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82 lines
2.2 KiB
82 lines
2.2 KiB
import pytest
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import os
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import numpy as np
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from .kinematic_kf import KinematicKalman, ObservationKind, States
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GENERATED_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), 'generated'))
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class TestKinematic:
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def test_kinematic_kf(self):
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np.random.seed(0)
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kf = KinematicKalman(GENERATED_DIR)
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# Simple simulation
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dt = 0.01
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ts = np.arange(0, 5, step=dt)
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vs = np.sin(ts * 5)
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x = 0.0
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xs = []
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xs_meas = []
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xs_kf = []
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vs_kf = []
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xs_kf_std = []
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vs_kf_std = []
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for t, v in zip(ts, vs):
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xs.append(x)
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# Update kf
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meas = np.random.normal(x, 0.1)
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xs_meas.append(meas)
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kf.predict_and_observe(t, ObservationKind.POSITION, [meas])
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# Retrieve kf values
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state = kf.x
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xs_kf.append(float(state[States.POSITION].item()))
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vs_kf.append(float(state[States.VELOCITY].item()))
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std = np.sqrt(kf.P)
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xs_kf_std.append(float(std[States.POSITION, States.POSITION].item()))
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vs_kf_std.append(float(std[States.VELOCITY, States.VELOCITY].item()))
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# Update simulation
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x += v * dt
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xs, xs_meas, xs_kf, vs_kf, xs_kf_std, vs_kf_std = (np.asarray(a) for a in (xs, xs_meas, xs_kf, vs_kf, xs_kf_std, vs_kf_std))
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assert xs_kf[-1] == pytest.approx(-0.010866289677966417)
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assert xs_kf_std[-1] == pytest.approx(0.04477103863330089)
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assert vs_kf[-1] == pytest.approx(-0.8553720537261753)
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assert vs_kf_std[-1] == pytest.approx(0.6695762270974388)
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if "PLOT" in os.environ:
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import matplotlib.pyplot as plt # pylint: disable=import-error
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plt.figure()
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plt.subplot(2, 1, 1)
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plt.plot(ts, xs, 'k', label='Simulation')
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plt.plot(ts, xs_meas, 'k.', label='Measurements')
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plt.plot(ts, xs_kf, label='KF')
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ax = plt.gca()
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ax.fill_between(ts, xs_kf - xs_kf_std, xs_kf + xs_kf_std, alpha=.2, color='C0')
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plt.xlabel("Time [s]")
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plt.ylabel("Position [m]")
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plt.legend()
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plt.subplot(2, 1, 2)
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plt.plot(ts, vs, 'k', label='Simulation')
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plt.plot(ts, vs_kf, label='KF')
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ax = plt.gca()
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ax.fill_between(ts, vs_kf - vs_kf_std, vs_kf + vs_kf_std, alpha=.2, color='C0')
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plt.xlabel("Time [s]")
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plt.ylabel("Velocity [m/s]")
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plt.legend()
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plt.show()
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