def calibration_combination(data):
min_magno = np.array([float("inf"), float("inf"), float("inf")])
max_magno = -np.array([float("inf"), float("inf"), float("inf")])
for vec in data:
min_magno = np.minimum(min_magno, vec)
max_magno = np.maximum(max_magno, vec)
magno_sf = 2 / (max_magno - min_magno)
magno_bias = (max_magno + min_magno) / 2
calibrated_initial = (data - magno_bias) * magno_sf
data2 = data_regularize(calibrated_initial)
center, (a, b, c), evecs, v = ellipsoid_fit(data2)
D = np.array([[1 / a, 0., 0.], [0., 1 / b, 0.], [0., 0., 1 / c]])
transform = np.dot(np.diag(magno_sf),evecs.dot(D).dot(evecs.T))
offset = center.T + magno_bias
calibrated = transform.dot((data - offset).T).T
plot_scatter(calibrated, "Combination Calibration", 100)
eval = eval_calibrated(calibrated)
print("Combination Calibration (Transform - {}, Offset - {} Eval - {})".format(
np.array_str(transform),
np.array_str(offset),
str(eval)))
def _monte_carlo_identity():
'''Generate the default params that don't affect readings by fitting to a unit sphere'''
# create points randomly distributed on the unit sphere
npoints = 25000
vec = np.random.randn(3, npoints)
vec /= np.linalg.norm(vec, axis=0)
# use those to fit
regularized = data_regularize(vec.T, divs=8)
return ellipsoid_fit(regularized)
def calibration_ellipsoid(file='magcalib.txt'):
data = read_mag_file(file)
data = data_regularize(data)
center, evecs, radii = ellipsoid_fit(data)
a, b, c = radii
#r = (a * b * c) ** (1. / 3.)
D = np.array([[1/a, 0., 0.], [0., 1/b, 0.], [0., 0., 1/c]])
transformation = evecs@[email protected]
return center.T,transformation
def calibration_ellipsoid(data):
data2 = data_regularize(data)
plot_scatter(data2, "Regularized Data", 1)
center, (a, b, c), evecs, v = ellipsoid_fit(data2)
D = np.array([[1 / a, 0., 0.], [0., 1 / b, 0.], [0., 0., 1 / c]])
transform = evecs.dot(D).dot(evecs.T)
calibrated = transform.dot((data - center.T).T).T
plot_scatter(calibrated, "Calibrated ADIS16405 Magnetometer Data", 100)
eval = eval_calibrated(calibrated)
print("Ellipsoid Calibration (Transform - {}, Offset - {} Eval - {})".format(
np.array_str(transform),
np.array_str(center.T),
str(eval)))
return transform
import numpy as np
from ellipsoid_fit import ellipsoid_fit as ellipsoid_fit, data_regularize
if __name__=='__main__':
data = np.loadtxt("mag_out.txt")
data2 = data_regularize(data)
center, radii, evecs, v = ellipsoid_fit(data2)
a,b,c = radii
r = (a*b*c)**(1./3.)
D = np.array([[r/a,0.,0.],[0.,r/b,0.],[0.,0.,r/c]])
TR = evecs.dot(D).dot(evecs.T)
print
print 'center: ',center
print 'transformation:'
print TR
np.savetxt('magcal_ellipsoid.txt', np.vstack((center.T, TR)))
import numpy as np
from ellipsoid_fit import ellipsoid_fit as ellipsoid_fit, data_regularize
if __name__ == '__main__':
data = np.loadtxt("mag_out.txt")
data2 = data_regularize(data)
center, radii, evecs, v = ellipsoid_fit(data2)
a, b, c = radii
r = (a * b * c)**(1. / 3.)
D = np.array([[r / a, 0., 0.], [0., r / b, 0.], [0., 0., r / c]])
TR = evecs.dot(D).dot(evecs.T)
print('')
print('center: ', center)
print('transformation:')
print(TR)
np.savetxt('magcal_ellipsoid.txt', np.vstack((center.T, TR)))
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from ellipsoid_fit import ellipsoid_fit, ellipsoid_plot, data_regularize
if __name__ == '__main__':
data = np.loadtxt("mag_out.txt")
data2 = data_regularize(data, divs=8)
center, radii, evecs, v = ellipsoid_fit(data2)
dataC = data - center.T
dataC2 = data2 - center.T
a, b, c = radii
r = (a * b * c)**(1. / 3.) #preserve volume?
D = np.array([[r / a, 0., 0.], [0., r / b, 0.], [0., 0., r / c]])
#http://www.cs.brandeis.edu/~cs155/Lecture_07_6.pdf
#affine transformation from ellipsoid to sphere (translation excluded)
TR = evecs.dot(D).dot(evecs.T)
dataE = TR.dot(dataC2.T).T
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#hack for equal axes
ax.set_aspect('equal')
MAX = 200
for direction in (-1, 1):
for point in np.diag(direction * MAX * np.array([1, 1, 1])):
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from ellipsoid_fit import ellipsoid_fit, ellipsoid_plot, data_regularize
if __name__=='__main__':
data = np.loadtxt("mag_out.txt")
data2 = data_regularize(data, divs=8)
center, radii, evecs, v = ellipsoid_fit(data2)
dataC = data - center.T
dataC2 = data2 - center.T
a,b,c = radii
r = (a*b*c)**(1./3.)#preserve volume?
D = np.array([[r/a,0.,0.],[0.,r/b,0.],[0.,0.,r/c]])
#http://www.cs.brandeis.edu/~cs155/Lecture_07_6.pdf
#affine transformation from ellipsoid to sphere (translation excluded)
TR = evecs.dot(D).dot(evecs.T)
dataE = TR.dot(dataC2.T).T
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#hack for equal axes
ax.set_aspect('equal')
MAX = 200
def calibrate(self, raw_mag):
#not sure what regularizing does but the ellipsoid_fit example uses it...
regularized = data_regularize(raw_mag, divs=8)
center, radii, evecs, v = ellipsoid_fit(regularized)
self.params = center, radii, evecs, v