Data Throughput of Honda Pilot Data¶

[1]:

from strym import strymread
import strym
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sea

/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

Loading BokehJS ...

[2]:

data = "../../PandaData/2020_04_30/2020-04-30-09-16-18_5FNYF6H05HB089022_CAN_Messages.csv"
dbcfile= "../examples/honda_pilot_touring_2017_can_generated.dbc"

[3]:

r = strymread(csvfile=data, dbcfile=dbcfile)

[4]:

r.count(plot = True)

_images/HondaPilotDataThroughput_4_0.png

[4]:

	MessageID	Counts_Bus_0	Counts_Bus_1	TotalCount
57	57	6374	0	6374
145	145	25496	0	25496
228	228	25495	0	25495
229	229	25496	0	25496
308	308	25496	0	25496
...	...	...	...	...
1612	1612	255	0	255
1613	1613	255	0	255
1616	1616	255	0	255
1618	1618	255	0	255
1668	1668	255	0	255

106 rows × 4 columns

[5]:

r.frequency()

[5]:

	MessageID	MeanRate	MedianRate	RateStd	MaxRate	MinRate	RateIQR
0	57	25.745966	25.000024	12.872444	329.300777	23.903527	0.248166
1	145	103.658655	100.019173	63.189448	2289.467249	36.796982	4.471483
2	228	103.991301	100.009633	69.621096	2872.810959	32.283992	4.390886
3	229	104.128671	99.978642	73.554050	2493.640904	32.460136	4.482534
4	308	104.177729	99.981025	74.794076	3095.427306	32.408719	4.246725
...	...	...	...	...	...	...	...
101	1612	1.000403	1.000039	0.005487	1.087484	0.998178	0.000392
102	1613	1.000725	1.000012	0.011486	1.183240	0.996545	0.000510
103	1616	1.000811	1.000080	0.012512	1.198342	0.995939	0.002005
104	1618	1.001128	0.999994	0.017985	1.287079	0.997487	0.000519
105	1668	1.003400	1.001523	0.027873	1.446203	0.998711	0.001350

106 rows × 7 columns

Let’s look at a few Messages¶

[6]:

msgs464_1 = r.get_ts(464,1)

[7]:

msgs464_1

[7]:

	Time	Message	Bus
Clock
2020-04-30 16:16:19.191863060	1.588263e+09	63.35	0.0
2020-04-30 16:16:19.193773985	1.588263e+09	63.33	0.0
2020-04-30 16:16:19.196507931	1.588263e+09	63.47	0.0
2020-04-30 16:16:19.198184013	1.588263e+09	63.54	0.0
2020-04-30 16:16:19.200067997	1.588263e+09	63.56	0.0
...	...	...	...
2020-04-30 16:20:33.233618975	1.588264e+09	13.64	0.0
2020-04-30 16:20:33.253679991	1.588264e+09	13.70	0.0
2020-04-30 16:20:33.274065018	1.588264e+09	13.74	0.0
2020-04-30 16:20:33.293877840	1.588264e+09	13.62	0.0
2020-04-30 16:20:33.314265013	1.588264e+09	13.56	0.0

12748 rows × 3 columns

[8]:

strymread.plt_ts(msgs464_1, title = "Message ID: 464, Signal ID: 1")

_images/HondaPilotDataThroughput_9_0.png

[9]:

strymread.ranalyze(msgs464_1, title="Message 464, Signal ID 1")

Analyzing Timestamp and Data Rate of Message 464, Signal ID 1
Interquartile Range of Rate for Message 464, Signal ID 1 is 1.148635679815989

_images/HondaPilotDataThroughput_10_1.png

[10]:

msgs490_1 = r.get_ts(490,1)
strymread.ranalyze(msgs490_1, title="Message 490, Signal ID 1")

Analyzing Timestamp and Data Rate of Message 490, Signal ID 1
Interquartile Range of Rate for Message 490, Signal ID 1 is 1.2079413490941846

