In [1]:
%reset
Once deleted, variables cannot be recovered. Proceed (y/[n])? y
In [1]:
%matplotlib inline
In [2]:
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import csv
import math
import matplotlib.pyplot as plt
from scipy.signal import hilbert, chirp
import scipy
In [3]:
c_dataset = ['Timestamp','vIDa', 'vIDb', 'dist_ab', 'v_a_Type', 'v_a_Vel', 'v_a_Lane', 'v_a_Pred', 'v_a_Foll', 'v_count', 'v_mean_vel',
'delta_vel']
dataset = pd.read_table('D:\\zzzLola\\PhD\\DataSet\\US101\\test\\dataset.txt', sep='\t', header=None, names=c_dataset)
In [4]:
dataset.groupby(['vIDa']).mean()
Out[4]:
Timestamp
vIDb
dist_ab
v_a_Type
v_a_Vel
v_a_Lane
v_a_Pred
v_a_Foll
v_count
v_mean_vel
delta_vel
vIDa
1073
1.118847e+12
1176.158038
336.358825
2
18.189096
2.000000
0.000000
1083.000000
22
18.188940
1.560187e-04
1077
1.118847e+12
1174.572650
351.493100
2
19.986171
1.000000
0.000000
1082.000000
7
19.986171
1.312852e-10
1080
1.118847e+12
1176.740763
332.343251
2
18.027473
3.000000
0.000000
1084.000000
28
18.027505
-3.207551e-05
1081
1.118847e+12
1175.083436
344.489554
2
18.011786
5.000000
0.000000
1092.000000
14
18.011721
6.518059e-05
1082
1.118847e+12
1177.113718
326.420286
2
19.721311
1.000000
243.462048
1086.000000
31
19.721347
-3.530707e-05
1083
1.118847e+12
1180.003564
309.916909
2
17.496698
2.000000
427.171393
1088.000000
55
17.503389
-6.691370e-03
1084
1.118847e+12
1178.884708
317.103805
2
17.379457
3.000000
655.607044
1102.826506
46
17.379100
3.573666e-04
1086
1.118847e+12
1179.357192
309.692766
2
19.530572
1.000000
668.044649
1091.000000
50
19.529999
5.725960e-04
1087
1.118847e+12
1174.835597
345.337665
2
18.544505
4.000000
0.000000
1089.000000
12
18.544540
-3.522240e-05
1088
1.118847e+12
1181.950595
296.880684
2
17.375434
2.000000
840.076812
1094.000000
71
17.374931
5.029661e-04
1089
1.118847e+12
1176.663817
329.464889
2
18.974919
4.000000
464.339847
1096.302290
28
18.975433
-5.141714e-04
1090
1.118847e+12
1181.444812
300.054514
2
17.676650
4.000000
786.464799
1097.089370
67
17.674215
2.435672e-03
1091
1.118847e+12
1180.676076
296.935768
2
20.401889
1.000000
890.532141
1093.000000
61
20.401313
5.752095e-04
1092
1.118847e+12
1178.598604
315.124797
2
19.951022
5.000000
341.677332
1109.248012
44
19.950615
4.070529e-04
1093
1.118847e+12
1182.652550
285.658860
2
19.236542
1.000000
865.542794
1099.000000
77
19.232999
3.543240e-03
1094
1.118847e+12
1186.487934
271.106816
2
17.017484
2.000000
716.122478
1101.000000
108
17.016674
8.108481e-04
1095
1.118847e+12
1183.023489
288.860202
2
17.889427
3.000000
828.720265
1102.434551
80
17.886426
3.001300e-03
1096
1.118847e+12
1186.350691
273.688334
2
16.699546
4.000000
963.838352
1100.000000
107
16.700106
-5.603965e-04
1097
1.118847e+12
1183.789981
287.317719
2
16.835796
4.000000
851.155054
1096.899911
86
16.837896
-2.100595e-03
1098
1.118847e+12
1179.003551
310.252875
2
20.041814
4.123402
633.622159
1092.591442
48
20.045108
-3.294604e-03
1099
1.118847e+12
1185.488155
274.184957
2
17.908793
1.000000
843.142051
1104.000000
100
17.909591
-7.981808e-04
1100
1.118847e+12
1187.867557
265.886711
2
16.454565
4.000000
985.636429
1105.000000
119
16.457202
-2.636652e-03
1101
1.118847e+12
1188.506788
261.443867
2
16.938267
2.000000
952.867893
1106.451520
124
16.939407
-1.140484e-03
1102
1.118847e+12
1184.739399
277.704934
2
17.841296
3.221838
1002.712739
1104.559782
94
17.841792
-4.964854e-04
1103
1.118847e+12
1180.447791
296.310159
1
21.299586
3.016622
830.925131
1095.033243
60
21.299068
5.171151e-04
1104
1.118847e+12
1186.527673
265.529468
1
18.181508
1.000000
1007.416667
1109.000000
109
18.182550
-1.042573e-03
1105
1.118847e+12
1188.869793
259.364821
2
16.584718
4.000000
1030.569948
1108.000000
127
16.587216
-2.498125e-03
1106
1.118847e+12
1189.884793
254.123895
2
16.835398
2.073912
1012.452974
1114.392932
135
16.835120
2.779860e-04
1107
1.118847e+12
1186.502038
264.902863
2
18.379422
3.000000
808.688774
1112.000000
109
18.382879
-3.457168e-03
1108
1.118847e+12
1190.896358
252.430064
2
16.160598
4.000000
982.082710
1113.417499
143
16.158833
1.764443e-03
...
