def test_batch_apply(points_1):
c = Calibraxis(verbose=False)
c.add_points(points_1)
c.calibrate_accelerometer()
out = c.batch_apply(points_1)
normed = np.sqrt((np.array(out) ** 2).sum(axis=1))
np.testing.assert_array_almost_equal(normed, 1.0, 2)
def test_batch_apply(points_1):
c = Calibraxis(verbose=False)
c.add_points(points_1)
c.calibrate_accelerometer()
out = c.batch_apply(points_1)
normed = np.sqrt((np.array(out)**2).sum(axis=1))
np.testing.assert_array_almost_equal(normed, 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)
print(np.array([jug.ax.mean(), jug.ay.mean(), jug.az.mean()]))
print(np.array([jug.gx.mean(), jug.gy.mean(), jug.gz.mean()]))
r = open('raw_juggle_data.csv')
r_lines = r.readlines()
r_lines = r_lines[:-1]
raw_data = genfromtxt(r_lines, delimiter=',')[1:, 1:-1]
print(raw_data.T.mean(axis=1))
#CALIBRATION ROUTINE
points = np.zeros((len(jug.t), 3))
points[:, 0] = jug.ax
points[:, 1] = jug.ay
points[:, 2] = jug.az
new_points = np.array(c.batch_apply(points))
#new_points = points
'''c = Calibraxis()
# Add points to calibration object's storage.
c.add_points(points)
# Run the calibration parameter optimization.
c.calibrate_accelerometer()
c.batch_apply(points)'''
jug.ax = new_points[:, 0]
jug.ay = new_points[:, 1]
jug.az = new_points[:, 2]
f, axarr = plt.subplots(6, sharex=True)
axarr[0].plot(jug.t, jug.ax)
axarr[1].plot(jug.t, jug.ay)