"""Road network risks and adaptation maps
"""
import os
import sys
from collections import defaultdict
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 plot_many_ranges(input_dfs, division_factor,x_label, y_label,plot_title,plot_color,plot_labels,plot_file_path):
fig, ax = plt.subplots(figsize=(8, 4))
length = []
for i in range(len(input_dfs)):
input_data = input_dfs[i]
vals_min_max = []
for a, b in input_data.itertuples(index=False):
if a < b:
min_, max_ = a, b
else:
min_, max_ = b, a
vals_min_max.append((min_, max_))
# vals_min_max.sort(key=lambda el: el[1])
vals_min_max = list(zip(*vals_min_max))
percentlies = 100.0*np.arange(0,len(vals_min_max[0]))/len(vals_min_max[0])
length.append(len(vals_min_max[0]))
ax.plot(percentlies,
1.0*np.array(vals_min_max[0])/division_factor,
linewidth=0.5,
color=plot_color[i]
)
ax.plot(percentlies,
1.0*np.array(vals_min_max[1])/division_factor,
linewidth=0.5,
color=plot_color[i]
)
ax.fill_between(percentlies,
1.0*np.array(vals_min_max[0])/division_factor,
1.0*np.array(vals_min_max[1])/division_factor,
alpha=0.5,
edgecolor=None,
facecolor=plot_color[i],
label = plot_labels[i]
)
length = max(length)
if 'BCR' in y_label:
ax.plot(np.arange(0,100),
np.array([1]*100),
linewidth=0.5,
color='red',
label = 'BCR = 1'
)
# ax.set_xscale('log')
ax.set_yscale('log')
# ax.tick_params(axis='x', rotation=45)
ax.legend(loc='upper left')
plt.xlabel(x_label, fontweight='bold')
plt.ylabel(y_label, fontweight='bold')
plt.title(plot_title)
plt.tight_layout()
plt.savefig(plot_file_path, dpi=500)
plt.close()
[docs]def main():
config = load_config()
output_path = config['paths']['output']
durations = [10,20,30]
value_thr =100000
change_colors = ['#1a9850','#66bd63','#a6d96a','#d9ef8b','#969696','#fee08b','#fdae61','#f46d43','#d73027']
change_labels = ['< -100','-100 to -50','-50 to -10','-10 to 0','0','0 to 10','10 to 50','50 to 100',' > 100']
change_ranges = [(-1e10,-100),(-100,-50),(-50,-10),(-10,0),(0,1e-7),(1e-7,10),(10,50),(50,100),(100,1e10)]
modes = ['road','bridge']
duration_colors = ['#f03b20','#6baed6','#3182bd','#08519c']
duration_labels = ['EAD'] + ['EAEL for max. {} days disruption events'.format(d) for d in durations]
for m in range(len(modes)):
# change_df = change_df[change_df['future'] != -1]
risk_df = pd.read_csv(os.path.join(output_path,
'risk_results',
'{}_combined_climate_risks.csv'.format(modes[m])
)
)
risk_df = risk_df[risk_df['climate_scenario'] == 'Baseline']
risk_df = risk_df[(risk_df['max_eael_per_day'] + risk_df['ead']) > value_thr]
risk_df['zeroes'] = [0]*len(risk_df.index)
# risk_ranges = [risk_df['zeroes'].values.tolist(),risk_df['ead'].values.tolist()]
risk_cols = ['zeroes','ead']
fig, ax = plt.subplots(figsize=(8, 4))
for d in range(len(durations)):
risk_df['max_risk_{}_days'.format(durations[d])] = durations[d]*risk_df['max_eael_per_day'] + risk_df['ead']
risk_cols.append('max_risk_{}_days'.format(durations[d]))
# risk_ranges.append(risk_df['max_risk_{}_days'.format(durations[d])].values.tolist())
# if d == 0:
# risk_ranges.append(risk_df[['ead','max_risk_{}_days'.format(durations[d])]])
# else:
# risk_ranges.append(risk_df[['max_risk_{}_days'.format(durations[d]),'max_risk_{}_days'.format(durations[d-1])]])
risk_df = risk_df[risk_cols].sort_values(['max_risk_{}_days'.format(durations[-1])], ascending=True)
risk_ranges = []
for c in range(len(risk_cols)-1):
risk_ranges.append(risk_df[[risk_cols[c],risk_cols[c+1]]])
plot_many_ranges(risk_ranges,
1e6,
'Percentile rank (%)',
'EAD and EAEL (million US$)',
'{} - EAD and EAEL ranges for Total Risks > {:,} US$'.format(modes[m].title(),value_thr),
duration_colors,
duration_labels,
os.path.join(config['paths']['figures'],
'{}-changing-risks-with-days.png'.format(modes[m])))
if __name__ == '__main__':
main()