...
...
...
...
...
...
...
...
...
...
...
1518
1.118847e+12
1438.973023
245.949595
2
9.053011
4.000000
1510.000000
1249.514408
89
9.053382
-3.712866e-04
1519
1.118847e+12
1431.981276
248.663379
2
4.778391
2.000000
1515.000000
1304.468712
126
4.763710
1.468134e-02
1520
1.118847e+12
1437.863722
255.192922
2
6.178601
3.000000
1509.000000
1188.042525
95
6.181504
-2.903646e-03
1521
1.118847e+12
1419.007238
239.775677
2
4.632319
1.000000
1514.000000
1321.563309
195
4.622097
1.022280e-02
1522
1.118847e+12
1441.765603
253.410713
2
8.198611
4.000000
1518.000000
1230.185106
73
8.202335
-3.723935e-03
1523
1.118847e+12
1441.586087
259.764590
2
6.351451
3.000000
1520.000000
1034.049148
74
6.359281
-7.830541e-03
1524
1.118847e+12
1423.902250
243.030526
2
4.760581
1.000000
1521.000000
1308.173728
170
4.751599
8.981807e-03
1525
1.118847e+12
1444.179474
258.944555
2
8.608584
4.000000
1522.000000
1163.026055
59
8.611065
-2.480959e-03
1526
1.118847e+12
1435.289790
253.017573
2
4.441942
2.000000
1519.000000
1276.030694
108
4.432356
9.585380e-03
1527
1.118847e+12
1449.799759
270.388097
2
9.281081
5.000000
1517.000000
236.446182
26
9.280926
1.556498e-04
1528
1.118847e+12
1446.649696
266.618338
2
7.641552
4.000000
1525.000000
783.190964
45
7.640049
1.502491e-03
1529
1.118847e+12
1428.265189
248.602114
2
4.705480
1.000000
1524.000000
1160.112501
146
4.700726
4.754518e-03
1530
1.118847e+12
1454.443580
276.525135
2
7.598664
5.000000
1527.000000
0.000000
4
7.598664
0.000000e+00
1531
1.118847e+12
1450.291101
272.786228
2
7.101546
4.000000
1528.000000
602.608696
23
7.099190
2.356402e-03
1533
1.118847e+12
1434.647541
254.371196
2
4.784076
1.000000
1529.000000
1065.892812
111
4.784426
-3.505785e-04
1534
1.118847e+12
1438.664443
255.924714
2
4.296319
2.000000
1526.000000
1196.695886
90
4.290500
5.819024e-03
1535
1.118847e+12
1445.726605
268.911686
2
4.950514
3.000000
1523.000000
706.909545
50
4.950684
-1.696328e-04
1536
1.118847e+12
1453.006061
277.373880
2
5.055740
4.000000
1531.000000
0.000000
9
5.054261
1.478698e-03
1537
1.118847e+12
1450.244226
274.523406
2
4.804381
3.000000
1535.000000
470.264946
23
4.803648
7.330109e-04
1538
1.118847e+12
1442.159078
262.050999
2
4.629474
2.000000
1534.000000
947.617945
70
4.623206
6.267215e-03
1539
1.118847e+12
1440.948311
259.122453
2
5.283104
1.000000
1533.000000
1002.948513
77
5.279571
3.532275e-03
1540
1.118847e+12
1453.497219
278.248090
2
4.115444
3.000000
1537.000000
0.000000
7
4.115671
-2.266748e-04
1548
1.118847e+12
1446.781779
269.920113
2
5.286283
2.000000
1538.000000
361.871795
43
5.286862
-5.794057e-04
1549
1.118847e+12
1445.617247
266.942912
2
5.556044
1.000000
1539.000000
899.965318
50
5.555651
3.934544e-04
1551
1.118847e+12
1452.569758
276.168573
2
7.566355
2.000000
1548.000000
0.000000
10
7.566660
-3.052751e-04
1554
1.118847e+12
1449.055571
271.993761
2
5.981515
1.000000
1549.000000
108.294578
29
5.983960
-2.444314e-03
1556
1.118847e+12
1454.635659
278.072693
2
6.099048
1.000000
1554.000000
0.000000
2
6.099048
