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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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