#!/usr/bin/python
import argparse
import configparser
-import csv
import re
-import sqlite3
+import sys
+import time
+from copy import deepcopy
from datetime import timedelta
-from itertools import islice
from typing import List, NamedTuple, Optional
import isodate
import jsonschema
from osgeo import ogr
-from osgeo.ogr import Layer, DataSource, Geometry, wkbPoint, Feature
+from osgeo.ogr import Layer, Geometry, wkbPoint, Feature
from osgeo.osr import SpatialReference, CoordinateTransformation, OAMS_TRADITIONAL_GIS_ORDER
from wrpylib.json_tools import order_json_keys, format_json
co2_kg: float
-def vao_car_distance(vao: Vao, parking_lon: float, parking_lat: float, city: Feature) -> Optional[DistInfo]:
+class VaoError(RuntimeError):
+ pass
+
+
+def try_vao_car_distance(vao: Vao, parking_lon: float, parking_lat: float, city: Feature) -> DistInfo:
+ """may throw VaoError with JSON decoded response as argument"""
geometry = city.GetGeometryRef()
point = geometry.GetPoint(0)
city_lon, city_lat, _ = point
duration_minutes = int(round(duration / timedelta(minutes=1)))
dist_m = leg['dist']
co2_kg = trip[0]['Eco']['co2']
- return DistInfo(city['name'], city['geonameid'], duration_minutes, dist_m, co2_kg)
+ return DistInfo(city['name'], city['geonames_id'], duration_minutes, dist_m, co2_kg)
+ raise VaoError(response)
+
+
+def vao_car_distance(vao: Vao, parking_lon: float, parking_lat: float, city: Feature, retry_count: int = 2) -> DistInfo:
+ for c in range(retry_count):
+ try:
+ return try_vao_car_distance(vao, parking_lon, parking_lat, city)
+ except VaoError as vao_error:
+ response = vao_error.args[0]
+ if response.get('errorCode') is not None:
+ print(response['errorCode'], response.get('errorText'), f'(attempt {c+1}/{retry_count})')
+ if response['errorCode'] == 'SVC_NO_RESULT':
+ time.sleep(2.)
+ continue
+ else:
+ print('Unexpected result from VAO')
+ sys.exit(1)
+
+
+def distance_meter(a: Geometry, b: Geometry) -> float:
+ spatial_reference_ll = SpatialReference()
+ spatial_reference_ll.ImportFromEPSG(4326)
+ spatial_reference_ll.SetAxisMappingStrategy(OAMS_TRADITIONAL_GIS_ORDER)
+
+ spatial_reference_m = SpatialReference()
+ spatial_reference_m.ImportFromProj4(f'+proj=merc +lat_ts={(a.GetY() + b.GetY()) / 2}')
+
+ ll_to_m = CoordinateTransformation(spatial_reference_ll, spatial_reference_m)
+
+ a_m = a.Clone()
+ a_m.Transform(ll_to_m)
+
+ b_m = b.Clone()
+ b_m.Transform(ll_to_m)
+
+ return a_m.Distance(b_m)
+
+
+def dist_info_to_dict(dist_info: DistInfo) -> dict:
+ return {
+ 'km': round(dist_info.dist_m / 1000, 1),
+ 'route': dist_info.city_name,
+ 'minutes': dist_info.duration_minutes,
+ 'geonames_id': dist_info.geoname_id,
+ 'onward_co2_kg': round(dist_info.co2_kg, 1),
+ }
def update_sledrun(vao: Vao, db_cities: Layer, site: WikiSite, title: str):
sledrun_json_page = site.query_page(f'{title}/Rodelbahn.json')
sledrun_json = page_json(sledrun_json_page)
- # for now...