0.000000e+00
3106
1.118847e+12
1195.385831
207.576778
2
14.208946
5.000000
1024.853330
1157.000000
275
14.191488
1.745828e-02
3107
1.118847e+12
1189.892269
181.377341
2
16.075497
5.717336
825.210781
733.088515
227
16.053400
2.209694e-02
3108
1.118847e+12
1237.683229
207.850782
1
17.963004
1.744325
1174.208829
1220.368693
359
17.934143
2.886041e-02
369 rows × 11 columns
In [5]:
t_max = dataset['Timestamp'].max()
t_min = dataset['Timestamp'].min()
In [6]:
axes = plt.gca()
axes.set_xlim([t_min,t_max])
Out[6]:
(1118847300000.0, 1118847399900.0)
In [7]:
vID_group = dataset.groupby(['vIDa'])
In [8]:
vID_info = vID_group['Timestamp', 'v_a_Vel']
In [ ]:
In [ ]:
In [9]:
plt.figure()
for i, group in dataset.groupby(['vIDa']):
if i == 1280 or i == 1274:
group.plot(x='Timestamp', y='v_a_Vel', title=str(i))
<matplotlib.figure.Figure at 0x98a46a0>
In [10]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['vIDa']):
#if i < 1200:
#print i
ax.plot(group.Timestamp, group.v_a_Vel, label = str(i))
#ax.legend()
plt.show()
In [11]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['vIDa']):
#if i < 1200:
#print i
ax.plot(group.Timestamp, group.delta_vel, label = str(i))
#ax.legend()
plt.show()
In [12]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['vIDa']):
if i < 1085:
#print i
ax.plot(group.Timestamp, group.v_a_Vel, label = str(i))
ax.legend()
plt.show()
In [13]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['vIDa']):
if i == 1280 or i == 1274:
#print i
ax.plot(group.Timestamp, group.delta_vel, label = str(i))
ax.legend()
plt.show()
In [14]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['vIDa']):
if i == 1280 or i == 1274:
#print i
ax.plot(group.Timestamp, group.v_a_Vel, label = str(i))
ax.legend()
plt.show()
In [15]:
dataset['delta_vel'].describe()
Out[15]:
count 15450296.000000
mean 0.028567
std 3.128589
min -15.367954
25% -1.959090
50% -0.211304
75% 2.004130
max 16.426508
Name: delta_vel, dtype: float64
In [16]:
dataset[:10]
Out[16]:
Timestamp
vIDa
vIDb
dist_ab
v_a_Type
v_a_Vel
v_a_Lane
v_a_Pred
v_a_Foll
v_count
v_mean_vel
delta_vel
0
1.118847e+12
1073
1077
26.760834
2
18.263616
2
0
1083
22
18.18894
0.074676
1
1.118847e+12
1073
1080
9.739521
2
18.263616
2
0
1083
22
18.18894
0.074676
2
1.118847e+12
1073
1081
19.198605
2
18.263616
2
0
1083
22
18.18894
0.074676
3
1.118847e+12
1073
1082
20.679750
2
18.263616
2
0
1083
22
18.18894
0.074676
4
1.118847e+12
1073
1083
56.396881
2
18.263616
2
0
1083
22
18.18894
0.074676
5
1.118847e+12
1073
1084
40.510313
2
18.263616
2
0
1083
22
18.18894
0.074676
6
1.118847e+12
1073
1086
56.592865
2
18.263616
2
0
1083
22
18.18894
0.074676
7
1.118847e+12
1073
1087
19.093697
2
18.263616
2
0
1083
22
18.18894
0.074676
8
1.118847e+12
1073
1088
83.171880
2
18.263616
2
0
1083
22
18.18894
0.074676
9
1.118847e+12
1073
1089
15.309671
2
18.263616
2
0
1083
22
18.18894
0.074676
In [17]:
dataset.groupby(['v_a_Lane','vIDa'])
Out[17]:
<pandas.core.groupby.DataFrameGroupBy object at 0x00000000455A4E80>
