"""Plot adaptation cost ranges (national results)
"""
import sys
import os
import ast
import matplotlib as mpl
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import cm
from atra.utils import *
mpl.style.use('ggplot')
mpl.rcParams['font.size'] = 10.
mpl.rcParams['font.family'] = 'tahoma'
mpl.rcParams['axes.labelsize'] = 10.
mpl.rcParams['xtick.labelsize'] = 9.
mpl.rcParams['ytick.labelsize'] = 9.
[docs]def adaptation_sentivity_plot(input_dataframe,x_col,y_col,z_col,x_label,y_label,plot_title,plot_path,mode):
fig, ax = plt.subplots(figsize=(8, 4))
hdfpivot = input_dataframe[[x_col,y_col,z_col]].pivot(x_col,y_col)
X = hdfpivot.columns.levels[1].values
Y = hdfpivot.index.values
Z = hdfpivot.values
Xi,Yi = np.meshgrid(X, Y)
plt.contourf(Yi, Xi,Z,10,alpha=0.7, cmap=cm.PuBu)
plt.colorbar()
plt.xlabel(x_label, fontweight='bold')
plt.ylabel(y_label, fontweight='bold')
plt.title(plot_title)
plt.tight_layout()
plt.savefig(plot_path, dpi=500)
plt.close()
[docs]def main():
config = load_config()
output_path = config['paths']['output']
duration_list = np.arange(10,110,10)
growth_rates = np.arange(-2,4,0.2)
modes = ['road','bridge']
modes_id = ['edge_id','bridge_id']
for m in range(len(modes)):
adapt_senstivity = []
for dur in duration_list:
for growth in growth_rates:
filename = 'output_adaptation_{}_{}_days_max_{}_growth_disruption_fixed_parameters.csv'.format(
modes[m], dur,str(round(growth,1)).replace('.','p').replace('-','minus'))
adapt = pd.read_csv(os.path.join(output_path,
'adaptation_results',
'combined_climate',
filename),encoding='utf-8-sig')
if modes[m] == 'road':
adapt_bcr = adapt[[modes_id[m],'max_exposure_length','min_bc_ratio','max_bc_ratio']]
adapt_bcr_min = adapt_bcr.groupby([modes_id[m]])['max_exposure_length','min_bc_ratio'].min().reset_index()
adapt_bcr_max = adapt_bcr.groupby([modes_id[m]])['max_exposure_length','max_bc_ratio'].max().reset_index()
tot_max = len(adapt_bcr_max[adapt_bcr_max['max_bc_ratio'] > 1].index)
tot_max_length = 0.001*adapt_bcr_max[adapt_bcr_max['max_bc_ratio'] > 1]['max_exposure_length'].sum()
tot_max_perc = 100*len(adapt_bcr_max[adapt_bcr_max['max_bc_ratio'] > 1].index)/len(adapt_bcr_max.index)
tot_robust = len(adapt_bcr_min[adapt_bcr_min['min_bc_ratio'] > 1].index)
tot_robust_length = 0.001*adapt_bcr_min[adapt_bcr_min['min_bc_ratio'] > 1]['max_exposure_length'].sum()
tot_robust_perc = 100*len(adapt_bcr_min[adapt_bcr_min['min_bc_ratio'] > 1].index)/len(adapt_bcr_min.index)
adapt_senstivity.append((dur,round(growth,1),
tot_max,tot_max_length,tot_max_perc,
tot_robust,tot_robust_length,tot_robust_perc))
else:
adapt_bcr = adapt[[modes_id[m],'min_bc_ratio','max_bc_ratio']]
adapt_bcr_min = adapt_bcr.groupby(modes_id[m])['min_bc_ratio'].min().reset_index()
adapt_bcr_max = adapt_bcr.groupby(modes_id[m])['max_bc_ratio'].max().reset_index()
tot_max = len(adapt_bcr_max[adapt_bcr_max['max_bc_ratio'] > 1].index)
tot_max_perc = 100*len(adapt_bcr_max[adapt_bcr_max['max_bc_ratio'] > 1].index)/len(adapt_bcr_max.index)
