65 lines
1.7 KiB
Python
Executable File
65 lines
1.7 KiB
Python
Executable File
#!/usr/bin/env python3
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import linear_model # pylint: disable=import-error
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from selfdrive.car.toyota.values import STEER_THRESHOLD
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from tools.lib.route import Route
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from tools.lib.logreader import MultiLogIterator
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MIN_SAMPLES = 30 * 100
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def to_signed(n, bits):
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if n >= (1 << max((bits - 1), 0)):
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n = n - (1 << max(bits, 0))
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return n
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def get_eps_factor(lr, plot=False):
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engaged = False
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steering_pressed = False
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torque_cmd, eps_torque = None, None
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cmds, eps = [], []
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for msg in lr:
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if msg.which() != 'can':
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continue
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for m in msg.can:
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if m.address == 0x2e4 and m.src == 128:
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engaged = bool(m.dat[0] & 1)
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torque_cmd = to_signed((m.dat[1] << 8) | m.dat[2], 16)
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elif m.address == 0x260 and m.src == 0:
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eps_torque = to_signed((m.dat[5] << 8) | m.dat[6], 16)
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steering_pressed = abs(to_signed((m.dat[1] << 8) | m.dat[2], 16)) > STEER_THRESHOLD
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if engaged and torque_cmd is not None and eps_torque is not None and not steering_pressed:
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cmds.append(torque_cmd)
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eps.append(eps_torque)
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else:
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if len(cmds) > MIN_SAMPLES:
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break
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cmds, eps = [], []
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if len(cmds) < MIN_SAMPLES:
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raise Exception("too few samples found in route")
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lm = linear_model.LinearRegression(fit_intercept=False)
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lm.fit(np.array(cmds).reshape(-1, 1), eps)
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scale_factor = 1. / lm.coef_[0]
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if plot:
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plt.plot(np.array(eps) * scale_factor)
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plt.plot(cmds)
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plt.show()
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return scale_factor
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if __name__ == "__main__":
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r = Route(sys.argv[1])
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lr = MultiLogIterator(r.log_paths())
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n = get_eps_factor(lr, plot="--plot" in sys.argv)
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print("EPS torque factor: ", n)
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