moved services/stops.py to enhancer
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amarillo/plugins/enhancer/services/stops.py
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182
amarillo/plugins/enhancer/services/stops.py
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import csv
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import geopandas as gpd
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import pandas as pd
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from amarillo.models.Carpool import StopTime
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from contextlib import closing
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from shapely.geometry import Point, LineString
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from shapely.ops import transform
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from pyproj import Proj, Transformer
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import re
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import requests
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from io import TextIOWrapper
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import codecs
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import logging
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logger = logging.getLogger(__name__)
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class StopsStore():
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def __init__(self, stop_sources = [], internal_projection = "EPSG:32632"):
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self.internal_projection = internal_projection
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self.projection = Transformer.from_crs("EPSG:4326", internal_projection, always_xy=True).transform
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self.stopsDataFrames = []
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self.stop_sources = stop_sources
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def load_stop_sources(self):
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"""Imports stops from stop_sources and registers them with
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the distance they are still associated with a trip.
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E.g. bus stops should be registered with a distance of e.g. 30m,
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while larger carpool parkings might be registered with e.g. 500m.
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Subsequent calls of load_stop_sources will reload all stop_sources
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but replace the current stops only if all stops could be loaded successfully.
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"""
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stopsDataFrames = []
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error_occured = False
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for stops_source in self.stop_sources:
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try:
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stopsDataFrame =self._load_stops(stops_source["url"])
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stopsDataFrames.append({'distanceInMeter': stops_source["vicinity"],
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'stops': stopsDataFrame})
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except Exception as err:
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error_occured = True
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logger.error("Failed to load stops from %s to StopsStore.", stops_source["url"], exc_info=True)
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if not error_occured:
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self.stopsDataFrames = stopsDataFrames
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def find_additional_stops_around(self, line, stops = None):
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"""Returns a GeoDataFrame with all stops in vicinity of the
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given line, sorted by distance from origin of the line.
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Note: for internal projection/distance calculations, the
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lat/lon geometries of line and stops are converted to
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"""
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stops_frames = []
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if stops:
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stops_frames.append(self._convert_to_dataframe(stops))
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transformedLine = transform(self.projection, LineString(line.coordinates))
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for stops_to_match in self.stopsDataFrames:
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stops_frames.append(self._find_stops_around_transformed(stops_to_match['stops'], transformedLine, stops_to_match['distanceInMeter']))
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stops = gpd.GeoDataFrame( pd.concat(stops_frames, ignore_index=True, sort=True))
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if not stops.empty:
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self._sort_by_distance(stops, transformedLine)
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return stops
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def find_closest_stop(self, carpool_stop, max_search_distance):
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transformedCoord = Point(self.projection(carpool_stop.lon, carpool_stop.lat))
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best_dist = max_search_distance + 1
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best_stop = None
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for stops_with_dist in self.stopsDataFrames:
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stops = stops_with_dist['stops']
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s, d = stops.sindex.nearest(transformedCoord, return_all= True, return_distance=True, max_distance=max_search_distance)
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if len(d) > 0 and d[0] < best_dist:
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best_dist = d[0]
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row = s[1][0]
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best_stop = StopTime(name=stops.at[row, 'stop_name'], lat=stops.at[row, 'y'], lon=stops.at[row, 'x'])
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return best_stop if best_stop else carpool_stop
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def _normalize_stop_name(self, stop_name):
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default_name = 'P+R-Parkplatz'
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if stop_name in ('', 'Park&Ride'):
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return default_name
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normalized_stop_name = re.sub(r"P(ark)?\s?[\+&]\s?R(ail|ide)?",'P+R', stop_name)
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return normalized_stop_name
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def _load_stops(self, source : str):
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"""Loads stops from given source and registers them with
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the distance they are still associated with a trip.
