"""Provincial 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 vtra.utils import *
[docs]def main():
config = load_config()
regions = ['Lao Cai', 'Binh Dinh', 'Thanh Hoa']
hazard_cols = ['hazard_type','climate_scenario','year']
duration = 10
hazard_set = [
{
'hazard': 'landslide',
'name': 'Landslide'
},
{
'hazard': 'flashflood',
'name':'Flashflood'
},
{
'hazard': 'flooding',
'name': 'Fluvial flooding'
},
{
'hazard': 'typhoon flooding',
'name': 'Typhoon flooding'
}
]
change_colors = ['#1a9850','#66bd63','#a6d96a','#d9ef8b','#fee08b','#fdae61','#f46d43','#d73027','#969696']
change_labels = ['< -40','-40 to -20','-20 to -10','-10 to 0','0 to 10','10 to 20','20 to 40',' > 40','No change/value']
change_ranges = [(-1e10,-40),(-40,-20),(-20,-10),(-10,0),(0.001,10),(10,20),(20,40),(40,1e10)]
eael_set = [
{
'column': 'min_eael',
'title': 'Min EAEL',
'legend_label': "Expected Annual losses ('000 USD)",
'divisor': 1000,
'significance': 0
},
{
'column': 'max_eael',
'title': 'Max EAEL',
'legend_label': "Expected Annual losses ('000 USD)",
'divisor': 1000,
'significance': 0
}
]
adapt_set = [
{
'column': 'min_eael',
'title': 'Min EAEL',
'legend_label': "Expected Annual losses ('000 USD)",
'divisor': 1000,
'significance': 0
},
{
'column': 'max_eael',
'title': 'Max EAEL',
'legend_label': "Expected Annual losses ('000 USD)",
'divisor': 1000,
'significance': 0
},
{
'column': 'min_ini_adap_cost',
'title': 'Min Initial Investment',
'legend_label': "Initial investment (USD million)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'max_ini_adap_cost',
'title': 'Max Initial Investment',
'legend_label': "Initial investment (USD million)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'min_benefit',
'title': 'Min Benefit over time',
'legend_label': "Benefit (USD million)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'max_benefit',
'title': 'Max Benefit over time',
'legend_label': "Benefit (USD million)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'min_tot_adap_cost',
'title': 'Min Investment over time',
'legend_label': "Total Investment (USD million)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'max_tot_adap_cost',
'title': 'Max Investment over time',
'legend_label': "Total Investment (USD million)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'min_bc_ratio',
'title': 'Min BCR of adaptation over time',
'legend_label': "BCR",
'divisor': 1,
'significance': 0
},
{
'column': 'max_bc_ratio',
'title': 'Max BCR of adaptation over time',
'legend_label': "BCR",
'divisor': 1,
'significance': 0
}
]
adapt_set = [
{
'column': 'min_ini_adap_cost_perkm',
'title': 'Min Initial Investment per km',
'legend_label': "Initial Investment (USD million/km)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'max_ini_adap_cost_perkm',
'title': 'Max Initial Investment per km',
'legend_label': "Initial Investment (USD million/km)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'min_tot_adap_cost_perkm',
'title': 'Min Investment per km over time',
'legend_label': "Total Investment (USD million/km)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'max_tot_adap_cost_perkm',
'title': 'Max Investment per km over time',
'legend_label': "Total Investment (USD million/km)",
'divisor': 1000000,
'significance': 0
},
]
adapt_cols = ['min_benefit','min_ini_adap_cost','min_ini_adap_cost_perkm','min_tot_adap_cost','min_tot_adap_cost_perkm','min_bc_ratio',\
'max_benefit','max_ini_adap_cost','max_ini_adap_cost_perkm','max_tot_adap_cost','max_tot_adap_cost_perkm','max_bc_ratio']
for region in regions:
region_file_path = os.path.join(config['paths']['data'], 'post_processed_networks',
'{}_roads_edges.shp'.format(region.lower().replace(' ', '')))
flow_file_path = os.path.join(config['paths']['output'], 'failure_results','minmax_combined_scenarios',
