def test_apply(points_1): c = Calibraxis(verbose=False) c.add_points(points_1) c.calibrate_accelerometer() np.testing.assert_almost_equal(np.linalg.norm(c.apply(points_1[0, :])), 1.0, 2)
import numpy as np from calibraxis import Calibraxis c = Calibraxis() points = np.array([[-4772.38754098, 154.04459016, -204.39081967], [3525.0346179, -68.64924886, -34.54604833], [-658.17681729, -4137.60248854, -140.49377865], [-564.18562092, 4200.29150327, -130.51895425], [-543.18289474, 18.14736842, -4184.43026316], [-696.62532808, 15.70209974, 3910.20734908], [406.65271419, 18.46827992, -4064.61085677], [559.45926413, -3989.69513798, -174.71879106], [597.22629169, -3655.54153041, -1662.83257031], [1519.02616089, -603.82472204, 3290.58469588]]) # Add points to calibration object's storage. c.add_points(points) # Run the calibration parameter optimization. c.calibrate_accelerometer() # Applying the calibration parameters to the calibration data. c.apply(points[0:]) c.batch_apply(points)
def test_apply(points_1): c = Calibraxis(verbose=False) c.add_points(points_1) c.calibrate_accelerometer() np.testing.assert_almost_equal(np.linalg.norm(c.apply(points_1[0, :])), 1.0, 2)