moved services/stops.py to enhancer

amarillo/plugins/enhancer/services/stops.py

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