Message subsetting and slicing¶
In this notebook, I will demonstrate, how to identify use strym to get a subset of message satisfying a condition or a combination of conditions. Let’s start with reading a csv file.
[1]:
import signal
import pandas as pd
import sys, math, time, datetime
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style
import numpy as np
from matplotlib.pyplot import cm
import pickle
from strym import strymread
import strym
import copy
/home/ivory/anaconda3/envs/dbn/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
import pandas.util.testing as tm
[3]:
datafolder = "../../PandaData/2020_03_03/"
import glob
csvlist = glob.glob(datafolder+"*.csv")
num_of_files = len(csvlist)
print("Total number of datafiles in {} is {}.".format(datafolder, num_of_files))
dbcfile = '../examples/newToyotacode.dbc'
Total number of datafiles in ../../PandaData/2020_03_03/ is 8.
[4]:
csvlist
[4]:
['../../PandaData/2020_03_03/2020-03-03-15-36-24-479038__CAN_Messages.csv',
'../../PandaData/2020_03_03/2020-03-03-18-29-15-641040__CAN_Messages.csv',
'../../PandaData/2020_03_03/2020-03-03-18-09-36-362663__CAN_Messages.csv',
'../../PandaData/2020_03_03/2020-03-03-15-27-20-702814__CAN_Messages.csv',
'../../PandaData/2020_03_03/2020-03-03-19-57-11-286348__CAN_Messages.csv',
'../../PandaData/2020_03_03/2020-03-03-19-32-39-704415__CAN_Messages.csv',
'../../PandaData/2020_03_03/2020-03-03-10-35-13-966077__CAN_Messages.csv',
'../../PandaData/2020_03_03/2020-03-03-20-15-01-595829__CAN_Messages.csv']
[5]:
r0 = strymread(csvlist[0], dbcfile)
Let’s see how original dataframe for first csvfile looks like
[6]:
r0.dataframe
[6]:
| Time | Buffer | Bus | MessageID | Message | MessageLength | |
|---|---|---|---|---|---|---|
| Clock | ||||||
| 2020-03-03 22:36:24.759860992 | 1.583275e+09 | 0000602c05004ef9130f038018000000 | 0 | 355 | 130f038018 | 5 |
| 2020-03-03 22:36:24.760409594 | 1.583275e+09 | 000080380800eef90000210056004084 | 0 | 452 | 0000210056004084 | 8 |
| 2020-03-03 22:36:24.761665106 | 1.583275e+09 | 00000030180058b4a5fff80000000025 | 1 | 384 | a5fff80000000025 | 8 |
| 2020-03-03 22:36:24.762725592 | 1.583275e+09 | 0000403018004eb6a5fff80000000027 | 1 | 386 | a5fff80000000027 | 8 |
| 2020-03-03 22:36:24.763091087 | 1.583275e+09 | 000060301800cbb6a5fff80000000028 | 1 | 387 | a5fff80000000028 | 8 |
| ... | ... | ... | ... | ... | ... | ... |
| 2020-03-03 23:21:06.934266090 | 1.583278e+09 | 000060500700666b0000000000008c00 | 0 | 643 | 0000000000008c | 7 |
| 2020-03-03 23:21:06.935342073 | 1.583278e+09 | 0000c05c0800606dfff800087fe0004e | 0 | 742 | fff800087fe0004e | 8 |
| 2020-03-03 23:21:06.935601950 | 1.583278e+09 | 0000403a08000f6e9904fa54ffae0073 | 0 | 466 | 9904fa54ffae0073 | 8 |
| 2020-03-03 23:21:06.937319279 | 1.583278e+09 | 000020320800467198fb71300964003b | 0 | 401 | 98fb71300964003b | 8 |
| 2020-03-03 23:21:06.938321590 | 1.583278e+09 | 0000401508000b731a6f1a6f1a6f1a6f | 0 | 170 | 1a6f1a6f1a6f1a6f | 8 |
3942410 rows × 6 columns
[9]:
speed = r0.speed()
strymread.plt_ts(speed, title="Speed")
[10]:
r0.dataframe.shape
[10]:
(3942410, 6)
Lead Vehicle Distance Data¶
I want to look at the lead vehicle’s distance data that will be used for cross-checking of message subsetting.
[13]:
plt.rcParams["figure.figsize"] = (16,5)
headway = r0.get_ts('DSU_CRUISE', "LEAD_DISTANCE")
strymread.plt_ts(headway)
Now lets filter above distance to remove all distance that is equal to 252m. A distance of 252m tells that there was no vehicle detected in the front.
