Commit 0a61a3de authored by Martin Reinecke's avatar Martin Reinecke

merge

parents 151c4e69 2547e3d0
......@@ -41,11 +41,12 @@ if __name__ == '__main__':
power_space = A.target[0]
power_distributor = ift.PowerDistributor(harmonic_space, power_space)
dummy = ift.Field.from_random('normal', harmonic_space)
domain = ift.MultiDomain.union(
(A.domain, ift.MultiDomain.make({'xi': harmonic_space})))
domain = ift.MultiDomain.union((A.domain,
ift.MultiDomain.make({
'xi': harmonic_space
})))
correlated_field = ht(
power_distributor(A)*ift.FieldAdapter(domain, "xi"))
correlated_field = ht(power_distributor(A)*ift.FieldAdapter(domain, "xi"))
# alternatively to the block above one can do:
# correlated_field = ift.CorrelatedField(position_space, A)
......@@ -54,8 +55,7 @@ if __name__ == '__main__':
# Building the Line of Sight response
LOS_starts, LOS_ends = get_random_LOS(100)
R = ift.LOSResponse(position_space, starts=LOS_starts,
ends=LOS_ends)
R = ift.LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends)
# build signal response model and model likelihood
signal_response = R(signal)
# specify noise
......@@ -68,8 +68,7 @@ if __name__ == '__main__':
data = signal_response(MOCK_POSITION) + N.draw_sample()
# set up model likelihood
likelihood = ift.GaussianEnergy(
mean=data, covariance=N)(signal_response)
likelihood = ift.GaussianEnergy(mean=data, covariance=N)(signal_response)
# set up minimization and inversion schemes
ic_sampling = ift.GradientNormController(iteration_limit=100)
......@@ -84,17 +83,18 @@ if __name__ == '__main__':
position = INITIAL_POSITION
plot = ift.Plot()
plot.add(signal(MOCK_POSITION), title='ground truth')
plot.add(R.adjoint_times(data), title='data')
plot.add([A(MOCK_POSITION)], title='power')
plot.output(nx=3, xsize=16, ysize=5, title="setup", name="setup.png")
plot.add(signal(MOCK_POSITION), title='Ground Truth')
plot.add(R.adjoint_times(data), title='Data')
plot.add([A(MOCK_POSITION)], title='Power Spectrum')
plot.output(ny=1, nx=3, xsize=24, ysize=6, name="setup.png")
# number of samples used to estimate the KL
N_samples = 20
for i in range(2):
metric = H(ift.Linearization.make_var(position)).metric
samples = [metric.draw_sample(from_inverse=True)
for _ in range(N_samples)]
samples = [
metric.draw_sample(from_inverse=True) for _ in range(N_samples)
]
KL = ift.SampledKullbachLeiblerDivergence(H, samples)
KL = ift.EnergyAdapter(position, KL)
......@@ -104,15 +104,17 @@ if __name__ == '__main__':
plot = ift.Plot()
plot.add(signal(position), title="reconstruction")
plot.add([A(position), A(MOCK_POSITION)], title="power")
plot.output(nx=2, xsize=12, ysize=6, title="loop", name="loop.png")
plot.output(ny=1, ysize=6, xsize=16, name="loop.png")
plot = ift.Plot()
sc = ift.StatCalculator()
for sample in samples:
sc.add(signal(sample+position))
plot.add(sc.mean, title="mean")
plot.add(ift.sqrt(sc.var), title="std deviation")
plot.add(sc.mean, title="Posterior Mean")
plot.add(ift.sqrt(sc.var), title="Posterior Standard Deviation")
powers = [A(s+position) for s in samples]
plot.add([A(position), A(MOCK_POSITION)]+powers, title="power")
plot.output(nx=3, xsize=16, ysize=5, title="results", name="results.png")
plot.add(
[A(position), A(MOCK_POSITION)]+powers,
title="Sampled Posterior Power Spectrum")
plot.output(ny=1, nx=3, xsize=24, ysize=6, name="results.png")
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