In [18]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['v_a_Lane','vIDa']):
if i[0] == 1:
#print i[0]
ax.plot(group.Timestamp, group.v_a_Vel, label = str(i[1]))
#ax.legend()
plt.show()
In [19]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['v_a_Lane','vIDa']):
if i[0] == 2:
#print i[0]
ax.plot(group.Timestamp, group.v_a_Vel, label = str(i[1]))
#ax.legend()
plt.show()
In [20]:
dataset[dataset['v_a_Vel'] == 0]
Out[20]:
Timestamp
vIDa
vIDb
dist_ab
v_a_Type
v_a_Vel
v_a_Lane
v_a_Pred
v_a_Foll
v_count
v_mean_vel
delta_vel
1068977
1.118847e+12
1149
1094
237.260602
3
0
4
1136
1156
245
15.367954
-15.367954
1068978
1.118847e+12
1149
1096
240.314179
3
0
4
1136
1156
245
15.367954
-15.367954
1068979
1.118847e+12
1149
1099
253.064879
3
0
4
1136
1156
245
15.367954
-15.367954
1068980
1.118847e+12
1149
1100
217.963162
3
0
4
1136
1156
245
15.367954
-15.367954
1068981
1.118847e+12
1149
1101
208.736876
3
0
4
1136
1156
245
15.367954
-15.367954
1068982
1.118847e+12
1149
1104
233.741339
3
0
4
1136
1156
245
15.367954
-15.367954
1068983
1.118847e+12
1149
1105
201.264804
3
0
4
1136
1156
245
15.367954
-15.367954
1068984
1.118847e+12
1149
1106
187.050502
3
0
4
1136
1156
245
15.367954
-15.367954
1068985
1.118847e+12
1149
1107
233.327525
3
0
4
1136
1156
245
15.367954
-15.367954
1068986
1.118847e+12
1149
1108
173.078704
3
0
4
1136
1156
245
15.367954
-15.367954
1068987
1.118847e+12
1149
1109
172.407384
3
0
4
1136
1156
245
15.367954
-15.367954
1068988
1.118847e+12
1149
1110
182.501836
3
0
4
1136
1156
245
15.367954
-15.367954
1068989
1.118847e+12
1149
1112
209.813489
3
0
4
1136
1156
245
15.367954
-15.367954
1068990
1.118847e+12
1149
1113
154.089308
3
0
4
1136
1156
245
15.367954
-15.367954
1068991
1.118847e+12
1149
1114
126.673516
3
0
4
1136
1156
245
15.367954
-15.367954
1068992
1.118847e+12
1149
1115
139.483761
3
0
4
1136
1156
245
15.367954
-15.367954
1068993
1.118847e+12
1149
1116
123.819156
3
0
4
1136
1156
245
15.367954
-15.367954
1068994
1.118847e+12
1149
1119
99.663989
3
0
4
1136
1156
245
15.367954
-15.367954
1068995
1.118847e+12
1149
1120
112.472144
3
0
4
1136
1156
245
15.367954
-15.367954
1068996
1.118847e+12
1149
1121
104.223314
3
0
4
1136
1156
245
15.367954
-15.367954
1068997
1.118847e+12
1149
1122
134.936776
3
0
4
1136
1156
245
15.367954
-15.367954
1068998
1.118847e+12
1149
1123
102.117103
3
0
4
1136
1156
245
15.367954
-15.367954
1068999
1.118847e+12
1149
1124
82.354897
3
0
4
1136
1156
245
15.367954
-15.367954
1069000
1.118847e+12
1149
1125
99.893285
3
0
4
1136
1156
245
15.367954
-15.367954
1069001
1.118847e+12
1149
1126
77.957393
3
0
4
1136
1156
245
15.367954
-15.367954
1069002
1.118847e+12
1149
1127
71.433854
3
0
4
1136
1156
245
15.367954
-15.367954
1069003
1.118847e+12
1149
1128
81.405594
3
0
4
1136
1156
245
15.367954
-15.367954
1069004
1.118847e+12
1149
1129
51.286725
3
0
4
1136
1156
245
15.367954
-15.367954
1069005
1.118847e+12
1149
1130
56.599997
3
0
4
1136
1156
245
15.367954
-15.367954
1069006
1.118847e+12
1149
1131
55.395381
3
0
4
1136
1156
245
15.367954
-15.367954
...