[14]:
headway_filtered = headway[headway['Message'] < 252]
strymread.plt_ts(headway_filtered)
Now we will use strym’s inbuilt function msg_subset to retrieve a subset of original message
Now lets specify conditions for subsetting.¶
Subsetting can be done in following ways: specify elpased-time ranges for which you want all messages, specify a list of particular message id or specify conditions. See the documentation for all options.
[15]:
print(r0.msg_subset.__doc__)
Get the subset of message dataframe based on a condition.
Parameters
-------------
kwargs: variable list of argument in the dictionary format
conditions: `str` | `list<str>`
Human readable condition for subsetting of message dataframe.
Following conditions are available:
- *lead vehicle present*: Extracts only those messages for which there was lead vehicle present.
- *cruise control on*: Extracts only those messages for which cruise control is on.
- *operand op x*: Extracts those messages for which operator `op` is operated on operand to fulfil `x`.
Available operators `op` are `[>,<,==, !=, >=,<=]`
Available operand `operand` are `[speed, acceleration, lead_distance, steering_angle, steering_rate, yaw_rate ]`.
Details of operands are as follows:
- speed: timeseries longitudinal speed of the vehicle
- acceleration: timeseries longitudinal acceleration of the vehicle
- lead_distance: timeseries distance of lead vehicle from the vehicle
- steering_angle: timeseries steering angle of the vehicle
- steering_rate: timeseries steering rate of the vehicle
- yaw_rate: timeseries yaw rate of the vehicle
For example, "speed < 2.3"
time: (t0, t1)
`t0` start elapsed-time
`t1` end elapsed-time
Extracts messages from time `t0` to `t1`. `t0` and `t1` denotes elapsed-time and not the actual time.
ids: `list`
Get message dataframe containing messages given the list `id`
Returns
-----------
`strymread`
Returns strymread object with a modified dataframe attribute
[16]:
r_new = r0.msg_subset(conditions="lead vehicle present")
[19]:
r_new.dataframe.shape
[19]:
(3086577, 6)
[16]:
r0.dataframe.shape
[16]:
(3942410, 6)
Now, to check whether subset has been successful, we will plot (‘DSU_CRUISE’, ‘LEAD_DISTANCE’) and the give plot should give headway distance same as filtered headway distance we saw above. Since strym’s function originally require passing csvfile to get the benefit or all utility and plotting functions, we need to manual set dataframe to this subset dataframe as we didn’t pass inplace=True. In future, we will be able to create strym object with a valid CAN dataframe.
[21]:
dsu = r_new.get_ts('DSU_CRUISE', 'LEAD_DISTANCE')
strymread.plt_ts(dsu)
As you can see, using msg_subset can provide a subset of the message that we previously got by operating on dataframe. However, msg_subset also combines multiple conditions and include human-readable conditions which are easier to understand.
Get the time slices.¶
We can also get the time slices satisfying the given condition. It can be done using function time_subset. Lets see documentation of this function.
[22]:
print(r0.time_subset.__doc__)
Get the time slices satsifying a particular condition for the dataframe.
Parameters
-------------
kwargs: variable list of argument in the dictionary format
conditions: `str` | `list<str>`
Human readable condition for subsetting of message dataframe.
Following conditions are available:
- "lead vehicle present": Extracts only those message for which there was lead vehicle present.
Returns
--------
`list`
A list of tuples with start and end time of slices. E.g. [(t0, t1), (t2, t3), ...] satisfying the given conditions
The timeslices cabe used for further analysis.
