"""Summarise hazard data
Get OD data and process it
"""
import ast
import itertools
import math
import operator
import os
import subprocess
import sys
import geopandas as gpd
import igraph as ig
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 scipy.spatial import Voronoi
from shapely.geometry import Point, Polygon
from vtra.utils import *
mpl.style.use('ggplot')
mpl.rcParams['font.size'] = 13.
mpl.rcParams['font.family'] = 'tahoma'
mpl.rcParams['axes.labelsize'] = 14.
mpl.rcParams['xtick.labelsize'] = 13.
mpl.rcParams['ytick.labelsize'] = 13.
[docs]def main():
data_path, calc_path, output_path, figure_path = load_config()['paths']['data'], load_config(
)['paths']['calc'], load_config()['paths']['output'], load_config()['paths']['figures']
# Get the modal shares
modes_cols = ['road', 'rail', 'air', 'inland', 'coastal']
for m in range(len(modes_cols)):
mode_data_path = os.path.join(data_path, 'Results', 'Flow_shapefiles',
'weighted_edges_flows_national_{0}.shp'.format(modes_cols[m]))
edges_df = gpd.read_file(mode_data_path)
if modes_cols[m] in ('air', 'inland', 'coastal'):
edges_df = edges_df[edges_df['max_tons'] > 0]
length = sum(edges_df['length'].values.tolist())
print('Total length of {0} network = {1}'.format(modes_cols[m], length))
if modes_cols[m] == 'road':
paved_df = edges_df[edges_df['road_cond'] == 'paved']
paved_length = sum(paved_df['length'].values.tolist())
unpaved_df = edges_df[edges_df['road_cond'] == 'unpaved']
unpaved_length = sum(unpaved_df['length'].values.tolist())
print('Road paved length = ', paved_length)
print('Road unpaved length = ', unpaved_length)
print('Road paved length percentage = ', 100.0*paved_length/length)
print('Road unpaved length percentage = ', 100.0*unpaved_length/length)
for road_class in range(1, 7):
road_class_df = edges_df[edges_df['road_class'] == road_class]
class_length = sum(road_class_df['length'].values.tolist())
print('Road class {0} length = {1}'.format(road_class, class_length))
print('Road class {0} length percentage = {1}'.format(
road_class, 100.0*class_length/length))
road_width_list = []
unique_rd_widths = sorted(list(set(edges_df['width'].values.tolist())))
for u_rdw in unique_rd_widths:
unique_rd_widths_df = edges_df[edges_df['width'] == u_rdw]
u_rdw_count = len(unique_rd_widths_df.index)
u_rdw_length = sum(unique_rd_widths_df['length'].values.tolist())
road_width_list.append(
(u_rdw, u_rdw_count, u_rdw_length, 100.0*u_rdw_length/length))
road_width_list_df = pd.DataFrame(road_width_list, columns=[
'Road width', 'Link Count', 'Length (km)', 'Length (%)'])
road_width_list_df.to_csv(os.path.join(
data_path, 'Results', 'network_stats', 'national_road_widths.csv'), index=False)
# plt.figure(figsize=(8, 4))
# ax = edges_df['width'].hist(bins=10)
# plt.xlabel('Road width (meters)', fontweight='bold')
# plt.ylabel('Count', fontweight='bold')
# plt.tight_layout()
# output_file = os.path.join(figure_path, 'national_road_width-histogram.png')
# plt.savefig(output_file, dpi=500)
# plt.close()
if modes_cols[m] == 'rail':
route_codes = list(set(edges_df['railwaylin'].values.tolist()))
rail_route_list = []
for u_r in route_codes:
unique_r_df = edges_df[edges_df['railwaylin'] == u_r]
u_r_length = sum(unique_r_df['length'].values.tolist())
rail_route_list.append((u_r, u_r_length, 100.0*u_r_length/length))
rail_route_list_df = pd.DataFrame(rail_route_list, columns=[
'Rail route code', 'Length (km)', 'Length (%)'])
rail_route_list_df.to_csv(os.path.join(
data_path, 'Results', 'network_stats', 'national_rail_routes.csv'), index=False)
province_list = ['Lao Cai', 'Binh Dinh', 'Thanh Hoa']
province_stats = []
level_names = ['National', 'Provincial', 'Local', 'Other']
for prn in range(len(province_list)):
province = province_list[prn]
province_name = province.replace(' ', '').lower()
edges_in = os.path.join(data_path, 'Results', 'Flow_shapefiles',
'weighted_edges_commune_center_flows_{0}_5_tons.shp'.format(province_name))
edges_df = gpd.read_file(edges_in)
length = sum(edges_df['length'].values.tolist())
for level in range(0, 4):
level_paved_df = edges_df[(edges_df['level'] == level) &
(edges_df['road_cond'] == 'paved')]
level_unpaved_df = edges_df[(edges_df['level'] == level)
& (edges_df['road_cond'] == 'unpaved')]
level_paved_length = sum(level_paved_df['length'].values.tolist())
level_unpaved_length = sum(level_unpaved_df['length'].values.tolist())
province_stats.append((province, level_names[level], level_paved_length,
level_unpaved_length, 1.0*level_paved_length/length, 1.0*level_unpaved_length/length))
province_stats_df = pd.DataFrame(province_stats, columns=[
'Province name', 'Road level', 'Paved (km)', 'Unpaved (km)', 'Paved(%)', 'Unpaved (%)'])
province_stats_df.to_csv(os.path.join(data_path, 'Results',
'network_stats', 'province_roads_conditions.csv'), index=False)
if __name__ == '__main__':
main()