'single_edge_failures_minmax_{}_5_tons_100_percent_disrupt.csv'.format(region.lower().replace(' ', '')))
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)
del flow_file
flow_file_path = os.path.join(config['paths']['output'], 'adaptation_results',
'output_adaptation_{}_10_days_max_disruption_fixed_parameters.csv'.format(region.lower().replace(' ', '')))
fail_scenarios = pd.read_csv(flow_file_path)
fail_scenarios['min_eael'] = duration*fail_scenarios['min_duration_wt']*fail_scenarios['risk_wt']*fail_scenarios['min_econ_impact']
fail_scenarios['max_eael'] = duration*fail_scenarios['max_duration_wt']*fail_scenarios['risk_wt']*fail_scenarios['max_econ_impact']
all_edge_fail_scenarios = fail_scenarios[hazard_cols + ['edge_id','min_eael','max_eael']]
all_edge_fail_scenarios = all_edge_fail_scenarios.groupby(hazard_cols + ['edge_id'])['min_eael','max_eael'].max().reset_index()
# Climate change effects
all_edge_fail_scenarios = all_edge_fail_scenarios.set_index(['hazard_type','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:
change_tup += list(zip([sc[0]]*len(cl),[sc[1]]*len(cl),cl,yrs,[0]*len(cl),eael,[1e9]*len(cl)))
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[0]]*len(cl),[sc[1]]*len(cl),cl,yrs,[vals[0][1]]*len(cl),fut,change))
change_df = pd.DataFrame(change_tup,columns=['hazard_type','edge_id','climate_scenario','year','current','future','change'])
change_df.to_csv(os.path.join(config['paths']['output'],
'network_stats',
'{}_eael_climate_change.csv'.format(region.replace(' ','').lower())
), index=False
)
# # Change effects
# change_df = change_df.set_index(hazard_cols)
# scenarios = list(set(change_df.index.values.tolist()))
# for sc in scenarios:
# hazard_type = sc[0]
# climate_scenario = sc[1]
# year = sc[2]
# 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
# plot_settings = get_region_plot_settings(region)
# ax = get_axes(plot_settings['bbox'], figsize=plot_settings['figure_size'])
# if region == 'Binh Dinh':
# plot_basemap(ax, config['paths']['data'], country_border='none',
# plot_states=False, plot_districts=True, highlight_region=region)
# else:
# plot_basemap(ax, config['paths']['data'], country_border='none',
# plot_states=True, plot_districts=True, highlight_region=region)
# scale_bar(ax, location=(0.8, 0.05), length=plot_settings['scale_legend'])
# proj = ccrs.PlateCarree()
# name = [c['name'] for c in hazard_set if c['hazard'] == hazard_type][0]
# for iter_,record in edges_vals.iterrows():
# 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,linewidth=1,edgecolor=change_colors[c],facecolor='none',zorder=2)
# else:
# ax.add_geometries([geom], crs=proj, linewidth=1,edgecolor=change_colors[-1],facecolor='none',zorder=1)
# # 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='lower left'
# )
# if climate_scenario == 'none':
# climate_scenario = 'current'
# else:
# climate_scenario = climate_scenario.upper()
# title = 'Percentage change in EAEL for {} {} {}'.format(name,climate_scenario,year)
# print(" * Plotting {} {}".format(title,region))
# # district labels
# plot_district_labels(ax, config['paths']['data'], highlight_region=region)
# plt.title(title, fontsize=14)
# output_file = os.path.join(config['paths']['figures'],
# '{}-{}-{}-{}-risks-change-percentage.png'.format(region.replace(' ',''),name,climate_scenario.replace('.',''),year))
# 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:
# hazard_type = sc[0]
# climate_scenario = sc[1]
# if climate_scenario == 'none':
# climate_scenario = 'current'
# else:
# climate_scenario = climate_scenario.upper()
# year = sc[2]
# 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)):
# plot_settings = get_region_plot_settings(region)
# ax = get_axes(plot_settings['bbox'], figsize=plot_settings['figure_size'])
# if region == 'Binh Dinh':