- if 'car_distances' in sledrun_json:
- return
-
sledrun_json_orig = sledrun_json.copy()
car_parking = sledrun_json.get('car_parking')
parking_lon = parking['longitude']
parking_lat = parking['latitude']
+ car_distance_list = deepcopy(sledrun_json.get('car_distances', []))
+ if len([car_distance for car_distance in car_distance_list if car_distance.get('geonames_id') is not None]) > 0:
+ return
+
+ db_cities.SetSpatialFilter(None)
+ db_cities.SetAttributeFilter(None)
+ for car_distance in car_distance_list:
+ if car_distance.get('geonames_id') is None:
+ name = car_distance['route']
+ match = re.match(r'([-\w. ]+)\(?.*$', name)
+ if match is not None:
+ name = match.group(1).strip()
+ candidates = [city for city in db_cities if city['name'] == name]
+ if len(candidates) == 1:
+ city = candidates[0]
+ dist_info = vao_car_distance(vao, parking_lon, parking_lat, city)
+ if dist_info is not None:
+ dist_info_dict = dist_info_to_dict(dist_info)
+ car_distance.update(dist_info_dict)
+
spatial_reference_ll = SpatialReference()
spatial_reference_ll.ImportFromEPSG(4326)
spatial_reference_ll.SetAxisMappingStrategy(OAMS_TRADITIONAL_GIS_ORDER)
spatial_reference_m = SpatialReference()
spatial_reference_m.ImportFromProj4(f'+proj=merc +lat_ts={parking_lat}')
- # spatial_reference_m.ImportFromProj4(f'+proj=merc')
ll_to_m = CoordinateTransformation(spatial_reference_ll, spatial_reference_m)
m_to_ll = CoordinateTransformation(spatial_reference_m, spatial_reference_ll)
- loc_ll = Geometry(wkbPoint)
- loc_ll.AddPoint(parking_lon, parking_lat)
- # print(loc_ll.ExportToWkt())
+ parking_ll = Geometry(wkbPoint)
+ parking_ll.AddPoint(parking_lon, parking_lat)
- loc_m = loc_ll.Clone()
- loc_m.Transform(ll_to_m)
- # print(loc_m.ExportToWkt())
+ parking_m = parking_ll.Clone()
+ parking_m.Transform(ll_to_m)
max_dist_m = 60000
- bound_m = loc_m.Buffer(max_dist_m)
- # print(bound_m.ExportToWkt())
+ bound_m = parking_m.Buffer(max_dist_m)
bound_ll = bound_m.Clone()
- # print(bound_ll.ExportToWkt())
bound_ll.Transform(m_to_ll)
- # print(bound_ll.ExportToWkt())
db_cities.SetSpatialFilter(bound_ll)
db_cities.SetAttributeFilter('level<=2')
- dist_info_list = []
- for city in db_cities:
- dist_info = vao_car_distance(vao, parking_lon, parking_lat, city)
- if dist_info is not None:
- dist_info_list.append(dist_info)
- dist_info_list = sorted(dist_info_list, key=lambda di: di.dist_m)[:3]
- car_distances = [{
- 'km': round(di.dist_m / 1000, 1),
- 'route': di.city_name,
- 'minutes': di.duration_minutes,
- 'geonames_id': di.geoname_id,
- 'onward_co2_kg': round(di.co2_kg, 1),
- } for di in dist_info_list]
- sledrun_json['car_distances'] = car_distances
+ close_cities = [city for city in db_cities]
+ close_cities = sorted(close_cities, key=lambda city: distance_meter(city.GetGeometryRef(), parking_ll))
+
+ max_number_distances = 3
+ car_distances_to_append: List[dict] = []
+ for city in close_cities:
+ if len(car_distances_to_append) >= max_number_distances:
+ if car_distances_to_append[2]['km'] * 1000 < distance_meter(city.GetGeometryRef(), parking_ll):
+ break
+ print(city['name'])
+ car_distance = next((cd for cd in car_distance_list if cd.get('geonames_id') == city['geonames_id']), None)
+ if car_distance is None:
+ car_distance = vao_car_distance(vao, parking_lon, parking_lat, city)
+ if car_distance is not None:
+ car_distance = dist_info_to_dict(car_distance)
+ if car_distance is not None:
+ car_distances_to_append.append(car_distance)
+ car_distances_to_append = sorted(car_distances_to_append, key=lambda di: di['km'])
+
+ car_distances_to_append = sorted(car_distances_to_append, key=lambda di: di['km'])[:max_number_distances]
+
+ for car_distance in car_distances_to_append:
+ if len([cd for cd in car_distance_list if cd.get('geonames_id') == car_distance['geonames_id']]) == 0:
+ car_distance_list.append(car_distance)
+
+ car_distance_list = sorted(car_distance_list, key=lambda di: di['km'])
+ sledrun_json['car_distances'] = car_distance_list
+
if sledrun_json == sledrun_json_orig:
return
'edit',
pageid=sledrun_json_page['pageid'],
text=sledrun_json_str,
- summary=f'Entfernungen zu {title} eingefügt (dank VAO).',
+ summary=f'Entfernungen zu {title} aktualisiert (dank VAO).',
# minor=1,
bot=1,
baserevid=sledrun_json_page['revisions'][0]['revid'],
for result in site.query(list='categorymembers', cmtitle='Kategorie:Rodelbahn', cmlimit='max'):
for page in result['categorymembers']:
print(page['title'])
+ if page['title'] in ['Anzère', 'Hochhäderich (Falkenhütte)', 'Hochlitten-Moosalpe', 'Saas-Fee']:
+ continue
update_sledrun(vao, db_cities, site, page['title'])
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