Commit 10e967de authored by Philipp Frank's avatar Philipp Frank Committed by Philipp Arras
Browse files

Update demo

parent 21f0128d
......@@ -31,6 +31,7 @@ import numpy as np
import nifty7 as ift
ift.random.push_sseq_from_seed(27)
def random_los(n_los):
starts = list(ift.random.current_rng().random((n_los, 2)).T)
......@@ -63,16 +64,16 @@ def main():
'offset_std': (1e-3, 1e-6),
# Amplitude of field fluctuations
'fluctuations': (2., 1.), # 1.0, 1e-2
'fluctuations': (1., 0.8), # 1.0, 1e-2
# Exponent of power law power spectrum component
'loglogavgslope': (-4., 1), # -6.0, 1
'loglogavgslope': (-3., 1), # -6.0, 1
# Amplitude of integrated Wiener process power spectrum component
'flexibility': (5, 2.), # 2.0, 1.0
'flexibility': (2, 1.), # 1.0, 0.5
# How ragged the integrated Wiener process component is
'asperity': (0.5, 0.5) # 0.1, 0.5
'asperity': (0.5, 0.4) # 0.1, 0.5
}
correlated_field = ift.SimpleCorrelatedField(position_space, **args)
......@@ -89,7 +90,6 @@ def main():
# Specify noise
data_space = R.target
noise = .001
sig = ift.ScalingOperator(data_space, np.sqrt(noise))
N = ift.ScalingOperator(data_space, noise)
# Generate mock signal and data
......@@ -102,6 +102,9 @@ def main():
ic_newton = ift.AbsDeltaEnergyController(
name='Newton', deltaE=0.5, iteration_limit=35)
minimizer = ift.NewtonCG(ic_newton)
ic_sampling_nl = ift.AbsDeltaEnergyController(
name='Sampling', deltaE=0.5, iteration_limit=15, convergence_level=2)
minimizer_sampling = ift.NewtonCG(ic_sampling_nl)
# Set up likelihood and information Hamiltonian
likelihood = (ift.GaussianEnergy(mean=data, inverse_covariance=N.inverse) @
......@@ -112,18 +115,21 @@ def main():
mean = initial_mean
plot = ift.Plot()
plot.add(signal(mock_position), title='Ground Truth')
plot.add(signal(mock_position), title='Ground Truth', zmin = 0, zmax = 1)
plot.add(R.adjoint_times(data), title='Data')
plot.add([pspec.force(mock_position)], title='Power Spectrum')
plot.output(ny=1, nx=3, xsize=24, ysize=6, name=filename.format("setup"))
# number of samples used to estimate the KL
N_samples = 20
N_samples = 10
# Draw new samples to approximate the KL five times
for i in range(5):
# Draw new samples to approximate the KL six times
for i in range(6):
if i==5:
# Double the number of samples in the last step for better statistics
N_samples = 2*N_samples
# Draw new samples and minimize KL
KL = ift.MetricGaussianKL(mean, H, N_samples, True)
KL = ift.GeoMetricKL(mean, H, N_samples, minimizer_sampling, True)
KL, convergence = minimizer(KL)
mean = KL.position
ift.extra.minisanity(data, lambda x: N.inverse, signal_response,
......@@ -131,7 +137,7 @@ def main():
# Plot current reconstruction
plot = ift.Plot()
plot.add(signal(KL.position), title="Latent mean")
plot.add(signal(KL.position), title="Latent mean", zmin = 0, zmax = 1)
plot.add([pspec.force(KL.position + ss) for ss in KL.samples],
title="Samples power spectrum")
plot.output(ny=1, ysize=6, xsize=16,
......@@ -144,16 +150,16 @@ def main():
# Plotting
filename_res = filename.format("results")
plot = ift.Plot()
plot.add(sc.mean, title="Posterior Mean")
plot.add(sc.mean, title="Posterior Mean", zmin = 0, zmax = 1)
plot.add(ift.sqrt(sc.var), title="Posterior Standard Deviation")
powers = [pspec.force(s + KL.position) for s in KL.samples]
sc = ift.StatCalculator()
for pp in powers:
sc.add(pp)
sc.add(pp.log())
plot.add(
powers + [pspec.force(mock_position),
pspec.force(KL.position), sc.mean],
pspec.force(KL.position), sc.mean.exp()],
title="Sampled Posterior Power Spectrum",
linewidth=[1.]*len(powers) + [3., 3., 3.],
label=[None]*len(powers) + ['Ground truth', 'Posterior latent mean', 'Posterior mean'])
......
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