[23]:
timeslices = r0.time_subset(conditions="lead vehicle present")
timeslices
[23]:
[[(Timestamp('2020-03-03 22:36:24.787207365'),
Timestamp('2020-03-03 22:37:33.590708017')),
(Timestamp('2020-03-03 22:38:01.589940071'),
Timestamp('2020-03-03 22:38:47.991396666')),
(Timestamp('2020-03-03 22:38:52.591384888'),
Timestamp('2020-03-03 22:39:00.391503572')),
(Timestamp('2020-03-03 22:39:36.792317629'),
Timestamp('2020-03-03 22:39:37.592354774')),
(Timestamp('2020-03-03 22:39:40.392557383'),
Timestamp('2020-03-03 22:39:56.393051624')),
(Timestamp('2020-03-03 22:40:00.192902565'),
Timestamp('2020-03-03 22:40:17.593435049')),
(Timestamp('2020-03-03 22:40:21.793538094'),
Timestamp('2020-03-03 22:40:24.993632317')),
(Timestamp('2020-03-03 22:40:27.393696547'),
Timestamp('2020-03-03 22:46:57.804323435')),
(Timestamp('2020-03-03 22:47:05.204793453'),
Timestamp('2020-03-03 22:48:31.208158493')),
(Timestamp('2020-03-03 22:48:32.007115126'),
Timestamp('2020-03-03 22:51:30.012042046')),
(Timestamp('2020-03-03 22:51:31.412022352'),
Timestamp('2020-03-03 22:51:31.611897945')),
(Timestamp('2020-03-03 22:51:32.211933613'),
Timestamp('2020-03-03 22:59:39.025678635')),
(Timestamp('2020-03-03 22:59:39.825560570'),
Timestamp('2020-03-03 22:59:40.225656509')),
(Timestamp('2020-03-03 22:59:40.825712681'),
Timestamp('2020-03-03 22:59:46.425674438')),
(Timestamp('2020-03-03 22:59:54.425823927'),
Timestamp('2020-03-03 23:00:08.226311445')),
(Timestamp('2020-03-03 23:00:23.226538420'),
Timestamp('2020-03-03 23:00:31.626742601')),
(Timestamp('2020-03-03 23:01:38.629034996'),
Timestamp('2020-03-03 23:10:15.442380190')),
(Timestamp('2020-03-03 23:10:16.642498970'),
Timestamp('2020-03-03 23:10:29.242714167')),
(Timestamp('2020-03-03 23:10:39.443002462'),
Timestamp('2020-03-03 23:11:51.646449804')),
(Timestamp('2020-03-03 23:12:06.646334887'),
Timestamp('2020-03-03 23:13:01.446827888')),
(Timestamp('2020-03-03 23:14:20.048857450'),
Timestamp('2020-03-03 23:14:37.649171114')),
(Timestamp('2020-03-03 23:14:55.849929571'),
Timestamp('2020-03-03 23:15:08.650103807')),
(Timestamp('2020-03-03 23:15:30.250662565'),
Timestamp('2020-03-03 23:15:52.251535891')),
(Timestamp('2020-03-03 23:19:22.256939173'),
Timestamp('2020-03-03 23:20:21.658813238')),
(Timestamp('2020-03-03 23:20:31.859943628'),
Timestamp('2020-03-03 23:20:32.458867073'))]]
Cruise Control ON¶
Lets look at cruise control information
[24]:
acc_on = r0.acc_state(plot = True)
[25]:
rnew_2 = r0.msg_subset(conditions="cruise control on")
[26]:
cruise = rnew_2.acc_state(plot = True)
ID based filtering¶
In the ID based filtering we can filter out message based on particular Message ID and Signal Name/ID
[29]:
r3new = r0.msg_subset(conditions="180.SPEED.Message > 0")
[31]:
speed_subset = r3new.speed()
strymread.plt_ts(speed_subset, title = "Positive Speed")
ID Based filtering to get timeslices¶
[35]:
timeslices = r0.time_subset(conditions=["180.SPEED.Message >= 4", "cruise control on"])
[36]:
timeslices = r0.time_subset(conditions="cruise control on")
[37]:
from itertools import chain
timeslices = list(chain.from_iterable(timeslices))
timeslices
[37]:
[(Timestamp('2020-03-03 22:40:30.250442504'),
Timestamp('2020-03-03 22:46:54.947874308')),
(Timestamp('2020-03-03 22:47:07.301365137'),
Timestamp('2020-03-03 22:47:11.397818565')),
(Timestamp('2020-03-03 22:48:39.183156490'),
Timestamp('2020-03-03 22:51:22.000110626')),
(Timestamp('2020-03-03 22:51:44.094992638'),
Timestamp('2020-03-03 22:52:03.551586390')),
(Timestamp('2020-03-03 22:59:33.474892616'),
Timestamp('2020-03-03 22:59:39.470468760')),
(Timestamp('2020-03-03 22:59:39.658811569'),
Timestamp('2020-03-03 22:59:46.827667952')),
(Timestamp('2020-03-03 22:59:54.273617506'),
Timestamp('2020-03-03 23:00:08.609681845')),
(Timestamp('2020-03-03 23:00:23.142306328'),
Timestamp('2020-03-03 23:00:30.310319901'))]
[38]:
subset_frames = []
for time_frame in timeslices:
sliced_frame = r0.dataframe.loc[time_frame[0]: time_frame[1]]
subset_frames.append(sliced_frame)
[39]:
subset_frames
[39]:
[ Time Buffer \
Clock
2020-03-03 22:40:30.250442504 1.583275e+09 000020730800e2781066003700000000
2020-03-03 22:40:30.250880480 1.583275e+09 0000607708006c790000c28000000000
2020-03-03 22:40:30.251142502 1.583275e+09 000040150800e5793b523b733b583b5a
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2020-03-03 22:40:30.252018452 1.583275e+09 0000e05c08009c7b7d04000000000072
... ... ...
