def makeFigureS20_SatisficingSurfaces_100samples():
designs = ['LHsamples_original_100_AnnQonly','CMIPunscaled_SOWs','Paleo_SOWs','LHsamples_wider_100_AnnQonly']
titles = ['Box Around Historical','CMIP','Paleo','All-Encompassing']
structures = ['53_ADC022','7200645','3704614']
sns.set_style("dark")
fig = plt.figure()
for i,structure in enumerate(structures):
for j,design in enumerate(designs):
ax = fig.add_subplot(3,4,i*4+j+1)
allSOWs, historic_percents, frequencies, magnitudes, gridcells, im = plotFailureHeatmap(ax, design, structure, False)
for k in range(len(historic_percents)):
if historic_percents[k] != 0: # highlight historical frequencies at each magnitude in orange
highlight_cell(k, gridcells[k], ax, color="orange", linewidth=2)
if i == 0:
ax.set_title(titles[j],fontsize=18)
cbar_ax = fig.add_axes([0.85, 0.15, 0.03, 0.7])
cbar = fig.colorbar(im, cax=cbar_ax)
cbar.ax.set_ylabel("Percent of realizations\nin which criterion is met", rotation=-90, va="bottom",fontsize=16)
fig.subplots_adjust(right=0.8,wspace=0.5,hspace=0.5)
fig.set_size_inches([19.2,9.6])
fig.savefig("FigureS20_SatisficingSurfaces_100samples.pdf")
fig.clf()
return None
def addPercentileBlocks(historic_percents, gridcells, percentiles,
short_magnitudes, ax):
for i in range(len(percentiles)):
highlight_cell(int(short_magnitudes[i] / 10) - 1,
int((100 - percentiles[i]) / 10) - 1,
ax,
color="black",
linewidth=2)
for i in range(len(historic_percents)):
if historic_percents[i] != 0:
highlight_cell(i, gridcells[i], ax, color="orange", linewidth=2)
return None
def makeFigureS21_RobustnessRanks_100samples():
allRobustness = np.load(
'../Output_analysis/RobustnessRanks_no15mi_100samples.npy')
nstructures = np.shape(allRobustness)[2]
colors = ['#fb8072', '#ffffb3', '#b3de69',
'#bebada'] # original, CMIP, paleo, wider
colOrder = [0, 2, 3, 1] # order of columns to plot
labels = ['Box Around Historical', 'CMIP', 'Paleo', 'All-Encompassing']
design = 'LHsamples_wider_100_AnnQonly'
structure = '53_ADC022'
#sns.set_style("dark")
mags = np.array([10, 50, 100])
freqs = np.array([50, 100])
sns.set_style("dark")
fig = plt.figure()
for i in range(len(mags)):
ax = fig.add_subplot(2, 3, i + 1)
# sort structures in all-encompassing design
sortedIndices = np.argsort(-allRobustness[int(mags[i] / 10) - 1, 0, :,
1]) # freq=10% of the time
for k in range(4): # loop through experimental designs
ax.scatter(range(1,nstructures+1),\
ss.rankdata(-allRobustness[int(mags[i]/10)-1,0,:,colOrder[k]])[sortedIndices],\
color=colors[k],label=labels[k])
if i == 2:
ax.set_xlabel('Robustness Rank in\nAll-Encompassing Experiment',
fontsize=16)
elif i == 0:
ax.set_ylabel('Robustness Rank in\neach Experiment', fontsize=16)
ax.tick_params(axis='both', labelsize=14)
ax.set_title('<' + str(mags[i]) + '% shortage <10% of the time',
fontsize=18)
for j in range(len(freqs)):
ax = fig.add_subplot(2, 3, j + 4)
# sort structures in all-encompassing design
sortedIndices = np.argsort(
-allRobustness[0, int(freqs[j] / 10) - 1, :,
1]) # mag=10% of demand
for k in range(4): # loop through experimental designs
ax.scatter(range(1,nstructures+1),\
ss.rankdata(-allRobustness[0,int(freqs[j]/10)-1,:,colOrder[k]])[sortedIndices],\
color=colors[k],label=labels[k])
if j == 0:
ax.set_ylabel('Robustness Rank in\neach Experiment', fontsize=16)
ax.tick_params(axis='both', labelsize=14)
ax.set_title('<10% shortage <' + str(freqs[j]) + '% of the time',
fontsize=18)
ax.set_xlabel('Robustness Rank in\nAll-Encompassing Experiment',
fontsize=16)
handles, labels = ax.get_legend_handles_labels()
ax = fig.add_subplot(2, 3, 6)
allSOWs, historic_percents, frequencies, magnitudes, gridcells, im = plotFailureHeatmap(
ax, design, structure)
for i in range(len(historic_percents)):
if historic_percents[
i] != 0: # highlight historical frequencies at each magnitude in orange
highlight_cell(i, gridcells[i], ax, color="orange", linewidth=2)
# highlight criteria in ranks in black
for i in range(len(mags)):
highlight_cell(0,
int(mags[i] / 10) - 1,
ax,
color='black',
linewidth=2)
for i in range(len(freqs)):
highlight_cell(int(freqs[i] / 10) - 1,
0,
ax,
color='black',
linewidth=2)
fig.set_size_inches([19.2, 9.6])
fig.subplots_adjust(bottom=0.2, wspace=0.25, hspace=0.35)
fig.legend(handles,
labels,
loc='lower center',
ncol=2,
frameon=True,
fontsize=16)
fig.savefig('FigureS21_RobustnessRanks_100samples.pdf')
fig.clf()
return None