"""Road network risks and adaptation maps
"""
import os
import sys
from collections import OrderedDict
import ast
import numpy as np
import geopandas as gpd
import pandas as pd
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import matplotlib.pyplot as plt
from shapely.geometry import LineString
from atra.utils import *
[docs]def main():
config = load_config()
data_path = config['paths']['data']
calc_path = config['paths']['calc']
output_path = config['paths']['output']
# Supply input data and parameters
modes = ['road', 'rail','bridge']
mode_files = ['road_edges','rail_edges','bridges']
modes_id_cols = ['edge_id','edge_id','bridge_id']
hazard_cols = ['climate_scenario','year']
# Give the paths to the input data files
network_data_csv = os.path.join(data_path, 'network')
risk_csv_dir = os.path.join(output_path, 'risk_results')
if os.path.exists(risk_csv_dir) == False:
os.mkdir(risk_csv_dir)
# load cost file
print ('* Get adaptation costs')
adapt = pd.read_excel(os.path.join(data_path,'adaptation_costs','ROCKS - Database - ARNG (Version 2.3) Feb2018.xls'),
sheet_name = 'Resultados Consolidados',
skiprows=6,
nrows=9,
usecols = [2,4,5],
encoding='utf-8-sig').fillna('No value')
adapt.columns = ['option','cost_perkm','climate_uplift_perkm']
adapt = adapt[~adapt.option.isin(['Subtotal','No value'])]
cost_rehab = adapt.loc[adapt['option']=='Reconstruction','cost_perkm'].values[0] + \
adapt.loc[adapt['option']=='Reconstruction','climate_uplift_perkm'].values[0]
del adapt
# Process national scale results
for m in range(len(modes)):
risk_cols = ['min_eael_per_day','max_eael_per_day']
output_cols = ['hazard_type','climate_scenario','year',
'min_flood_depth','max_flood_depth',
'min_exposure_length','min_exposure_percent',
'max_exposure_length','max_exposure_percent',
'min_eael_per_day','max_eael_per_day']
# Load mode network DataFrame
print('* Creating {} network risk results'.format(modes[m]))
fail_results = pd.read_csv(os.path.join(output_path,
'failure_results',
'minmax_combined_scenarios',
'single_edge_failures_minmax_{}_100_percent_disrupt.csv'.format(modes[m])))
risk_results = pd.read_csv(os.path.join(output_path,
'risk_results',
'{}_hazard_intersections_risk_weights.csv'.format(modes[m])))
risk_results = pd.merge(risk_results,
fail_results[[modes_id_cols[m],'min_econ_impact','max_econ_impact']],
how='left',
on=[modes_id_cols[m]]).fillna(0)
del fail_results
risk_results['min_eael_per_day'] = risk_results['risk_wt']*risk_results['min_econ_impact']
risk_results['max_eael_per_day'] = risk_results['risk_wt']*risk_results['max_econ_impact']
print('* Merging with {} network DataFrame'.format(modes[m]))
if modes[m] in ('road','bridge'):
risk_cols += ['ead']
output_cols = [modes_id_cols[m]] + output_cols + ['ead']
width_file = pd.read_csv(os.path.join(network_data_csv,
'{}.csv'.format(mode_files[m])), encoding='utf-8-sig')
risk_results = pd.merge(risk_results,
width_file[[modes_id_cols[m],'width']],
how='left',
on=[modes_id_cols[m]])
del width_file
if modes[m] == 'road':
risk_results['ead'] = 1.0e3*cost_rehab*risk_results['dam_wt']*risk_results['width']/7.3
else:
risk_results['ead'] = 1.0e6*cost_rehab*risk_results['risk_wt']*risk_results['width']/7.3
risk_results[output_cols].to_csv(os.path.join(risk_csv_dir,
'{}_hazard_and_climate_risks.csv'.format(modes[m])), index=False)
print('* Creating {} network risk results for climate outlooks'.format(modes[m]))
min_height = risk_results.groupby([modes_id_cols[m]] + hazard_cols)['min_flood_depth'].min().reset_index()
max_height = risk_results.groupby([modes_id_cols[m]] + hazard_cols)['max_flood_depth'].max().reset_index()
heights = pd.merge(min_height,max_height,how='left',on=[modes_id_cols[m]]+hazard_cols).fillna(0)
del min_height,max_height
min_exposures = risk_results.groupby([modes_id_cols[m]] + hazard_cols)['min_exposure_length',
'min_exposure_percent'].min().reset_index()
max_exposures = risk_results.groupby([modes_id_cols[m]] + hazard_cols)['max_exposure_length',
'max_exposure_percent'].max().reset_index()
exposures = pd.merge(min_exposures,max_exposures,how='left',on=[modes_id_cols[m]]+hazard_cols).fillna(0)
del min_exposures,max_exposures
height_exposures = pd.merge(heights,exposures,how='left',on=[modes_id_cols[m]]+hazard_cols).fillna(0)
del heights, exposures
# all_edge_fail_scenarios = risk_results[hazard_cols + [modes_id_cols[m]] + risk_cols]
all_edge_fail_scenarios = risk_results.groupby([modes_id_cols[m]] + hazard_cols)[risk_cols].sum().reset_index()
pd.merge(height_exposures,
all_edge_fail_scenarios,
how='left',
on=[modes_id_cols[m]]+hazard_cols).fillna(0).to_csv(os.path.join(risk_csv_dir,
'{}_combined_climate_risks.csv'.format(modes[m])), index=False)
del risk_results, height_exposures, all_edge_fail_scenarios
if __name__ == '__main__':
main()