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E.g. bus stops should be registered with a distance of e.g. 30m,
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while larger carpool parkings might be registered with e.g. 500m
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"""
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logger.info("Load stops from %s", source)
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if source.startswith('http'):
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if source.endswith('json'):
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with requests.get(source) as json_source:
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stopsDataFrame = self._load_stops_geojson(json_source.json())
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else:
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with requests.get(source) as csv_source:
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stopsDataFrame = self._load_stops_csv(codecs.iterdecode(csv_source.iter_lines(), 'utf-8'))
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else:
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with open(source, encoding='utf-8') as csv_source:
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stopsDataFrame = self._load_stops_csv(csv_source)
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return stopsDataFrame
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def _load_stops_csv(self, csv_source):
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id = []
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lat = []
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lon = []
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stop_name = []
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reader = csv.DictReader(csv_source, delimiter=';')
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columns = ['stop_id', 'stop_lat', 'stop_lon', 'stop_name']
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lists = [id, lat, lon, stop_name]
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for row in reader:
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for col, lst in zip(columns, lists):
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if col == "stop_lat" or col == "stop_lon":
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lst.append(float(row[col].replace(",",".")))
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elif col == "stop_name":
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row_stop_name = self._normalize_stop_name(row[col])
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lst.append(row_stop_name)
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else:
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lst.append(row[col])
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return self._as_dataframe(id, lat, lon, stop_name)
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def _load_stops_geojson(self, geojson_source):
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id = []
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lat = []
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lon = []
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stop_name = []
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columns = ['stop_id', 'stop_lat', 'stop_lon', 'stop_name']
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lists = [id, lat, lon, stop_name]
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for row in geojson_source['features']:
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coord = row['geometry']['coordinates']
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if not coord or not row['properties'].get('name'):
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logger.error('Stop feature {} has null coord or name'.format(row['id']))
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continue
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for col, lst in zip(columns, lists):
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if col == "stop_lat":
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lst.append(coord[1])
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elif col == "stop_lon":
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lst.append(coord[0])
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elif col == "stop_name":
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row_stop_name = self._normalize_stop_name(row['properties']['name'])
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lst.append(row_stop_name)
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elif col == "stop_id":
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lst.append(row['id'])
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return self._as_dataframe(id, lat, lon, stop_name)
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def _as_dataframe(self, id, lat, lon, stop_name):
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df = gpd.GeoDataFrame(data={'x':lon, 'y':lat, 'stop_name':stop_name, 'id':id})
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stopsGeoDataFrame = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.x, df.y, crs='EPSG:4326'))
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stopsGeoDataFrame.to_crs(crs=self.internal_projection, inplace=True)
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return stopsGeoDataFrame
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def _find_stops_around_transformed(self, stopsDataFrame, transformedLine, distance):
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bufferedLine = transformedLine.buffer(distance)
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sindex = stopsDataFrame.sindex
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possible_matches_index = list(sindex.intersection(bufferedLine.bounds))
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possible_matches = stopsDataFrame.iloc[possible_matches_index]
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exact_matches = possible_matches[possible_matches.intersects(bufferedLine)]
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return exact_matches
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def _convert_to_dataframe(self, stops):
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return gpd.GeoDataFrame([[stop.name, stop.lon, stop.lat,
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stop.id, Point(self.projection(stop.lon, stop.lat))] for stop in stops], columns = ['stop_name','x','y','id','geometry'], crs=self.internal_projection)
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def _sort_by_distance(self, stops, transformedLine):
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stops['distance']=stops.apply(lambda row: transformedLine.project(row['geometry']), axis=1)
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stops.sort_values('distance', inplace=True)
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def is_carpooling_stop(stop_id, name):
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stop_name = name.lower()
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# mfdz: or bbnavi: prefixed stops are custom stops which are explicitly meant to be carpooling stops
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return stop_id.startswith('mfdz:') or stop_id.startswith('bbnavi:') or 'mitfahr' in stop_name or 'p&m' in stop_name
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