def testModelLibrary(space, seed):
# Tests amplitude model and coorelated field model
np.random.seed(seed)
domain = ift.PowerSpace(space.get_default_codomain())
model = ift.SLAmplitude(target=domain,
n_pix=4,
a=.5,
k0=2,
sm=3,
sv=1.5,
im=1.75,
iv=1.3)
assert_(isinstance(model, ift.Operator))
S = ift.ScalingOperator(1., model.domain)
pos = S.draw_sample()
ift.extra.check_jacobian_consistency(model, pos, ntries=20)
model2 = ift.CorrelatedField(space, model)
S = ift.ScalingOperator(1., model2.domain)
pos = S.draw_sample()
ift.extra.check_jacobian_consistency(model2, pos, ntries=20)
domtup = ift.DomainTuple.make((space, space))
model3 = ift.MfCorrelatedField(domtup, [model, model])
S = ift.ScalingOperator(1., model3.domain)
pos = S.draw_sample()
ift.extra.check_jacobian_consistency(model3, pos, ntries=20)
# Set up an amplitude operator for the field
# We want to set up a model for the amplitude spectrum with some magic numbers
dct = {
'target': power_space,
'n_pix': 64, # 64 spectral bins
# Spectral smoothness (affects Gaussian process part)
'a': 10, # relatively high variance of spectral curvature
'k0': .2, # quefrency mode below which cepstrum flattens
# Power-law part of spectrum:
'sm': -4, # preferred power-law slope
'sv': .6, # low variance of power-law slope
'im': -6, # y-intercept mean, in-/decrease for more/less contrast
'iv': 2. # y-intercept variance
}
A = ift.SLAmplitude(**dct)
correlated_field = ift.CorrelatedField(position_space, A)
### SETTING UP SPECIFIC SCENARIO ####
R = ift.GeometryRemover(position_space)
data_space = R.target
signal_response = R(correlated_field)
# Set up likelihood and load data
N = ift.ScalingOperator(0.1, data_space)
data, ground_truth = generate_mysterious_data(position_space)
np.random.seed(42)
position_space = ift.RGSpace(2*(256,))
harmonic_space = position_space.get_default_codomain()
HT = ift.HarmonicTransformOperator(harmonic_space, target=position_space)
power_space = ift.PowerSpace(harmonic_space)
# Set up generative model
A = ift.SLAmplitude(
**{
'target': power_space,
'n_pix': 64, # 64 spectral bins
# Smoothness of spectrum
'a': 10, # relatively high variance of spectral curvature
'k0': .2, # quefrency mode below which cepstrum flattens
# Power-law part of spectrum
'sm': -4, # preferred power-law slope
'sv': .6, # low variance of power-law slope
'im': -2, # y-intercept mean, in-/decrease for more/less contrast
'iv': 2. # y-intercept variance
})
signal = ift.CorrelatedField(position_space, A)
R = checkerboard_response(position_space)
data_space = R.target
signal_response = R @ signal
# Set up likelihood and generate data from the model
N = ift.ScalingOperator(0.1, data_space)
data, ground_truth = generate_gaussian_data(signal_response, N)