79 lines
2.8 KiB
Python
79 lines
2.8 KiB
Python
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import geopandas as gpd
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from geopy import distance
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import pandas as pd
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from shapely.geometry import Point
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import numpy as np
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from shapely.ops import nearest_points
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from sklearn.neighbors import KDTree
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from tqdm import tqdm
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from matplotlib import pyplot as plt
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import sys
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from util import constants as C
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CITIES = [('NYC', 'New York'), ('SF', 'San Francisco'), ('Seattle', 'Seattle'), ('Boston', 'Boston'), ('Chicago', 'Chicago'), ('Philadelphia', 'Philadelphia'), ('DC', 'Washington'),
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('LA', 'Los Angeles'), ('Baltimore', 'Baltimore'), ('Milwaukee', 'Milwaukee')]
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class Zoning:
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def __init__(self, path):
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self.path = path
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self.gdf = gpd.read_file(self.path)
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self.zone_type = self.gdf.zone_type.tolist()
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self._get_centroids()
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def _get_centroids(self):
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centroids = self.gdf.centroid
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coords = []
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for i, c in enumerate(centroids):
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if c is None or self.zone_type[i] == 'roads':
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coords.append([10000, 10000])
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else:
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coords.append([c.y, c.x])
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self.coords = KDTree(np.array(coords), leaf_size=30)
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def get_zone(self, lat, lon, n=-1, return_polygon=False):
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if n == -1:
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ind = range(len(self.gdf))
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else:
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ind = self.coords.query(np.array([lat, lon])[np.newaxis,:], k=n, return_distance=False).flatten()
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dist = 10000
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zone_type = None
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zone = None
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for i in list(ind):
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_zone = self.gdf.geometry.iloc[i]
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#for p in nearest_points(_zone, Point(lon, lat)):
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p = nearest_points(_zone, Point(lon, lat))[0]
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_lat, _lon = p.y, p.x
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_dist = distance.distance((lat, lon), (_lat, _lon)).m
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if _dist < dist:
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zone_type = self.zone_type[i]
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dist = _dist
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zone = _zone
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if return_polygon:
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return zone_type, dist, zone
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else:
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return zone_type, dist
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def calculate_zone(meta_path="/share/data/camera/deployment/verified_0425.csv"):
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df = pd.read_csv(meta_path)
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dfs = []
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for city, city_tag in CITIES:
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print(f"Loading zoning shapefile for [{city_tag}]..")
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try:
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zone = Zoning(f"/share/data/camera/zoning/{city_tag}_zoning_clean.shp")
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except Exception as e:
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print(str(e))
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continue
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final = df.query(f"city == '{city}'")
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rows = []
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for rid, row in tqdm(final.iterrows(), total=len(final)):
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z, d = zone.get_zone(row['lat'], row['lon'], n=5)
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row['zone_type'] = z
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row['zone_distance'] = d
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rows.append(row)
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zone_final = pd.DataFrame(rows)
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dfs.append(zone_final)
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pd.concat(dfs).to_csv("/share/data/camera/deployment/verified_0425_zone.csv", index=False)
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