def get_Om_from_abs_angles(abs_angles, m):
angles_vector = vector_from_matrix(abs_angles)
Om = np.zeros((m, m))
for i in range(m):
for j in range(m):
Om[i, j] = np.cos(angles_vector[i] - angles_vector[j])
return Om
def get_noisy_points(points_original, noise):
from pylocus.plots_cti import plot_matrix
from pylocus.basics_angles import from_0_to_2pi
points_noisy = points_original.copy()
points_noisy.add_noise(noise)
abs_angles_vector = vector_from_matrix(points_noisy.abs_angles)
#diff_angles = points_noisy.abs_angles - points_original.abs_angles
#diff_dm = (points_noisy.dm - points_original.dm).reshape((1,-1))
#diff = from_0_to_2pi(diff)
#plot_matrix(diff_angles, 'diff abs angles')
#print('diff dm:',diff_dm)
return points_noisy.Om, points_noisy.dm, abs_angles_vector
def get_noisy_absolute(abs_angles_original, dm_original, rho, sigmad,
gaussian):
m = len(dm_original)
if gaussian:
errors = np.triu(np.random.normal(0, rho, abs_angles_original.shape))
else:
from tikhonov import tikhonov_scipy
tik_sc = tikhonov_scipy(rho)
errors = np.triu(tik_sc.rvs(size=abs_angles_original.shape))
abs_angles_noisy = abs_angles_original + errors + errors.T
np.fill_diagonal(abs_angles_noisy, 0.0)
abs_angles_vector = vector_from_matrix(abs_angles_noisy)
if gaussian:
# create Om with same variance as absolute angles.
errors = np.triu(
np.random.normal(0, rho / 2.0, abs_angles_original.shape))
abs_angles_noisy = abs_angles_original + errors + errors.T
Om = get_Om_from_abs_angles(abs_angles_noisy, m)
dm = (np.sqrt(dm_original) + np.random.normal(0, sigmad, m))**2
return Om, dm, abs_angles_vector
def dm_from_edm(edm):
from pylocus.basics import vector_from_matrix
dm = vector_from_matrix(edm)
dm = np.extract(dm > 0, dm)
return np.power(dm, 0.5)