def testLOSResponse(sp, dtype):
starts = np.random.randn(len(sp.shape), 10)
ends = np.random.randn(len(sp.shape), 10)
sigma_low = 1e-4 * np.random.randn(10)
sigma_ups = 1e-5 * np.random.randn(10)
op = ift.LOSResponse(sp, starts, ends, sigma_low, sigma_ups)
ift.extra.consistency_check(op, dtype, dtype)
def testLOSResponse(sp, dtype):
starts = np.random.randn(len(sp.shape), 10)
ends = np.random.randn(len(sp.shape), 10)
sigma_low = 1e-4 * np.random.randn(10)
sigma_ups = 1e-5 * np.random.randn(10)
_check_repr(ift.LOSResponse(sp, starts, ends, sigma_low, sigma_ups))
def random_tomography_response(position_space, lines_of_sight=100):
starts = list(np.random.uniform(0, 1, (lines_of_sight, 2)).T)
ends = list(np.random.uniform(0, 1, (lines_of_sight, 2)).T)
return ift.LOSResponse(position_space, starts=starts, ends=ends)
A = ift.SLAmplitude(**dct)
# Build the operator for a correlated signal
power_distributor = ift.PowerDistributor(harmonic_space, power_space)
vol = harmonic_space.scalar_dvol**-0.5
xi = ift.ducktape(harmonic_space, None, 'xi')
correlated_field = ht(vol*power_distributor(A)*xi)
# Alternatively, one can use:
# correlated_field = ift.CorrelatedField(position_space, A)
# Apply a nonlinearity
signal = ift.sigmoid(correlated_field)
# Build the line-of-sight response and define signal response
LOS_starts, LOS_ends = random_los(100) if mode == 0 else radial_los(100)
R = ift.LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends)
signal_response = R(signal)
# Specify noise
data_space = R.target
noise = .001
N = ift.ScalingOperator(noise, data_space)
# Generate mock signal and data
mock_position = ift.from_random('normal', signal_response.domain)
data = signal_response(mock_position) + N.draw_sample()
# Minimization parameters
ic_sampling = ift.GradientNormController(iteration_limit=100)
ic_newton = ift.GradInfNormController(
name='Newton', tol=1e-7, iteration_limit=35)