2020-03-03 22:46:54.945369959 1.583276e+09 000040150800adc01f371f311f341f34
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2020-03-03 22:46:54.947874308 1.583276e+09 00002073080077c51026003700000000
Bus MessageID Message MessageLength
Clock
2020-03-03 22:40:30.250442504 0 921 1066003700000000 8
2020-03-03 22:40:30.250880480 0 955 0000c28000000000 8
2020-03-03 22:40:30.251142502 0 170 3b523b733b583b5a 8
2020-03-03 22:40:30.251735926 0 186 01ed00ac 4
2020-03-03 22:40:30.252018452 0 743 7d04000000000072 8
... ... ... ... ...
2020-03-03 22:46:54.945369959 0 170 1f371f311f341f34 8
2020-03-03 22:46:54.945670605 0 180 000000005504b9ce 8
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2020-03-03 22:46:54.946479559 0 812 0000000000000037 8
2020-03-03 22:46:54.947874308 0 921 1026003700000000 8
[553052 rows x 6 columns],
Time Buffer \
Clock
2020-03-03 22:47:07.301365137 1.583276e+09 000020730800acff1026003700000000
2020-03-03 22:47:07.301627398 1.583276e+09 0000404c08002500000800021800008e
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... ... ...
2020-03-03 22:47:11.396260500 1.583276e+09 000080040800853b01fd01f5420280e4
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2020-03-03 22:47:11.397818565 1.583276e+09 000020730800a13e1026003700000000
Bus MessageID Message MessageLength
Clock
2020-03-03 22:47:07.301365137 0 921 1026003700000000 8
2020-03-03 22:47:07.301627398 0 610 000800021800008e 8
2020-03-03 22:47:07.301880360 0 355 13af010000 5
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... ... ... ... ...
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2020-03-03 22:47:11.397078991 0 565 000000000000003f 8
2020-03-03 22:47:11.397513151 0 812 0000000000000037 8
2020-03-03 22:47:11.397818565 0 921 1026003700000000 8
[5900 rows x 6 columns],
Time Buffer \
Clock
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... ... ...
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2020-03-03 22:51:22.000110626 1.583276e+09 0000207308007fba1026003700000000
Bus MessageID Message MessageLength
Clock
2020-03-03 22:48:39.183156490 0 921 1026003700000000 8
2020-03-03 22:48:39.184428215 1 412 05f8008060faf06c 8
2020-03-03 22:48:39.184720039 1 413 0500000660ff6070 8
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... ... ... ... ...
2020-03-03 22:51:21.998913050 0 170 1f261f2f1f2b1f29 8
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[241997 rows x 6 columns],
Time Buffer \
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... ... ...
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Bus MessageID Message MessageLength
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... ... ... ... ...
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[28418 rows x 6 columns],
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Bus MessageID Message MessageLength
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2020-03-03 22:59:33.474892616 0 921 1026003600000000 8
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... ... ... ... ...
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[8383 rows x 6 columns],
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Bus MessageID Message MessageLength
Clock
2020-03-03 22:59:39.658811569 0 921 1026003700000000 8
2020-03-03 22:59:39.659069300 0 1541 0000000000000013 8
2020-03-03 22:59:39.659510851 0 743 7d04000000000072 8
2020-03-03 22:59:39.659811258 0 610 000800021800008e 8
2020-03-03 22:59:39.660312414 1 296 fd8080000834 6
... ... ... ... ...
2020-03-03 22:59:46.826662540 0 170 3bbe3bc63bb43bb4 8
2020-03-03 22:59:46.826917171 0 186 01f800b7 4
2020-03-03 22:59:46.827166319 0 37 0fff0fffe0000029 8
2020-03-03 22:59:46.827418566 0 36 01ff01f9420480ec 8
2020-03-03 22:59:46.827667952 0 921 1026003700000000 8
[10468 rows x 6 columns],
Time Buffer \
Clock
2020-03-03 22:59:54.273617506 1.583276e+09 000020730800e21e1026003700000000
2020-03-03 22:59:54.275180101 1.583276e+09 0000807f08000a220000060004005fa8
2020-03-03 22:59:54.276608229 1.583276e+09 000040150800e9243b883b8b3b943b7f
2020-03-03 22:59:54.277416229 1.583276e+09 00000045040062260000002e003fffff
2020-03-03 22:59:54.278011560 1.583276e+09 00006048180012546000100100100100
... ... ...
