Correlation of distance traveled and fuel consumed¶

[3]:

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

Read the Honda Pilot Dataset¶

[4]:

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

[5]:

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

Read wheel speed¶

[7]:

speed = r.speed()
speed['Message'] = speed['Message']*0.277778
strymread.plt_ts(speed, title="Timeseries plot: Speed [m/s]")

_images/HondaPilotFuelConsumptionCorrelation_6_0.png

Read Trip Fuel Consumed¶

[8]:

msgs804_2 = r.get_ts(804,2)
strymread.plt_ts(msgs804_2,  title="Timeseries plot: Trip Fuel Consumed, Unit unknown")

_images/HondaPilotFuelConsumptionCorrelation_8_0.png

Integrate speed to get total distance¶

[12]:

distance = strymread.integrate(speed)
distance.dropna(inplace=True)

strymread.plt_ts(distance, title = "Distance Traveled")

_images/HondaPilotFuelConsumptionCorrelation_10_0.png

[13]:

distance

[13]:

	Time	Message
2020-04-30 16:16:19.190859079	1.588263e+09	0.000000
2020-04-30 16:16:19.191996098	1.588263e+09	0.020065
2020-04-30 16:16:19.192753792	1.588263e+09	0.033430
2020-04-30 16:16:19.193773985	1.588263e+09	0.051425
2020-04-30 16:16:19.195368052	1.588263e+09	0.079556
...	...	...
2020-04-30 16:20:33.275126934	1.588264e+09	3934.963256
2020-04-30 16:20:33.285344839	1.588264e+09	3935.002624
2020-04-30 16:20:33.295157909	1.588264e+09	3935.040431
2020-04-30 16:20:33.305240154	1.588264e+09	3935.079276
2020-04-30 16:20:33.315083981	1.588264e+09	3935.117120

25494 rows × 2 columns

Scatterplot of correlation of total distance and fuel consumed¶

[23]:

distance_resampled, msgs804_2_resampled = strymread.ts_sync(distance, msgs804_2, rate="second")

[26]:

fig, ax = strymread.create_fig(1)
ax = ax[0]
ax.scatter(distance_resampled['Message'], msgs804_2_resampled['Message'],
           c=distance_resampled['Time'], s = 8, cmap="magma")
cbr = fig.colorbar(im, ax=ax)
cbr.set_label("Time")
ax.set_title("Scatterplot showing correlation of total\ndistance travelled and total fuel consumed")
ax.set_xlabel("Total distance travelled")
ax.set_ylabel("Total fuel consumed")
plt.show()

_images/HondaPilotFuelConsumptionCorrelation_14_0.png

[25]:

distance_resampled['Message'].corr(msgs804_2_resampled['Message'])

[25]:

0.9830666406297385