Speed-Acceleration Phasespace

In this notebook, we will plot speed-acceleration phase from a drive collection

from strym import strymread
import strym
import glob
import pandas as pd
import os
import matplotlib.pyplot as plt
import scipy.io as sio
import datetime
import time
import pickle

/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 ...

Set the data folder and dbc file location for Toyota

parentfolder = "../../PandaData/2020_03_05"
dbcfile = '../examples/newToyotacode.dbc'

csvlist = []
folderlist = glob.glob(parentfolder+"*")
speedlist = []
for datafolder in folderlist:
    csvlisttmp = glob.glob(datafolder+"/*.csv")
    for f in csvlisttmp:
        if "CAN" not in f:
            continue
        if "_5F" in f:
            continue
        csvlist.append(f)
print(csvlist)

['../../PandaData/2020_03_05/2020-03-05-08-23-30-382135__CAN_Messages.csv', '../../PandaData/2020_03_05/2020-03-05-13-21-29-803650__CAN_Messages.csv', '../../PandaData/2020_03_05/2020-03-05-08-42-39-921531__CAN_Messages.csv', '../../PandaData/2020_03_05/2020-03-05-09-09-59-241536__CAN_Messages.csv', '../../PandaData/2020_03_05/2020-03-05-13-00-56-941071__CAN_Messages.csv', '../../PandaData/2020_03_05/2020-03-05-13-59-18-553197__CAN_Messages.csv', '../../PandaData/2020_03_05/2020-03-05-10-11-35-602492__CAN_Messages.csv', '../../PandaData/2020_03_05/2020-03-05-09-21-37-022653__CAN_Messages.csv']

Read all the CSV file

speed_list = []
accel_list = []
r_list = []
counter = 0
for csv in csvlist:
    print("\nReading the CSV file {}".format(csv))
    r = strymread(csvfile=csv, dbcfile=dbcfile)

    # Don't read the speed if data came in burst, basically filter out files which was recorded with Python
    if r.success == True :
        if r.burst:
            continue
        r_list.append(r)
        speed = r.speed()
        speed['Message'] = speed['Message']*0.277778
        accelx = r.accelx()
        speed_list.append(speed)
        accel_list.append(accelx)

Reading the CSV file ../../PandaData/2020_03_05/2020-03-05-08-23-30-382135__CAN_Messages.csv

Reading the CSV file ../../PandaData/2020_03_05/2020-03-05-13-21-29-803650__CAN_Messages.csv

Reading the CSV file ../../PandaData/2020_03_05/2020-03-05-08-42-39-921531__CAN_Messages.csv

Reading the CSV file ../../PandaData/2020_03_05/2020-03-05-09-09-59-241536__CAN_Messages.csv

Reading the CSV file ../../PandaData/2020_03_05/2020-03-05-13-00-56-941071__CAN_Messages.csv
CSVfile is empty.

Reading the CSV file ../../PandaData/2020_03_05/2020-03-05-13-59-18-553197__CAN_Messages.csv

Reading the CSV file ../../PandaData/2020_03_05/2020-03-05-10-11-35-602492__CAN_Messages.csv

Reading the CSV file ../../PandaData/2020_03_05/2020-03-05-09-21-37-022653__CAN_Messages.csv

Resample Speed to time-points of Accel Data

resampled_speed_list = []
for i, speed in enumerate(speed_list):
    if speed.shape[0] == 0:
        continue
    speed_new, accel_new = strymread.ts_sync(speed_list[i], accel_list[i], rate="second")
    resampled_speed_accel = pd.DataFrame()
    resampled_speed_accel['Time'] = speed_new['Time']
    resampled_speed_accel['Speed'] = speed_new['Message']
    resampled_speed_accel['Accel'] = accel_new['Message']
    resampled_speed_list.append(resampled_speed_accel)

Combine all list of resampled dataframe to a single dataframe

speed_accel = pd.concat(resampled_speed_list)

Make a phase-space plot

fig, ax = strymread.create_fig(1)
fig.set_size_inches(8, 8)
ax[0].scatter(x = 'Speed', y = 'Accel', data = speed_accel, s = 1, color = "#E32b2b")
ax[0].set_xlabel('Speed [m/s]')
ax[0].set_ylabel('Acceleration [m/s^2]')
ax[0].set_title('Speed[m/s] - Acceleration [m/s^2] Phase-space')
plt.show()

dt_object = datetime.datetime.fromtimestamp(time.time())
dt = dt_object.strftime('%Y-%m-%d-%H-%M-%S-%f')
description = "_2020_03_05_Acceleration_Speed_Data"
fig.savefig(dt+ description + ".pdf", dpi = 300)
fig.savefig(dt+ description + ".png", dpi = 300)
pickle.dump(fig, open(dt+ description +".pickle", 'wb'))

variable_dictionary = {}
variable_dictionary['speed_accel'] = speed_accel.to_numpy()

sio.savemat(dt+"_2020_03_05_Acceleration_Speed_Data.mat", variable_dictionary)