def testPPO(sp, dtype):
_check_repr(ift.PowerDistributor(target=sp))
ps = ift.PowerSpace(
sp, ift.PowerSpace.useful_binbounds(sp, logarithmic=False, nbin=3))
_check_repr(ift.PowerDistributor(target=sp, power_space=ps))
ps = ift.PowerSpace(
sp, ift.PowerSpace.useful_binbounds(sp, logarithmic=True, nbin=3))
_check_repr(ift.PowerDistributor(target=sp, power_space=ps))
def testPPO(sp, dtype):
op = ift.PowerDistributor(target=sp)
ift.extra.consistency_check(op, dtype, dtype)
ps = ift.PowerSpace(
sp, ift.PowerSpace.useful_binbounds(sp, logarithmic=False, nbin=3))
op = ift.PowerDistributor(target=sp, power_space=ps)
ift.extra.consistency_check(op, dtype, dtype)
ps = ift.PowerSpace(
sp, ift.PowerSpace.useful_binbounds(sp, logarithmic=True, nbin=3))
op = ift.PowerDistributor(target=sp, power_space=ps)
ift.extra.consistency_check(op, dtype, dtype)
def test_gaussian_energy(space, nonlinearity, noise, seed):
np.random.seed(seed)
dim = len(space.shape)
hspace = space.get_default_codomain()
ht = ift.HarmonicTransformOperator(hspace, target=space)
binbounds = ift.PowerSpace.useful_binbounds(hspace, logarithmic=False)
pspace = ift.PowerSpace(hspace, binbounds=binbounds)
Dist = ift.PowerDistributor(target=hspace, power_space=pspace)
xi0 = ift.Field.from_random(domain=hspace, random_type='normal')
def pspec(k):
return 1 / (1 + k**2)**dim
pspec = ift.PS_field(pspace, pspec)
A = Dist(ift.sqrt(pspec))
N = ift.ScalingOperator(noise, space)
n = N.draw_sample()
R = ift.ScalingOperator(10., space)
def d_model():
if nonlinearity == "":
return R(ht(ift.makeOp(A)))
else:
tmp = ht(ift.makeOp(A))
nonlin = getattr(tmp, nonlinearity)()
return R(nonlin)
d = d_model()(xi0) + n
if noise == 1:
N = None
energy = ift.GaussianEnergy(d, N)(d_model())
ift.extra.check_jacobian_consistency(energy, xi0, ntries=10, tol=5e-8)
# Specify harmonic space corresponding to signal
harmonic_space = position_space.get_default_codomain()
# Harmonic transform from harmonic space to position space
HT = ift.HarmonicTransformOperator(harmonic_space, target=position_space)
# Set prior correlation covariance with a power spectrum leading to
# homogeneous and isotropic statistics
def power_spectrum(k):
return 100. / (20. + k**3)
# 1D spectral space on which the power spectrum is defined
power_space = ift.PowerSpace(harmonic_space)
# Mapping to (higher dimensional) harmonic space
PD = ift.PowerDistributor(harmonic_space, power_space)
# Apply the mapping
prior_correlation_structure = PD(ift.PS_field(power_space, power_spectrum))
# Insert the result into the diagonal of an harmonic space operator
S = ift.DiagonalOperator(prior_correlation_structure)
# S is the prior field covariance
# Build instrument response consisting of a discretization, mask
# and harmonic transformaion
# Data is defined on a geometry-free space, thus the geometry is removed
GR = ift.GeometryRemover(position_space)
# Masking operator to model that parts of the field have not been observed
position_space = ift.HPSpace(128)
exposure = ift.Field.full(position_space, 100.)
# Define harmonic space and harmonic transform
harmonic_space = position_space.get_default_codomain()
HT = ift.HarmonicTransformOperator(harmonic_space, position_space)
# Domain on which the field's degrees of freedom are defined
domain = ift.DomainTuple.make(harmonic_space)
# Define amplitude (square root of power spectrum)
def sqrtpspec(k):
return 1. / (20. + k**2)
p_space = ift.PowerSpace(harmonic_space)
pd = ift.PowerDistributor(harmonic_space, p_space)
a = ift.PS_field(p_space, sqrtpspec)
A = pd(a)
# Define sky operator
sky = ift.exp(HT(ift.makeOp(A)))
M = ift.DiagonalOperator(exposure)
GR = ift.GeometryRemover(position_space)
# Define instrumental response
R = GR(M)
# Generate mock data and define likelihood operator
d_space = R.target[0]
lamb = R(sky)
mock_position = ift.from_random('normal', domain)