-
Notifications
You must be signed in to change notification settings - Fork 23
/
utils.py
288 lines (214 loc) · 10.3 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
import os
import numpy as np
import geopy.distance
import time
from datetime import date, timedelta
from datetime import datetime as dt
import scipy.sparse as sp
import torch
def to_sparse_tensor(dense_matrix):
coo = sp.coo_matrix(dense_matrix)
indices = torch.LongTensor(np.vstack((coo.row, coo.col)))
values = torch.FloatTensor(coo.data)
shape = coo.shape
sparse_tensor = torch.sparse.FloatTensor(indices, values, torch.Size(shape))
return sparse_tensor
def date_range(date1, date2):
# date1, date2 = '20160401', '20160428'
datetime1 = dt.strptime(date1, '%Y%m%d')
datetime2 = dt.strptime(date2, '%Y%m%d')
days = (datetime2 - datetime1).days + 1
date_list = [(datetime1 + timedelta(day)).strftime('%Y%m%d') for day in range(days)]
return date_list
def time_difference(time1, time2):
# format: '25/03/2016 00:00:04'
# time_difference = time1 - time2
return (dt.strptime(time1, '%d/%m/%Y %H:%M:%S') - dt.strptime(time2, '%d/%m/%Y %H:%M:%S')).total_seconds()
def df_to_csv(df, file_path, index=False):
print('Saving to file at %s'%(file_path))
if os.path.exists(file_path):
temp_file_path = '%s_temp'%(file_path)
df.to_csv(temp_file_path, index=index)
os.system('rm %s'%(file_path))
os.system('mv %s %s'%(temp_file_path, file_path))
else:
df.to_csv(file_path, index=index)
print('Saved.')
def print_log(line, log_path):
with open(log_path, 'a') as f:
f.write(str(line)+'\n')
def round_time(t, interval=5):
# t = '25/03/2016 12:26:45'
# output: '25/03/2016 12:25:00'
# interval: in minutes
interval = interval * 60 # convert minutes to seconds
datetime = dt.strptime(t, '%d/%m/%Y %H:%M:%S')
new_datetime = dt.fromtimestamp(int(time.mktime(datetime.timetuple())) // interval * interval)
return new_datetime.strftime('%d/%m/%Y %H:%M:%S')
def geodistance(coords_1, coords_2):
return geopy.distance.great_circle(coords_1, coords_2).m
##### Visualization #####
##### The code below for displaying road segments, road network, and vehicle trajectories
#
# import folium
#
# class Point():
# def __init__(self, latitude=None, longitude=None, time=None):
# self.lat = latitude
# self.lon = longitude
# self.time = time
# def __str__(self):
# return '%s, %s'%(self.lat, self.lon, self.time)
# def get_bearing(p1, p2):
# # Returns compass bearing from p1 to p2
# long_diff = np.radians(p2.lon - p1.lon)
# lat1 = np.radians(p1.lat)
# lat2 = np.radians(p2.lat)
# x = np.sin(long_diff) * np.cos(lat2)
# y = (np.cos(lat1) * np.sin(lat2)
# - (np.sin(lat1) * np.cos(lat2)
# * np.cos(long_diff)))
# bearing = np.degrees(np.arctan2(x, y))
# # adjusting for compass bearing
# if bearing < 0:
# return bearing + 360
# return bearing
# def get_arrow(locations, color='#3388ff', size=6, n_arrows=3, road_id=''):
# # get arrow for a road segment to indicate the direction
# # locations e.g. [(1.3096, 103.9081), (1.3103, 103.9079)]
# # creating point from our Point named tuple
# p1 = Point(locations[0][0], locations[0][1])
# p2 = Point(locations[1][0], locations[1][1])
# # getting the rotation needed for our marker.
# # Subtracting 90 to account for the marker's orientation
# # of due East(get_bearing returns North)
# rotation = get_bearing(p1, p2) - 90
# # get an evenly space list of lats and lons for our arrows
# # note that I'm discarding the first and last for aesthetics
# # as I'm using markers to denote the start and end
# # arrow_lats = np.linspace(p1.lat, p2.lat, n_arrows + 2)[1:n_arrows+1]
# # arrow_lons = np.linspace(p1.lon, p2.lon, n_arrows + 2)[1:n_arrows+1]
# arrow_lat = p2.lat
# arrow_lon = p2.lon
# arrows = []
# #creating each "arrow" and appending them to our arrows list
# # for points in zip(arrow_lats, arrow_lons):
# arrow = folium.RegularPolygonMarker(location=(arrow_lat, arrow_lon),
# weight=1, color=color, fill_color=color, number_of_sides=3,
# radius=size, rotation=rotation, popup='%s, %s, %s'%(arrow_lat, arrow_lon, road_id))
# return arrow
# def display_road(start_lon, start_lat, end_lon, end_lat, color='#3388ff', weight=3, m=None, tiles='OpenStreetMap', road_id='', arrow=True):
# center_lat = (start_lat + end_lat) / 2
# center_lon = (start_lon + end_lon) / 2
# # plot map
# if m is None:
# m = folium.Map(location=[center_lat, center_lon], zoom_start=20, tiles=tiles)
# # add road line
# folium.PolyLine([(start_lat, start_lon), (end_lat, end_lon)], color=color, weight=weight).add_to(m)
# # add direction arrow to road
# if arrow:
# get_arrow([(start_lat, start_lon), (end_lat, end_lon)], color=color, road_id=road_id).add_to(m)
# return m
# def display_roads(road_ids, road_df, color='#3388ff', m=None, tiles='OpenStreetMap', arrow=True):