_images/HondaPilotDataThroughput_11_1.png

[11]:

msgs145_0 = r.get_ts(145,0)
strymread.ranalyze(msgs145_0, title="Message 145, Signal ID 0")

Analyzing Timestamp and Data Rate of Message 145, Signal ID 0
Interquartile Range of Rate for Message 145, Signal ID 0 is 4.47148348251315

_images/HondaPilotDataThroughput_12_1.png

[12]:

msgs464_0 = r.get_ts(464,0)
strymread.plt_ts(msgs464_0)

_images/HondaPilotDataThroughput_13_0.png

[13]:

msgs464_1 = r.get_ts(464,1)
strymread.plt_ts(msgs464_1)

_images/HondaPilotDataThroughput_14_0.png

[14]:

msgs464_2 = r.get_ts(464,2)
strymread.plt_ts(msgs464_2)

_images/HondaPilotDataThroughput_15_0.png

[15]:

msgs464_3 = r.get_ts(464,3)
strymread.plt_ts(msgs464_3)

_images/HondaPilotDataThroughput_16_0.png

[16]:

msgs804_1 = r.get_ts(804,0)
strymread.plt_ts(msgs804_1)

_images/HondaPilotDataThroughput_17_0.png

[17]:

msgs804_0 = r.get_ts(804,0)
strymread.plt_ts(msgs804_0)

_images/HondaPilotDataThroughput_18_0.png

[18]:

msgs804_1 = r.get_ts(804,1)
strymread.plt_ts(msgs804_1)

_images/HondaPilotDataThroughput_19_0.png

[19]:

msgs804_2 = r.get_ts(804,2)
strymread.plt_ts(msgs804_2)

_images/HondaPilotDataThroughput_20_0.png

[20]:

msgs464_0

[20]:

	Time	Message	Bus
Clock
2020-04-30 16:16:19.191863060	1.588263e+09	63.54	0.0
2020-04-30 16:16:19.193773985	1.588263e+09	63.56	0.0
2020-04-30 16:16:19.196507931	1.588263e+09	63.65	0.0
2020-04-30 16:16:19.198184013	1.588263e+09	63.63	0.0
2020-04-30 16:16:19.200067997	1.588263e+09	63.68	0.0
...	...	...	...
2020-04-30 16:20:33.233618975	1.588264e+09	14.22	0.0
2020-04-30 16:20:33.253679991	1.588264e+09	14.17	0.0
2020-04-30 16:20:33.274065018	1.588264e+09	14.26	0.0
2020-04-30 16:20:33.293877840	1.588264e+09	14.29	0.0
2020-04-30 16:20:33.314265013	1.588264e+09	13.99	0.0

12748 rows × 3 columns

[21]:

msgs464_0['Message'] = msgs464_0['Message']*1000.0/3600.0
distance = strymread.integrate(msgs464_0)

[22]:

msgs464_0

[22]:

	Time	Message	Bus
Clock
2020-04-30 16:16:19.191863060	1.588263e+09	17.650000	0.0
2020-04-30 16:16:19.193773985	1.588263e+09	17.655556	0.0
2020-04-30 16:16:19.196507931	1.588263e+09	17.680556	0.0
2020-04-30 16:16:19.198184013	1.588263e+09	17.675000	0.0
2020-04-30 16:16:19.200067997	1.588263e+09	17.688889	0.0
...	...	...	...
2020-04-30 16:20:33.233618975	1.588264e+09	3.950000	0.0
2020-04-30 16:20:33.253679991	1.588264e+09	3.936111	0.0
2020-04-30 16:20:33.274065018	1.588264e+09	3.961111	0.0
2020-04-30 16:20:33.293877840	1.588264e+09	3.969444	0.0
2020-04-30 16:20:33.314265013	1.588264e+09	3.886111	0.0

12748 rows × 3 columns

[23]:

strymread.plt_ts(distance)

_images/HondaPilotDataThroughput_24_0.png