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)