# plot_basemap(ax, config['paths']['data'], country_border='none',
# plot_states=False, plot_districts=True, highlight_region=region)
# else:
# plot_basemap(ax, config['paths']['data'], country_border='none',
# plot_states=True, plot_districts=True, highlight_region=region)
# scale_bar(ax, location=(0.8, 0.05), length=plot_settings['scale_legend'])
# proj_lat_lon = ccrs.PlateCarree()
# # generate weight bins
# column = eael_set[c]['column']
# weights = [record[column] for iter_, record in edges_vals.iterrows()]
# max_weight = max(weights)
# if column in ('min_bc_rat', 'max_bc_rat') and region == 'Lao Cai':
# width_by_range = generate_weight_bins_with_colour_gradient(
# weights, n_steps=6, width_step=0.001)
# else:
# width_by_range = generate_weight_bins_with_colour_gradient(
# weights, width_step=0.001)
# road_geoms_by_category = {
# region: []
# }
# styles = OrderedDict([
# ('1', Style(color='#252525', zindex=6, label='Hazard failure: No access')), # black
# ('2', Style(color='#f1605d', zindex=8, label='Hazard failure: Reroutiing possible')), # orange
# ('3', Style(color='#969696', zindex=7, label='No hazard exposure/effect')) # grey
# ])
# name = [h['name'] for h in hazard_set if h['hazard'] == hazard_type][0]
# for iter_, record in edges_vals.iterrows():
# cat = region
# geom = record.geometry
# noaccess = record.no_access
# val = record[column]
# buffered_geom = None
# for (nmin, nmax), line_style in width_by_range.items():
# if nmin <= val and val < nmax:
# buffered_geom = geom.buffer(line_style[1])
# if buffered_geom is not None:
# if val == 0 and noaccess == 0:
# ax.add_geometries(
# [buffered_geom],
# crs=proj_lat_lon,
# linewidth=0,
# facecolor='#969696',
# edgecolor='none',
# zorder=2 + line_style[0]
# )
# elif noaccess == 0:
# ax.add_geometries(
# [buffered_geom],
# crs=proj_lat_lon,
# linewidth=0,
# facecolor=str(line_style[2]),
# edgecolor='none',
# zorder=3 + line_style[0]
# )
# else:
# ax.add_geometries(
# [buffered_geom],
# crs=proj_lat_lon,
# linewidth=0,
# facecolor='#252525',
# edgecolor='none',
# zorder=4 + line_style[0]
# )
# else:
# print("Feature was outside range to plot", iter_)
# x_l = plot_settings['weight_legend']['x_l']
# x_r = plot_settings['weight_legend']['x_r']
# base_y = plot_settings['weight_legend']['base_y']
# y_step = plot_settings['weight_legend']['y_step']
# y_text_nudge = plot_settings['weight_legend']['y_text_nudge']
# x_text_nudge = plot_settings['weight_legend']['x_text_nudge']
# # text above weight legend
# ax.text(
# x_l,
# base_y + y_step - y_text_nudge,
# eael_set[c]['legend_label'],
# horizontalalignment='left',
# transform=proj_lat_lon,
# size=10)
# # weight legend
# divisor = eael_set[c]['divisor']
# # print (width_by_range)
# 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), line_style)) in enumerate(width_by_range.items()):
# y = base_y - (i*y_step)
# line = LineString([(x_l, y), (x_r, y)]).buffer(line_style[1])
# ax.add_geometries(
# [line],
# crs=proj_lat_lon,
# linewidth=0,
# edgecolor='#969696',
# facecolor='#969696',
# 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)
# # district labels
# plot_district_labels(ax, config['paths']['data'], highlight_region=region)
# # plot
# title = '{} ({}) {} {} {}'.format(region, eael_set[c]['title'],name,climate_scenario,year)
# print(" * Plotting", title)
# plt.title(title, fontsize=14)
# legend_from_style_spec(ax, styles,loc='upper right')
# # output
# output_file = os.path.join(
# config['paths']['figures'], '{}-roads-{}-{}-{}-{}.png'.format(region.lower().replace(' ', ''), name,climate_scenario.replace('.',''),year,eael_set[c]['column']))
# save_fig(output_file)
# plt.close()
# all_edge_fail_scenarios = fail_scenarios[['edge_id','asset_type','min_exposure_length','max_exposure_length','road_length','min_eael','max_eael',\
# 