# # E.g. display_roads([103067603, 103106763], road_df)
# # extract roads
# df = road_df.set_index('road_id')
# roads = df.loc[road_ids]
# # locate the center of the map
# min_lat = min(min(roads['start_lat']), min(roads['end_lat']))
# max_lat = max(max(roads['start_lat']), max(roads['end_lat']))
# min_lon = min(min(roads['start_lon']), min(roads['end_lon']))
# max_lon = max(max(roads['start_lon']), max(roads['end_lon']))
# center_lat = (min_lat + max_lat) / 2
# center_lon = (min_lon + max_lon) / 2
# # plot map
# if m is None:
# m = folium.Map(location=[center_lat, center_lon], zoom_start=15, tiles=tiles)
# # add road
# for _, road in roads.iterrows():
# display_road(road['start_lon'], road['start_lat'], road['end_lon'], road['end_lat'], color=color, m=m, road_id=road.name, arrow=arrow)
# return m
# def display_roads_heatmap(road_ids, road_df, colors=None, weights=None, m=None, tiles='OpenStreetMap', arrow=True):
# # E.g. display_roads([103067603, 103106763], road_df, ['#ff0000', '#ffa500'], [3, 3])
# # extract roads
# df = road_df.set_index('road_id')
# roads = df.loc[road_ids]
# # locate the center of the map
# min_lat = min(min(roads['start_lat']), min(roads['end_lat']))
# max_lat = max(max(roads['start_lat']), max(roads['end_lat']))
# min_lon = min(min(roads['start_lon']), min(roads['end_lon']))
# max_lon = max(max(roads['start_lon']), max(roads['end_lon']))
# center_lat = (min_lat + max_lat) / 2
# center_lon = (min_lon + max_lon) / 2
# # plot map
# if m is None:
# m = folium.Map(location=[center_lat, center_lon], zoom_start=15, tiles=tiles)
# # add road
# if colors is None: colors = ['#3388ff'] * len(road_ids)
# if weights is None: weights = [3] * len(road_ids)
# for (_, road), color, weight in zip(roads.iterrows(), colors, weights):
# display_road(road['start_lon'], road['start_lat'], road['end_lon'], road['end_lat'], color=color, weight=weight, m=m, road_id=road.name, arrow=arrow)
# return m
# def display_road_network(min_lat, max_lat, min_lon, max_lon, road_df, color='#3388ff', m=None, tiles='OpenStreetMap', arrow=True):
# # E.g. display_road_network(1.310, 1.315, 103.90, 103.91, road_df, color='red', tiles='cartodbpositron')
# # extract roads
# df = road_df[(road_df['start_lat']>=min_lat) & (road_df['start_lat']<=max_lat) &
# (road_df['end_lat']>=min_lat) & (road_df['end_lat']<=max_lat) &
# (road_df['start_lon']>=min_lon) & (road_df['start_lon']<=max_lon) &
# (road_df['end_lon']>=min_lon) & (road_df['end_lon']<=max_lon)]
# road_ids = list(df['road_id'])
# df = df.set_index('road_id')
# roads = df.loc[road_ids]
# # locate the center of the map
# center_lat = (min_lat + max_lat) / 2
# center_lon = (min_lon + max_lon) / 2
# # plot map
# if m is None:
# m = folium.Map(location=[center_lat, center_lon], zoom_start=15, tiles=tiles)
# # add road
# for _, road in roads.iterrows():
# display_road(road['start_lon'], road['start_lat'], road['end_lon'], road['end_lat'], color=color, m=m, arrow=arrow)
# return m
# def display_trajectory(points, m=None, tiles='OpenStreetMap'):
# min_lat, max_lat, min_lon, max_lon = (91, -90, 181, -181)
# markers = []
# for point in points:
# min_lat = min(min_lat, point.lat)
# max_lat = max(max_lat, point.lat)
# min_lon = min(min_lon, point.lon)
# max_lon = max(max_lon, point.lon)
# markers.append(folium.Marker([point.lat, point.lon], popup='%s'%(point.time)))
# # locate the center of the map
# center_lat = (min_lat + max_lat) / 2
# center_lon = (min_lon + max_lon) / 2
# # plot map
# if m is None:
# m = folium.Map(location=[center_lat, center_lon], zoom_start=15, tiles=tiles)
# # add point
# for marker in markers:
# marker.add_to(m)
# return m
# def display_vehicle_raw_trajectory(vehicle_id, start_time, end_time, df, m=None, tiles='OpenStreetMap'):
# # extract points of the vehicle
# vehicle_df = df[(df['vehicle_id']==vehicle_id) &
# (df['time'].apply(lambda t: time_difference(t, start_time) >= 0)) &
# (df['time'].apply(lambda t: time_difference(end_time, t) >= 0))
# ].drop_duplicates()
# points = []
# for _, row in vehicle_df.iterrows():
# points.append(Point(row['lat'], row['lon'], row['time']))
# # display trajectory
# m = display_trajectory(points, m=m, tiles=tiles)
# return m
# def display_vehicle_matched_trajectory(vehicle_id, start_time, end_time, df, m=None, tiles='OpenStreetMap'):
# # extract points of the vehicle
# vehicle_df = df[(df['vehicle_id']==vehicle_id) &
# (df['time'].apply(lambda t: time_difference(t, start_time) >= 0)) &
# (df['time'].apply(lambda t: time_difference(end_time, t) >= 0))
# ].drop_duplicates()
# points = []
# for _, row in vehicle_df.iterrows():
# points.append(Point(row['matched_lat'], row['matched_lon'], row['time']))
# # display trajectory
# m = display_trajectory(points, m=m, tiles=tiles)
# return m