...
...
...
...
...
...
...
...
...
...
...
...
15300609
1.118847e+12
1534
1512
48.478214
2
0
2
1526
1538
90
4.290500
-4.290500
15300610
1.118847e+12
1534
1513
60.927394
2
0
2
1526
1538
90
4.290500
-4.290500
15300611
1.118847e+12
1534
1514
67.609037
2
0
2
1526
1538
90
4.290500
-4.290500
15300612
1.118847e+12
1534
1515
39.614347
2
0
2
1526
1538
90
4.290500
-4.290500
15300613
1.118847e+12
1534
1517
28.936789
2
0
2
1526
1538
90
4.290500
-4.290500
15300614
1.118847e+12
1534
1518
39.147748
2
0
2
1526
1538
90
4.290500
-4.290500
15300615
1.118847e+12
1534
1519
18.890338
2
0
2
1526
1538
90
4.290500
-4.290500
15300616
1.118847e+12
1534
1520
19.071413
2
0
2
1526
1538
90
4.290500
-4.290500
15300617
1.118847e+12
1534
1521
50.233681
2
0
2
1526
1538
90
4.290500
-4.290500
15300618
1.118847e+12
1534
1522
21.182585
2
0
2
1526
1538
90
4.290500
-4.290500
15300619
1.118847e+12
1534
1523
8.323210
2
0
2
1526
1538
90
4.290500
-4.290500
15300620
1.118847e+12
1534
1524
40.812893
2
0
2
1526
1538
90
4.290500
-4.290500
15300621
1.118847e+12
1534
1525
11.991253
2
0
2
1526
1538
90
4.290500
-4.290500
15300622
1.118847e+12
1534
1526
8.723656
2
0
2
1526
1538
90
4.290500
-4.290500
15300623
1.118847e+12
1534
1527
19.403340
2
0
2
1526
1538
90
4.290500
-4.290500
15300624
1.118847e+12
1534
1528
9.580541
2
0
2
1526
1538
90
4.290500
-4.290500
15300625
1.118847e+12
1534
1529
28.166759
2
0
2
1526
1538
90
4.290500
-4.290500
15300626
1.118847e+12
1534
1530
37.384098
2
0
2
1526
1538
90
4.290500
-4.290500
15300627
1.118847e+12
1534
1531
23.779995
2
0
2
1526
1538
90
4.290500
-4.290500
15300628
1.118847e+12
1534
1533
13.091411
2
0
2
1526
1538
90
4.290500
-4.290500
15300629
1.118847e+12
1534
1535
15.120746
2
0
2
1526
1538
90
4.290500
-4.290500
15300630
1.118847e+12
1534
1536
34.844641
2
0
2
1526
1538
90
4.290500
-4.290500
15300631
1.118847e+12
1534
1537
27.416507
2
0
2
1526
1538
90
4.290500
-4.290500
15300632
1.118847e+12
1534
1538
7.680274
2
0
2
1526
1538
90
4.290500
-4.290500
15300633
1.118847e+12
1534
1539
3.656987
2
0
2
1526
1538
90
4.290500
-4.290500
15300634
1.118847e+12
1534
1540
36.581226
2
0
2
1526
1538
90
4.290500
-4.290500
15300635
1.118847e+12
1534
1548
17.940446
2
0
2
1526
1538
90
4.290500
-4.290500
15300636
1.118847e+12
1534
1549
13.299185
2
0
2
1526
1538
90
4.290500
-4.290500
15300637
1.118847e+12
1534
1551
31.843265
2
0
2
1526
1538
90
4.290500
-4.290500
15300638
1.118847e+12
1534
1554
22.810777
2
0
2
1526
1538
90
4.290500
-4.290500
17250 rows × 12 columns
In [21]:
vel0 = dataset[dataset['v_a_Vel'] == 0]
In [22]:
vel0[:10]
Out[22]:
Timestamp
vIDa
vIDb
dist_ab
v_a_Type
v_a_Vel
v_a_Lane
v_a_Pred
v_a_Foll
v_count
v_mean_vel
delta_vel
1068977
1.118847e+12
1149
1094
237.260602
3
0
4
1136
1156
245
15.367954
-15.367954
1068978
1.118847e+12
1149
1096
240.314179
3
0
4
1136
1156
245
15.367954
-15.367954
1068979
1.118847e+12
1149
1099
253.064879
3
0
4
1136
1156
245
15.367954
-15.367954
1068980
1.118847e+12
1149
1100
217.963162
3
0
4
1136
1156
245
15.367954
-15.367954
1068981
1.118847e+12
1149
1101
208.736876
3
0
4
1136
1156
245
15.367954
-15.367954
1068982
1.118847e+12
1149
1104
233.741339
3
0
4
1136
1156
245
15.367954
-15.367954
1068983
1.118847e+12
1149
1105
201.264804
3
0
4
1136
1156
245
15.367954
-15.367954
1068984
1.118847e+12
1149
1106
187.050502
3
0
4
1136
1156
245
15.367954
-15.367954
1068985
1.118847e+12
1149
1107
233.327525
3
0
4
1136
1156
245
15.367954
-15.367954
1068986
1.118847e+12
1149
1108
173.078704
3
0
4
1136
1156
245
15.367954
-15.367954
In [23]:
vel0.groupby(['vIDa']).count()
Out[23]:
Timestamp
vIDb
dist_ab
v_a_Type
v_a_Vel
v_a_Lane
v_a_Pred
v_a_Foll
v_count
v_mean_vel
delta_vel
vIDa
1149
117
117
117
117
117
117
117
