"""rail 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']
output_path = config['paths']['output']
hazard_cols = ['climate_scenario','year']
duration = [10,30]
change_colors = ['#1a9850','#66bd63','#a6d96a','#d9ef8b','#fee08b','#fdae61','#f46d43','#d73027','#969696']
change_labels = ['< -100','-100 to -50','-50 to -10','-10 to 0','0 to 10','10 to 50','50 to 100',' > 100','No change/value']
change_ranges = [(-1e10,-100),(-100,-50),(-50,-10),(-10,0),(0.001,10),(10,50),(50,100),(100,1e10)]
eael_set = [
{
'column': 'min_eael',
'title': 'Min EAEL',
'legend_label': "EAEL (million USD)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'max_eael',
'title': 'Max EAEL',
'legend_label': "EAEL (million USD)",
'divisor': 1000000,
'significance': 0
}
]
data_path = config['paths']['data']
region_file_path = os.path.join(config['paths']['data'], 'network',
'rail_edges.shp')
flow_file_path = os.path.join(config['paths']['output'], 'flow_mapping_combined',
'weighted_flows_rail_100_percent.csv')
region_file = gpd.read_file(region_file_path,encoding='utf-8')
flow_file = pd.read_csv(flow_file_path)
region_file = pd.merge(region_file,flow_file,how='left', on=['edge_id']).fillna(0)
region_file = region_file[region_file['max_total_tons'] > 0]
del flow_file
for dur in duration:
fail_scenarios = pd.read_csv(os.path.join(output_path,
'risk_results',
'rail_combined_climate_risks.csv'))
fail_scenarios['min_eael'] = dur*fail_scenarios['min_eael_per_day']
fail_scenarios['max_eael'] = dur*fail_scenarios['max_eael_per_day']
all_edge_fail_scenarios = fail_scenarios
# Climate change effects
all_edge_fail_scenarios = all_edge_fail_scenarios.set_index(['edge_id'])
scenarios = list(set(all_edge_fail_scenarios.index.values.tolist()))
change_tup = []
for sc in scenarios:
eael = all_edge_fail_scenarios.loc[[sc], 'max_eael'].values.tolist()
yrs = all_edge_fail_scenarios.loc[[sc], 'year'].values.tolist()
cl = all_edge_fail_scenarios.loc[[sc], 'climate_scenario'].values.tolist()
if 2016 not in yrs:
for e in range(len(eael)):
if eael[e] > 0:
# change_tup += list(zip([sc[0]]*len(cl),[sc[1]]*len(cl),cl,yrs,[0]*len(cl),eael,[1e9]*len(cl)))
change_tup += [(sc,cl[e],yrs[e],0,eael[e],1e9)]
elif len(yrs) > 1:
vals = list(zip(cl,eael,yrs))
vals = sorted(vals, key=lambda pair: pair[-1])
change = 100.0*(np.array([p for (c,p,y) in vals[1:]]) - vals[0][1])/vals[0][1]
cl = [c for (c,p,y) in vals[1:]]
yrs = [y for (c,p,y) in vals[1:]]
fut = [p for (c,p,y) in vals[1:]]
change_tup += list(zip([sc]*len(cl),cl,yrs,[vals[0][1]]*len(cl),fut,change))
change_df = pd.DataFrame(change_tup,columns=['edge_id','climate_scenario','year','current','future','change']).fillna('inf')
change_df = change_df[change_df['change'] != 'inf']
change_df.to_csv(os.path.join(config['paths']['output'],
'network_stats',
'national_rail_{}_days_eael_climate_change_combined.csv'.format(dur)
), index=False
)
# Change effects
change_df = change_df.set_index(hazard_cols)
scenarios = list(set(change_df.index.values.tolist()))
for sc in scenarios:
climate_scenario = sc[0]
year = sc[1]
percentage = change_df.loc[[sc], 'change'].values.tolist()
edges = change_df.loc[[sc], 'edge_id'].values.tolist()
edges_df = pd.DataFrame(list(zip(edges,percentage)),columns=['edge_id','change'])
edges_vals = pd.merge(region_file,edges_df,how='left',on=['edge_id']).fillna(0)
del percentage,edges,edges_df
proj_lat_lon = ccrs.PlateCarree()
ax = get_axes()
plot_basemap(ax, data_path)
scale_bar(ax, location=(0.8, 0.05))
plot_basemap_labels(ax, data_path, include_regions=True)
# name = [c['name'] for c in hazard_set if c['hazard'] == hazard_type][0]
for record in edges_vals.itertuples():
geom = record.geometry
region_val = record.change
if region_val:
cl = [c for c in range(len((change_ranges))) if region_val >= change_ranges[c][0] and region_val < change_ranges[c][1]]
if cl:
c = cl[0]
ax.add_geometries([geom],crs=proj_lat_lon,linewidth=2.0,edgecolor=change_colors[c],facecolor='none',zorder=8)
# ax.add_geometries([geom.buffer(0.1)],crs=proj_lat_lon,linewidth=0,facecolor=change_colors[c],edgecolor='none',zorder=8)
else:
ax.add_geometries([geom], crs=proj_lat_lon, linewidth=1.5,edgecolor=change_colors[-1],facecolor='none',zorder=7)
# ax.add_geometries([geom.buffer(0.1)], crs=proj_lat_lon, linewidth=0,facecolor=change_colors[-1],edgecolor='none',zorder=7)
# Legend
legend_handles = []
for c in range(len(change_colors)):
legend_handles.append(mpatches.Patch(color=change_colors[c], label=change_labels[c]))
ax.legend(
handles=legend_handles,
title='Percentage change in EAEL',
loc=(0.55,0.2),
fancybox=True,
framealpha=1.0
)
if climate_scenario == 'none':
climate_scenario = 'current'
else:
climate_scenario = climate_scenario.upper()