'min_ini_adap_cost','max_ini_adap_cost','min_tot_adap_cost',\
# 'max_tot_adap_cost','min_benefit','max_benefit','min_bc_ratio','max_bc_ratio']]
# for cols in ['min_ini_adap_cost','max_ini_adap_cost']:
# all_edge_fail_scenarios[cols] = all_edge_fail_scenarios[cols].apply(lambda x: np.max(np.array(ast.literal_eval(x))))
# all_edge_fail_scenarios['min_ini_adap_cost_perkm'] = 1000*all_edge_fail_scenarios['min_ini_adap_cost']/all_edge_fail_scenarios['road_length']
# all_edge_fail_scenarios['max_ini_adap_cost_perkm'] = 1000*all_edge_fail_scenarios['max_ini_adap_cost']/all_edge_fail_scenarios['road_length']
# all_edge_fail_scenarios['min_tot_adap_cost_perkm'] = 1000*all_edge_fail_scenarios['min_tot_adap_cost']/all_edge_fail_scenarios['road_length']
# all_edge_fail_scenarios['max_tot_adap_cost_perkm'] = 1000*all_edge_fail_scenarios['max_tot_adap_cost']/all_edge_fail_scenarios['road_length']
# all_edge_fail_scenarios = all_edge_fail_scenarios.groupby(['edge_id','asset_type','min_exposure_length','max_exposure_length','road_length'])[adapt_cols + ['min_eael','max_eael']].max().reset_index()
# asset_locations = pd.read_excel(os.path.join(config['paths']['output'],
# 'hazard_scenarios','province_roads_hazard_intersections.xlsx'),sheet_name=region.replace(' ','').lower())
# asset_locations = asset_locations[['edge_id','commune_name','district_name']]
# asset_locations = asset_locations.drop_duplicates(subset=['edge_id','commune_name','district_name'], keep='first')
# all_edge_fail_scenarios = pd.merge(all_edge_fail_scenarios,asset_locations,how='left',on=['edge_id'])
# all_edge_fail_scenarios = all_edge_fail_scenarios.drop_duplicates(subset=['edge_id','commune_name','district_name'], keep='first')
# all_edge_fail_scenarios = all_edge_fail_scenarios[['edge_id','commune_name','district_name','min_exposure_length',\
# 'max_exposure_length','road_length','min_eael','max_eael',\
# 'min_ini_adap_cost','max_ini_adap_cost','min_tot_adap_cost',\
# 'min_ini_adap_cost_perkm','max_ini_adap_cost_perkm','min_tot_adap_cost_perkm','max_tot_adap_cost_perkm',\
# 'max_tot_adap_cost','min_benefit','max_benefit','min_bc_ratio','max_bc_ratio']]
# edges_vals = pd.merge(region_file,all_edge_fail_scenarios,how='left',on=['edge_id']).fillna(0)
# edges_vals = edges_vals.drop_duplicates(subset=['edge_id'], keep='first')
# edge_summary = edges_vals[['edge_id','asset_type','commune_name','district_name','min_exposure_length',\
# 'max_exposure_length','road_length','min_eael','max_eael',\
# 'min_ini_adap_cost','max_ini_adap_cost','min_tot_adap_cost',\
# 'min_ini_adap_cost_perkm','max_ini_adap_cost_perkm','min_tot_adap_cost_perkm','max_tot_adap_cost_perkm',\
# 'max_tot_adap_cost','min_benefit','max_benefit','min_bc_ratio','max_bc_ratio','no_access']]
# edge_summary.to_csv(os.path.join(config['paths']['output'],
# 'network_stats',
# '{}_adapt_summary_fixed_parameter.csv'.format(region.replace(' ','').lower())
# ), index=False
# )
# edge_summary.groupby(['no_access'])['min_ini_adap_cost','max_ini_adap_cost','min_tot_adap_cost',\
# 'max_tot_adap_cost','min_benefit','max_benefit'].sum().reset_index().to_csv(os.path.join(config['paths']['output'],
# 'network_stats',
# '{}_adapt_total_bc_fixed_parameter.csv'.format(region.replace(' ','').lower())
# ), index=False
# )
# edge_min_costs = edge_summary[edge_summary['min_ini_adap_cost_perkm'] > 0].groupby(['asset_type'])['min_ini_adap_cost_perkm','min_tot_adap_cost_perkm'].min().reset_index()
# edge_max_costs = edge_summary.groupby(['asset_type'])['max_ini_adap_cost_perkm','max_tot_adap_cost_perkm'].max().reset_index()
# edge_minmax_costs = pd.merge(edge_min_costs,edge_max_costs,how='left',on=['asset_type'])
# edge_minmax_costs.to_csv(os.path.join(config['paths']['output'],
# 'network_stats',
# '{}_adapt_total_costsperkm_fixed_parameter.csv'.format(region.replace(' ','').lower())