117
117
117
117
1274
240
240
240
240
240
240
240
240
240
240
240
1328
385
385
385
385
385
385
385
385
385
385
385
1347
1561
1561
1561
1561
1561
1561
1561
1561
1561
1561
1561
1389
3281
3281
3281
3281
3281
3281
3281
3281
3281
3281
3281
1397
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
3507
1431
4192
4192
4192
4192
4192
4192
4192
4192
4192
4192
4192
1443
2559
2559
2559
2559
2559
2559
2559
2559
2559
2559
2559
1471
129
129
129
129
129
129
129
129
129
129
129
1534
1279
1279
1279
1279
1279
1279
1279
1279
1279
1279
1279
In [24]:
vel0['Timestamp'].max()
Out[24]:
1118847399700.0
In [25]:
vel0['Timestamp'].min()
Out[25]:
1118847309500.0
In [26]:
v1149 = dataset['vIDa'] == 1149
v0 = dataset['v_a_Vel'] < 5
d0 = dataset['dist_ab'] < 50
dataset[v1149 & v0 & d0]
Out[26]:
Timestamp
vIDa
vIDb
dist_ab
v_a_Type
v_a_Vel
v_a_Lane
v_a_Pred
v_a_Foll
v_count
v_mean_vel
delta_vel
1068655
1.118847e+12
1149
1129
46.787493
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068658
1.118847e+12
1149
1132
27.870894
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068660
1.118847e+12
1149
1134
27.055387
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068661
1.118847e+12
1149
1135
14.952611
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068662
1.118847e+12
1149
1136
49.334503
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068663
1.118847e+12
1149
1137
5.232251
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068664
1.118847e+12
1149
1138
12.629167
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068665
1.118847e+12
1149
1143
18.942788
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068666
1.118847e+12
1149
1144
16.847638
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068667
1.118847e+12
1149
1145
16.047411
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068668
1.118847e+12
1149
1146
22.298351
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068669
1.118847e+12
1149
1147
29.934643
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068670
1.118847e+12
1149
1148
33.758819
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068671
1.118847e+12
1149
1150
18.959026
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068672
1.118847e+12
1149
1152
41.156080
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068675
1.118847e+12
1149
1155
47.614436
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068742
1.118847e+12
1149
3106
33.656140
3
3.233928
4
1136
1156
245
15.367954
-12.134026
1068772
1.118847e+12
1149
1129
48.089707
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068775
1.118847e+12
1149
1132
29.358039
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068777
1.118847e+12
1149
1134
28.235367
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068778
1.118847e+12
1149
1135
15.605895
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068780
1.118847e+12
1149
1137
4.868633
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068781
1.118847e+12
1149
1138
13.537518
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068782
1.118847e+12
1149
1143
18.069284
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068783
1.118847e+12
1149
1144
15.827718
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068784
1.118847e+12
1149
1145
15.057941
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068785
1.118847e+12
1149
1146
23.689432
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068786
1.118847e+12
1149
1147
28.770616
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068787
1.118847e+12
1149
1148
32.865926
3
1.173480
4
1136
1156
245
15.367954
-14.194474
1068788
1.118847e+12
1149
1150
17.774308
3
1.173480
4
1136
1156
245
15.367954
-14.194474
...