2020-03-03 23:00:08.607620239 1.583276e+09 000080650800fe730000000000000037
2020-03-03 23:00:08.607867956 1.583276e+09 000080040800ec7401fd0200420180ef
2020-03-03 23:00:08.608118534 1.583276e+09 0000203208006875b1ffe9320064fac3
2020-03-03 23:00:08.609238386 1.583276e+09 0000606808005777fffd43000000008d
2020-03-03 23:00:08.609681845 1.583276e+09 00002073080020781026003700000000
Bus MessageID Message MessageLength
Clock
2020-03-03 22:59:54.273617506 0 921 1026003700000000 8
2020-03-03 22:59:54.275180101 0 1020 0000060004005fa8 8
2020-03-03 22:59:54.276608229 0 170 3b883b8b3b943b7f 8
2020-03-03 22:59:54.277416229 0 552 0000002e 4
2020-03-03 22:59:54.278011560 1 579 6000100100100100 8
... ... ... ... ...
2020-03-03 23:00:08.607620239 0 812 0000000000000037 8
2020-03-03 23:00:08.607867956 0 36 01fd0200420180ef 8
2020-03-03 23:00:08.608118534 0 401 b1ffe9320064fac3 8
2020-03-03 23:00:08.609238386 0 835 fffd43000000008d 8
2020-03-03 23:00:08.609681845 0 921 1026003700000000 8
[22216 rows x 6 columns],
Time Buffer \
Clock
2020-03-03 23:00:23.142306328 1.583276e+09 00002073080060511026003700000000
2020-03-03 23:00:23.142556906 1.583276e+09 0000e0321800917a04000000000000a4
2020-03-03 23:00:23.142806768 1.583276e+09 0000003318000e7b04fa000260fc706d
2020-03-03 23:00:23.143601894 1.583276e+09 000020331800857c04fc2f026002a0d5
2020-03-03 23:00:23.143851757 1.583276e+09 000040331800fd7c04ca005060fd607e
... ... ...
2020-03-03 23:00:30.307449818 1.583276e+09 00008004080046f802000205420480fb
2020-03-03 23:00:30.307700872 1.583276e+09 0000a0040800c2f800000fffe000001b
2020-03-03 23:00:30.308242083 1.583276e+09 0000a0460800f8f9000000000000003f
2020-03-03 23:00:30.308512211 1.583276e+09 00008065080075fa0000000000000037
2020-03-03 23:00:30.310319901 1.583276e+09 0000207308000afe1026003700000000
Bus MessageID Message MessageLength
Clock
2020-03-03 23:00:23.142306328 0 921 1026003700000000 8
2020-03-03 23:00:23.142556906 1 407 04000000000000a4 8
2020-03-03 23:00:23.142806768 1 408 04fa000260fc706d 8
2020-03-03 23:00:23.143601894 1 409 04fc2f026002a0d5 8
2020-03-03 23:00:23.143851757 1 410 04ca005060fd607e 8
... ... ... ... ...
2020-03-03 23:00:30.307449818 0 36 02000205420480fb 8
2020-03-03 23:00:30.307700872 0 37 00000fffe000001b 8
2020-03-03 23:00:30.308242083 0 565 000000000000003f 8
2020-03-03 23:00:30.308512211 0 812 0000000000000037 8
2020-03-03 23:00:30.310319901 0 921 1026003700000000 8
[10222 rows x 6 columns]]
We can also visualize time slices on time-axis.¶
[42]:
fig, ax = strymread.create_fig(1)
for slices in timeslices:
ax[0].scatter([slices[0], slices[1]], [0, 0], marker = '*')
ax[0].plot([slices[0], slices[1]], [0, 0], linewidth=2.0)
ax[0].set_xlim([np.min(timeslices)-0.1*(np.max(timeslices)-np.min(timeslices)), np.max(timeslices)+0.1*(np.max(timeslices)-np.min(timeslices))])
ax[0].set_ylim([-0.0001, 0.0001])
plt.yticks([])
plt.show()
