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# Percentiles for analysis to loop over
percentiles = np.arange(0,100)
# Estimate upper and lower bounds
globalmax = [np.percentile(np.max(expData_sort[:,:],1),p) for p in percentiles]
globalmin = [np.percentile(np.min(expData_sort[:,:],1),p) for p in percentiles]
delta_values = pd.read_csv('./DELTA_scores.csv')
delta_values.set_index(list(delta_values)[0],inplace=True)
delta_values = delta_values.clip(lower=0)
bottom_row = pd.DataFrame(data=np.array([np.zeros(100)]), index= ['Interaction'], columns=list(delta_values.columns.values))
top_row = pd.DataFrame(data=np.array([globalmin]), index= ['Min'], columns=list(delta_values.columns.values))
delta_values = pd.concat([top_row,delta_values.loc[:],bottom_row])
for p in range(len(percentiles)):
total = np.sum(delta_values[str(percentiles[p])])-delta_values.at['Min',str(percentiles[p])]
if total!=0:
for param in param_names:
value = (globalmax[p]-globalmin[p])*delta_values.at[param,str(percentiles[p])]/total
delta_values.set_value(param,str(percentiles[p]),value)
delta_values = delta_values.round(decimals = 2)
delta_values_to_plot = delta_values.values.tolist()
S1_values = pd.read_csv('./S1_scores.csv')
S1_values.set_index(list(S1_values)[0],inplace=True)
S1_values = S1_values.clip(lower=0)
bottom_row = pd.DataFrame(data=np.array([np.zeros(100)]), index= ['Interaction'], columns=list(S1_values.columns.values))
top_row = pd.DataFrame(data=np.array([globalmin]), index= ['Min'], columns=list(S1_values.columns.values))
S1_values = pd.concat([top_row,S1_values.loc[:],bottom_row])
for p in range(len(percentiles)):
total = np.sum(S1_values[str(percentiles[p])])-S1_values.at['Min',str(percentiles[p])]
if total!=0:
diff = 1-total
S1_values.set_value('Interaction',str(percentiles[p]),diff)
for param in param_names+['Interaction']:
value = (globalmax[p]-globalmin[p])*S1_values.at[param,str(percentiles[p])]
S1_values.set_value(param,str(percentiles[p]),value)
S1_values = S1_values.round(decimals = 2)
S1_values_to_plot = S1_values.values.tolist()
R2_values = pd.read_csv('./R2_scores.csv')
R2_values.set_index(list(R2_values)[0],inplace=True)
R2_values = R2_values.clip(lower=0)
bottom_row = pd.DataFrame(data=np.array([np.zeros(100)]), index= ['Interaction'], columns=list(R2_values.columns.values))
top_row = pd.DataFrame(data=np.array([globalmin]), index= ['Min'], columns=list(R2_values.columns.values))
R2_values = pd.concat([top_row,R2_values.loc[:],bottom_row])
for p in range(len(percentiles)):
total = np.sum(R2_values[str(percentiles[p])])-R2_values.at['Min',str(percentiles[p])]
if total!=0:
diff = 1-total
R2_values.set_value('Interaction',str(percentiles[p]),diff)
for param in param_names+['Interaction']:
value = (globalmax[p]-globalmin[p])*R2_values.at[param,str(percentiles[p])]
R2_values.set_value(param,str(percentiles[p]),value)
R2_values = R2_values.round(decimals = 2)
R2_values_to_plot = R2_values.values.tolist()
color_list = ["white", "#F18670", "#E24D3F", "#CF233E", "#681E33", "#676572", "#F3BE22", "#59DEBA", "#14015C", "#DAF8A3", "#0B7A0A", "#F8FFA2", "#578DC0", "#4E4AD8", "#F77632"]
fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(14.5,8))
ax1.stackplot(percentiles, delta_values_to_plot, colors = color_list, labels=parameter_names_long)
l1 = ax1.plot(percentiles, globalmax, color='black', linewidth=2)
l2 = ax1.plot(percentiles, globalmin, color='black', linewidth=2)
ax1.set_title("Delta index")
ax1.set_xlim(0,100)
ax2.stackplot(np.arange(0,100), S1_values_to_plot, colors = color_list, labels=parameter_names_long)
ax2.plot(percentiles, globalmax, color='black', linewidth=2)
ax2.plot(percentiles, globalmin, color='black', linewidth=2)
ax2.set_title("S1")
ax2.set_xlim(0,100)
ax3.stackplot(np.arange(0,100), R2_values_to_plot, colors = color_list, labels=parameter_names_long)
ax3.plot(percentiles, globalmax, color='black', linewidth=2)
ax3.plot(percentiles, globalmin, color='black', linewidth=2)
ax3.set_title("R^2")
ax3.set_xlim(0,100)
handles, labels = ax3.get_legend_handles_labels()
ax1.set_ylabel('Annual shortage (af)', fontsize=12)
ax2.set_xlabel('Shortage magnitude percentile', fontsize=12)
ax1.legend((l1), ('Global ensemble',), fontsize=10, loc='upper left')
fig.legend(handles[1:], labels[1:], fontsize=10, loc='lower center',ncol = 5)
plt.subplots_adjust(bottom=0.2)
fig.savefig('./experiment_sensitivity_curves.png')
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