...
...
...
...
...
...
...
...
...
...
...
...
1069007
1.118847e+12
1149
1132
32.937467
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069009
1.118847e+12
1149
1134
31.322200
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069010
1.118847e+12
1149
1135
17.552440
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069012
1.118847e+12
1149
1137
4.750929
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069013
1.118847e+12
1149
1138
16.041542
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069014
1.118847e+12
1149
1143
15.944823
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069015
1.118847e+12
1149
1144
13.222572
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069016
1.118847e+12
1149
1145
11.528343
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069017
1.118847e+12
1149
1146
27.113989
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069018
1.118847e+12
1149
1147
25.847682
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069019
1.118847e+12
1149
1148
30.456780
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069020
1.118847e+12
1149
1150
14.809209
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069021
1.118847e+12
1149
1152
37.799782
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069024
1.118847e+12
1149
1155
43.729000
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069092
1.118847e+12
1149
3106
29.969189
3
0.000000
4
1136
1156
245
15.367954
-15.367954
1069124
1.118847e+12
1149
1132
34.317538
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069126
1.118847e+12
1149
1134
32.534508
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069127
1.118847e+12
1149
1135
18.347134
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069129
1.118847e+12
1149
1137
5.042416
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069130
1.118847e+12
1149
1138
16.998561
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069131
1.118847e+12
1149
1143
15.259444
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069132
1.118847e+12
1149
1144
12.344703
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069133
1.118847e+12
1149
1145
10.104892
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069134
1.118847e+12
1149
1146
28.367865
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069135
1.118847e+12
1149
1147
24.814216
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069136
1.118847e+12
1149
1148
29.680068
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069137
1.118847e+12
1149
1150
13.720436
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069138
1.118847e+12
1149
1152
36.920495
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069141
1.118847e+12
1149
1155
42.713298
3
2.225040
4
1136
1156
245
15.367954
-13.142914
1069209
1.118847e+12
1149
3106
29.052431
3
2.225040
4
1136
1156
245
15.367954
-13.142914
79 rows × 12 columns
In [27]:
v1137 = dataset['vIDa'] == 1137
vIDb_1137 = dataset['vIDb'] == 1149
distvv = dataset['dist_ab'] == 15.973205
dataset[v1137 & vIDb_1137 & distvv]
Out[27]:
Timestamp
vIDa
vIDb
dist_ab
v_a_Type
v_a_Vel
v_a_Lane
v_a_Pred
v_a_Foll
v_count
v_mean_vel
delta_vel
In [28]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['v_a_Lane','vIDa']):
if i[1] == 1149:
#print i[0]
ax.plot(group.Timestamp, group.v_a_Vel, label = str(i[0]))
ax.legend()
plt.show()
In [29]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['v_a_Lane','vIDa']):
if i[1] == 1274:
#print i[0]
ax.plot(group.Timestamp, group.v_a_Vel, label = str(i[0]))
ax.legend()
plt.show()
In [30]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['v_a_Lane','vIDa']):
if i[1] == 1328:
#print i[0]
ax.plot(group.Timestamp, group.v_a_Vel, label = str(i[0]))
ax.legend()
plt.show()
In [31]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['v_a_Lane','vIDa']):
if i[1] == 1347:
#print i[0]
ax.plot(group.Timestamp, group.v_a_Vel, label = str(i[0]))
ax.legend()
plt.show()
In [32]:
fig, ax = plt.subplots()
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling
for i, group in dataset.groupby(['v_a_Lane','vIDa']):
if i[1] == 1389:
#print i[0]
ax.plot(group.Timestamp, group.v_a_Vel, label = str(i[0]))
ax.legend()
plt.show()
In [ ]:
Content source: lalonica/PhD
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