tot_robust = len(adapt_bcr_min[adapt_bcr_min['min_bc_ratio'] > 1].index)
tot_robust_perc = 100*len(adapt_bcr_min[adapt_bcr_min['min_bc_ratio'] > 1].index)/len(adapt_bcr_min.index)
adapt_senstivity.append((dur,round(growth,1),
tot_max,tot_max_perc,
tot_robust,tot_robust_perc))
print ('* Done {} {} days disruption and {} growth'.format(modes[m],dur,round(growth,1)))
if modes[m] == 'road':
adapt_senstivity = pd.DataFrame(adapt_senstivity,
columns=['duration',
'growth',
'max_robust','max_robust_length',
'max_robust_percent',
'tot_robust','tot_robust_length','tot_robust_percent'])
adapt_senstivity.to_csv(os.path.join(output_path,'network_stats','{}_adaptation_sensitvities_lengths.csv'.format(modes[m])),index=False)
adaptation_sentivity_plot(adapt_senstivity,'duration','growth',
'max_robust_length','Maximum duration of disruption (days)',
'Annual GDP growth (%)',
'{} - Kilometers of roads for which Max. BCR > 1'.format(modes[m].title()),
os.path.join(config['paths']['figures'],
'{}-adapt-senstivity-max-robust-length.png'.format(modes[m])),
modes[m])
adaptation_sentivity_plot(adapt_senstivity,'duration','growth',
'tot_robust_length','Maximum duration of disruption (days)',
'Annual GDP growth (%)',
'{} - Kilometers of roads for which Min.- Max. BCR > 1'.format(modes[m].title()),
os.path.join(config['paths']['figures'],
'{}-adapt-senstivity-tot-robust-length.png'.format(modes[m])),
modes[m])
adaptation_sentivity_plot(adapt_senstivity,'duration','growth',
'max_robust','Maximum duration of disruption (days)',
'Annual GDP growth (%)',
'{} - Numbers of roads for which Max. BCR > 1'.format(modes[m].title()),
os.path.join(config['paths']['figures'],
'{}-adapt-senstivity-max-robust-numbers.png'.format(modes[m])),
modes[m])
adaptation_sentivity_plot(adapt_senstivity,'duration','growth',
'tot_robust','Maximum duration of disruption (days)',
'Annual GDP growth (%)',
'{} - Numbers of roads for which Min.- Max. BCR > 1'.format(modes[m].title()),
os.path.join(config['paths']['figures'],
'{}-adapt-senstivity-tot-robust-numbers.png'.format(modes[m])),
modes[m])
else:
adapt_senstivity = pd.DataFrame(adapt_senstivity,
columns=['duration',
'growth',
'max_robust',
'max_robust_percent',
'tot_robust',
'tot_robust_percent'])
adapt_senstivity.to_csv(os.path.join(output_path,
'network_stats',
'{}_adaptation_sensitvities.csv'.format(modes[m])),index=False)
adaptation_sentivity_plot(adapt_senstivity,'duration','growth',
'max_robust','Maximum duration of disruption (days)',
'Annual GDP growth (%)',
'{} - Numbers of assets with Max. BCR > 1'.format(modes[m].title()),
os.path.join(config['paths']['figures'],
'{}-adapt-senstivity-max-robust-numbers.png'.format(modes[m])),
modes[m])
adaptation_sentivity_plot(adapt_senstivity,'duration','growth',
'tot_robust','Maximum duration of disruption (days)',
'Annual GDP growth (%)',
'{} - Numbers of assets with Min.- Max. BCR > 1'.format(modes[m].title()),
os.path.join(config['paths']['figures'],
'{}-adapt-senstivity-tot-robust-numbers.png'.format(modes[m])),
modes[m])
if __name__ == '__main__':
main()