-
Notifications
You must be signed in to change notification settings - Fork 20
/
nycbike20160809.py
130 lines (115 loc) · 3.71 KB
/
nycbike20160809.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
# link: https://github.com/hxstarklin/DSAN
import numpy as np
import pandas as pd
import json
import util
outputdir = 'output/NYCBIKE20160809'
util.ensure_dir(outputdir)
dataurl = 'input/NYCBIKE20160809/'
dataname = outputdir+'/NYCBIKE20160809'
data_train = np.load(dataurl + 'bike_train.npz')
data_train_flow = data_train['flow']
data_test = np.load(dataurl + 'bike_test.npz')
data_test_flow = data_test['flow']
def get_geo():
li = []
ind = 0
for x in range(14):
for y in range(8):
li.append([ind, "Polygon", "[]", x, y])
ind += 1
return li
def get_time(x):
if x % 2 == 0:
x = int(x*0.5)
y = int((x - x % 24) / 24 + 1)
if y > 31:
month = 9
day = y - 31
if day < 10:
day_str = '0'+str(day)
else:
day_str = str(day)
else:
month = 8
day = y
if day < 10:
day_str = '0'+str(day)
else:
day_str = str(day)
hour = x % 24
if hour < 10:
hour_str = '0' + str(hour)
else:
hour_str = str(hour)
return '2016-0' + str(month) + '-' + \
day_str + 'T' + hour_str + ':00:00Z'
else:
x = x - 1
x = int(x * 0.5)
y = int((x - x % 24) / 24 + 1)
if y > 31:
month = 9
day = y - 31
if day < 10:
day_str = '0' + str(day)
else:
day_str = str(day)
else:
month = 8
day = y
if day < 10:
day_str = '0' + str(day)
else:
day_str = str(day)
hour = x % 24
if hour < 10:
hour_str = '0' + str(hour)
else:
hour_str = str(hour)
return '2016-0' + str(month) + '-' + \
day_str + 'T' + hour_str + ':30:00Z'
def get_dyna():
ind = 0
l = []
for x in range(14):
for y in range(8):
for time in range(1920):
l.append([ind, "state", get_time(time), x, y,
data_train_flow[time][x][y][0],
data_train_flow[time][x][y][1]])
ind += 1
for time in range(960):
l.append([ind, "state", get_time(time+1920), x, y,
data_test_flow[time][x][y][0],
data_test_flow[time][x][y][1]])
ind += 1
return l
L0 = get_geo()
pd.DataFrame(L0, columns=["geo_id", "type", "coordinates",
"row_id", "column_id"]).\
to_csv(dataname + '.geo', index=None)
L1 = get_dyna()
pd.DataFrame(L1, columns=["dyna_id", "type", "time", "row_id",
"column_id", "inflow", "outflow"]).\
to_csv(dataname + '.grid', index=None)
config = dict()
config['geo'] = dict()
config['geo']['including_types'] = ['Polygon']
config['geo']['Polygon'] = {"row_id": 'num', "column_id": 'num'}
config['grid'] = dict()
config['grid']['including_types'] = ['state']
config['grid']['state'] = {'row_id': 14, 'column_id': 8,
'inflow': 'num', 'outflow': 'num'}
config['info'] = dict()
config['info']['data_col'] = ['inflow', 'outflow']
config['info']['data_files'] = ['NYCBIKE20160809']
config['info']['geo_file'] = 'NYCBIKE20160809'
config['info']['output_dim'] = 2
config['info']['time_intervals'] = 1800
config['info']['init_weight_inf_or_zero'] = 'inf'
config['info']['set_weight_link_or_dist'] = 'dist'
config['info']['calculate_weight_adj'] = False
config['info']['weight_adj_epsilon'] = 0.1
json.dump(config, open(outputdir + '/config.json',
'w', encoding='utf-8'), ensure_ascii=False)