MaxDeltaMod_Histogram_' + Version + '.png'
png_out_path = png_output_directory + '/' + png_out_file_name
plt.title(Clim_var_type + ' - ' + Stats + ' - hist - full period - max delta model')
#prepare data
xmin = float(max(np.nanpercentile(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]),50) - 4 * np.std(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]))))
xmax = float(max(np.nanpercentile(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]),50) + 4 * np.std(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]))))
fig = plt.figure(figsize=(5,5))
ax=plt.subplot(2,1,1)
Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]).plot.kde(xlim=(xmin,xmax), ax=ax)
plt.legend(loc=9, bbox_to_anchor=(0.5, -0.3))
#ax.xaxis.grid(False)
ax.yaxis.grid(False)
fig.patch.set_alpha(ALPHA_figs)
fig.tight_layout()
fig.savefig(png_out_path)
#=========#
#=========#
# time series plot annual ALL models
#=========#
png_out_file_name = Clim_var_type + '_' + Stats + '_TimeSeries_AllMods_' + Version + '.png'
png_out_path = png_output_directory + '/' + png_out_file_name
plt.title(Clim_var_type + ' - ' + Stats + ' - Time series - representative models')
#prepare data
Mod_order = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,19,20,21,16,17,18,22,23,24,31,32,33,25,26,27,28,29,30,34,35,36]
test = Fdf_1900_2080_annual
Mod_Names = test.columns
New_Mod_Name = []
for i in range(0,len(Mod_Names)):
New_Mod_Name.append(str(Mod_order[i]+10) + '_' + Mod_Names[i])
test.columns = New_Mod_Name
test_sorted = test.reindex_axis(sorted(test.columns), axis=1)
colnamest = test.columns
test_sorted.columns = [w[3:-5] for w in colnamest]
#plot
fig = plt.figure(figsize=(8,7))
ax=plt.subplot(2,1,1)
test_sorted.plot(ax=ax,color = plotcolours36)
plt.legend(loc=9, bbox_to_anchor=(0.5, -0.2))
ax.xaxis.grid(False)
fig.patch.set_alpha(ALPHA_figs)
fig.tight_layout()
fig.savefig(png_out_path)
#=========#
#==========================================================#
#output some summary plot into pdf
#==========================================================#
if plot_pdf == 'yes':
plotcolours36 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal',
'darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal',
'darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal']
plotcolours36b = ['tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ,
'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ,
'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ]
plotcolours12 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal']
plotcolours15 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal', 'lightgreen','lightpink','slateblue']
#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 + '_start_' + Base_period_start + '_NARCliM_summary_' + Version + '.pdf'
pdf_out_file_name = Clim_var_type + '_' + Stats + '_NARCliM_summary_' + Version + '.pdf'
pdf_out_path = output_directory +'/' + pdf_out_file_name
#open pdf and add the plots
with PdfPages(pdf_out_path) as pdf:
@ -226,6 +329,7 @@ for Est in Estuaries:
plt.title(Clim_var_type + ' - model deltas - near-present')
neardeltadf=delta_all_df['near']
neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
ax.patch.set_alpha(ALPHA_figs)
plt.xticks([])
#ax.xaxis.set_ticklabels([])
#pdf.savefig(bbox_inches='tight', ylim=(ymin,ymax), pad_inches=0.4)
@ -234,9 +338,10 @@ for Est in Estuaries:
ax=plt.subplot(2,1,2)
plt.title(Clim_var_type + ' - model deltas - far-present')
neardeltadf=delta_all_df['far']
neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
fig = neardeltadf.plot(kind='bar', color=plotcolours15, ylim=(ymin,ymax), ax=ax)
ax.xaxis.grid(False)
#fig.patch.set_alpha(0)
ax.patch.set_alpha(ALPHA_figs)
#fig.patch.set_alpha(ALPHA_figs)
#plt.show()
pdf.savefig(bbox_inches='tight', ylim=(ymin,ymax), pad_inches=0.4)
plt.close()
@ -253,6 +358,7 @@ for Est in Estuaries:
xmax = float(max(np.nanpercentile(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]),50) + 4 * np.std(Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]))))
Fdf_1900_2080.filter(regex= Max_dif_mod_name[:-5]).plot.kde(xlim=(xmin,xmax))
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
fig.patch.set_alpha(ALPHA_figs)
plt.close()
#annual box
plt.title(Clim_var_type + ' - Annual means/sums for max diff model')
@ -277,8 +383,9 @@ for Est in Estuaries:
test_sorted = test.reindex_axis(sorted(test.columns), axis=1)
colnamest = test.columns
test_sorted.columns = [w[3:-5] for w in colnamest]
test_sorted.plot(legend=False, color = plotcolours36)
fig = test_sorted.plot(legend=False, color = plotcolours36)
pdf.savefig(bbox_inches='tight', pad_inches=0.4)
fig.patch.set_alpha(ALPHA_figs)
plt.close()
# time series plot annual ALL models
plt.title(Clim_var_type + ' - Time series - representative models')

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