# ), index=False
# )
# for c in range(len(adapt_set)):
# plot_settings = get_region_plot_settings(region)
# ax = get_axes(plot_settings['bbox'], figsize=plot_settings['figure_size'])
# if region == 'Binh Dinh':
# plot_basemap(ax, config['paths']['data'], country_border='none',
# plot_states=False, plot_districts=True, highlight_region=region)
# else:
# plot_basemap(ax, config['paths']['data'], country_border='none',
# plot_states=True, plot_districts=True, highlight_region=region)
# scale_bar(ax, location=(0.8, 0.05), length=plot_settings['scale_legend'])
# proj_lat_lon = ccrs.PlateCarree()
# # generate weight bins
# column = adapt_set[c]['column']
# weights = [record[column] for iter_, record in edges_vals.iterrows()]
# max_weight = max(weights)
# if column in ('min_bc_rat', 'max_bc_rat') and region == 'Lao Cai':
# width_by_range = generate_weight_bins_with_colour_gradient(
# weights, n_steps=6, width_step=0.001)
# else:
# width_by_range = generate_weight_bins_with_colour_gradient(
# weights, width_step=0.001)
# road_geoms_by_category = {
# region: []
# }
# styles = OrderedDict([
# ('1', Style(color='#252525', zindex=6, label='Hazard failure: No access')), # black
# ('2', Style(color='#f1605d', zindex=8, label='Hazard failure: Reroutiing possible')), # orange
# ('3', Style(color='#969696', zindex=7, label='No hazard exposure/effect')) # grey
# ])
# for iter_, record in edges_vals.iterrows():
# cat = region
# geom = record.geometry
# noaccess = record.no_access
# val = record[column]
# buffered_geom = None
# for (nmin, nmax), line_style in width_by_range.items():
# if nmin <= val and val < nmax:
# buffered_geom = geom.buffer(line_style[1])
# if buffered_geom is not None:
# if val == 0 and noaccess == 0:
# ax.add_geometries(
# [buffered_geom],
# crs=proj_lat_lon,
# linewidth=0,
# facecolor='#969696',
# edgecolor='none',
# zorder=2 + line_style[0]
# )
# elif noaccess == 0:
# ax.add_geometries(
# [buffered_geom],
# crs=proj_lat_lon,
# linewidth=0,
# facecolor=str(line_style[2]),
# edgecolor='none',
# zorder=3 + line_style[0]
# )
# else:
# ax.add_geometries(
# [buffered_geom],
# crs=proj_lat_lon,
# linewidth=0,
# facecolor='#252525',
# edgecolor='none',
# zorder=4 + line_style[0]
# )
# else:
# print("Feature was outside range to plot", iter_)
# x_l = plot_settings['weight_legend']['x_l']
# x_r = plot_settings['weight_legend']['x_r']
# base_y = plot_settings['weight_legend']['base_y']
# y_step = plot_settings['weight_legend']['y_step']
# y_text_nudge = plot_settings['weight_legend']['y_text_nudge']
# x_text_nudge = plot_settings['weight_legend']['x_text_nudge']
# # text above weight legend
# ax.text(
# x_l,
# base_y + y_step - y_text_nudge,
# adapt_set[c]['legend_label'],
# horizontalalignment='left',
# transform=proj_lat_lon,
# size=10)
# # weight legend
# divisor = adapt_set[c]['divisor']
# # print (width_by_range)
# significance_ndigits = adapt_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), line_style)) in enumerate(width_by_range.items()):
# y = base_y - (i*y_step)
# line = LineString([(x_l, y), (x_r, y)]).buffer(line_style[1])
# ax.add_geometries(
# [line],
# crs=proj_lat_lon,
# linewidth=0,
# edgecolor='#969696',
# facecolor='#969696',
# 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)
# # district labels
# plot_district_labels(ax, config['paths']['data'], highlight_region=region)
# # plot
# title = '{} ({})'.format(region, adapt_set[c]['title'])
# print(" * Plotting", title)
# plt.title(title, fontsize=14)
# legend_from_style_spec(ax, styles,loc='upper right')
# # output
# output_file = os.path.join(
# config['paths']['figures'], 'commune_center-{}-{}-values-fixed-parameter.png'.format(region.lower().replace(' ', ''), column))
# save_fig(output_file)
# plt.close()
if __name__ == '__main__':
main()