title = 'Percentage change in EAEL for {} {}'.format(climate_scenario.replace('_',' ').title(),year)
print(" * Plotting {}".format(title))
plt.title(title, fontsize=10)
output_file = os.path.join(config['paths']['figures'],
'national-rail-{}-{}-{}-days-risks-change-percentage.png'.format(climate_scenario.replace('-',' ').title(),
year,dur))
save_fig(output_file)
plt.close()
# Absolute effects
all_edge_fail_scenarios = all_edge_fail_scenarios.reset_index()
all_edge_fail_scenarios = all_edge_fail_scenarios.set_index(hazard_cols)
scenarios = list(set(all_edge_fail_scenarios.index.values.tolist()))
for sc in scenarios:
climate_scenario = sc[0]
if climate_scenario == 'none':
climate_scenario = 'baseline'
else:
climate_scenario = climate_scenario.upper()
year = sc[1]
min_eael = all_edge_fail_scenarios.loc[[sc], 'min_eael'].values.tolist()
max_eael = all_edge_fail_scenarios.loc[[sc], 'max_eael'].values.tolist()
edges = all_edge_fail_scenarios.loc[[sc], 'edge_id'].values.tolist()
edges_df = pd.DataFrame(list(zip(edges,min_eael,max_eael)),columns=['edge_id','min_eael','max_eael'])
edges_vals = pd.merge(region_file,edges_df,how='left',on=['edge_id']).fillna(0)
del edges_df
for c in range(len(eael_set)):
proj_lat_lon = ccrs.PlateCarree()
ax = get_axes()
plot_basemap(ax, data_path)
scale_bar(ax, location=(0.8, 0.05))
plot_basemap_labels(ax, data_path, include_regions=True)
# generate weight bins
column = eael_set[c]['column']
weights = [record[column] for iter_, record in edges_vals.iterrows()]
max_weight = max(weights)
width_by_range = generate_weight_bins(weights, width_step=0.04, n_steps=5)
rail_geoms_by_category = {
'1': [],
'2': []
}
for iter_,record in edges_vals.iterrows():
geom = record.geometry
val = record[column]
if val == 0:
cat = '2'
else:
cat = '1'
buffered_geom = None
for (nmin, nmax), width in width_by_range.items():
if nmin <= val and val < nmax:
buffered_geom = geom.buffer(width)
if buffered_geom is not None:
rail_geoms_by_category[cat].append(buffered_geom)
else:
print("Feature was outside range to plot", iter_)
styles = OrderedDict([
('1', Style(color='#006d2c', zindex=9, label='Hazard failure effect')), # green
('2', Style(color='#969696', zindex=7, label='No hazard exposure/effect'))
])
for cat, geoms in rail_geoms_by_category.items():
cat_style = styles[cat]
ax.add_geometries(
geoms,
crs=proj_lat_lon,
linewidth=0,
facecolor=cat_style.color,
edgecolor='none',
zorder=cat_style.zindex
)
# name = [h['name'] for h in hazard_set if h['hazard'] == hazard_type][0]
x_l = -62.4
x_r = x_l + 0.4
base_y = -42.1
y_step = 0.8
y_text_nudge = 0.2
x_text_nudge = 0.2
ax.text(
x_l,
base_y + y_step - y_text_nudge,
eael_set[c]['legend_label'],
horizontalalignment='left',
transform=proj_lat_lon,
size=10)
divisor = eael_set[c]['divisor']
significance_ndigits = eael_set[c]['significance']
max_sig = []
for (i, ((nmin, nmax), line_style)) in enumerate(width_by_range.items()):
if round(nmin/divisor, significance_ndigits) < round(nmax/divisor, significance_ndigits):
max_sig.append(significance_ndigits)
elif round(nmin/divisor, significance_ndigits+1) < round(nmax/divisor, significance_ndigits+1):
max_sig.append(significance_ndigits+1)
elif round(nmin/divisor, significance_ndigits+2) < round(nmax/divisor, significance_ndigits+2):
max_sig.append(significance_ndigits+2)
else:
max_sig.append(significance_ndigits+3)
significance_ndigits = max(max_sig)
for (i, ((nmin, nmax), width)) in enumerate(width_by_range.items()):
y = base_y - (i*y_step)
line = LineString([(x_l, y), (x_r, y)]).buffer(width)
ax.add_geometries(
[line],
crs=proj_lat_lon,
linewidth=0,
edgecolor='#000000',
facecolor='#000000',
zorder=2)
if nmin == max_weight:
value_template = '>{:.' + str(significance_ndigits) + 'f}'
label = value_template.format(
round(max_weight/divisor, significance_ndigits))
else:
value_template = '{:.' + str(significance_ndigits) + \
'f}-{:.' + str(significance_ndigits) + 'f}'
label = value_template.format(
round(nmin/divisor, significance_ndigits), round(nmax/divisor, significance_ndigits))
ax.text(
x_r + x_text_nudge,
y - y_text_nudge,
label,
horizontalalignment='left',
transform=proj_lat_lon,
size=10)
if climate_scenario == 'none':
climate_scenario = 'Baseline'
climate_scenario = climate_scenario.replace('_',' ')
title = 'Railways ({}) {} {}'.format(eael_set[c]['title'],climate_scenario.title(),year)
print ('* Plotting ',title)
plt.title(title, fontsize=12)
legend_from_style_spec(ax, styles,loc='lower left')
# output
output_file = os.path.join(
config['paths']['figures'], 'national-rail-{}-{}-{}-{}-days.png'.format(climate_scenario.replace('.',''),
year,eael_set[c]['column'],dur))
save_fig(output_file)
plt.close()
if __name__ == '__main__':
main()