diff --git a/demos/getting_started_mf.py b/demos/getting_started_mf.py
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+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+# Copyright(C) 2013-2019 Max-Planck-Society
+#
+# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
+
+############################################################
+# Non-linear tomography
+#
+# The signal is a sigmoid-normal distributed field.
+# The data is the field integrated along lines of sight that are
+# randomly (set mode=0) or radially (mode=1) distributed
+#
+# Demo takes a while to compute
+#############################################################
+
+import sys
+
+import numpy as np
+
+import nifty5 as ift
+
+class SingleDomain(ift.LinearOperator):
+ def __init__(self,domain,target):
+ self._domain = ift.makeDomain(domain)
+ self._target = ift.makeDomain(target)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
+ def apply(self,x,mode):
+ self._check_input(x,mode)
+ return ift.from_global_data(self._tgt(mode),x.to_global_data())
+
+def random_los(n_los):
+ starts = list(np.random.uniform(0, 1, (n_los, 2)).T)
+ ends = list(np.random.uniform(0, 1, (n_los, 2)).T)
+ return starts, ends
+
+
+def radial_los(n_los):
+ starts = list(np.random.uniform(0, 1, (n_los, 2)).T)
+ ends = list(0.5 + 0*np.random.uniform(0, 1, (n_los, 2)).T)
+ return starts, ends
+
+
+if __name__ == '__main__':
+ np.random.seed(45)
+
+ # Choose between random line-of-sight response (mode=0) and radial lines
+ # of sight (mode=1)
+ if len(sys.argv) == 2:
+ mode = int(sys.argv[1])
+ else:
+ mode = 0
+ filename = "getting_started_mf_mode_{}_".format(mode) + "{}.png"
+
+ npix1, npix2 = 128, 128
+ position_space = ift.RGSpace([npix1, npix2])
+ sp1 = ift.RGSpace(npix1)
+ sp2 = ift.RGSpace(npix2)
+
+ power_space1 = ift.PowerSpace(sp1.get_default_codomain())
+ power_space2 = ift.PowerSpace(sp2.get_default_codomain())
+
+ cfmaker = ift.CorrelatedFieldMaker()
+ amp1 = 0.5
+ cfmaker.add_fluctuations(power_space1,
+ amp1, 1e-2,
+ 1, .1,
+ .01, .5,
+ -2, 1.,
+ 'amp1')
+ cfmaker.add_fluctuations(power_space2,
+ np.sqrt(1.-amp1**2), 1e-2,
+ 1, .1,
+ .01, .5,
+ -1.5, .5,
+ 'amp2')
+ correlated_field = cfmaker.finalize(1e-3, 1e-6, '')
+ sams = [ift.from_random('normal',correlated_field.domain)
+ for _ in range(20)]
+
+ print("Prior expected total fluctuations: "+str(
+ cfmaker.stats(cfmaker.total_fluctuation,sams)[0]))
+
+ A1 = cfmaker.amplitudes[0]
+ A2 = cfmaker.amplitudes[1]
+ DC = SingleDomain(correlated_field.target,position_space)
+
+ # Apply a nonlinearity
+ signal = DC @ 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.AbsDeltaEnergyController(
+ name='Sampling', deltaE=0.01, iteration_limit=100)
+ ic_newton = ift.AbsDeltaEnergyController(
+ name='Newton', deltaE=0.01, iteration_limit=35)
+ minimizer = ift.NewtonCG(ic_newton)
+
+ # Set up likelihood and information Hamiltonian
+ likelihood = ift.GaussianEnergy(mean=data,
+ inverse_covariance=N.inverse)(signal_response)
+ H = ift.StandardHamiltonian(likelihood, ic_sampling)
+
+ initial_mean = ift.MultiField.full(H.domain, 0.)
+ mean = initial_mean
+
+ plot = ift.Plot()
+ plot.add(signal(mock_position), title='Ground Truth')
+ plot.add(R.adjoint_times(data), title='Data')
+ plot.add([A1.force(mock_position)], title='Power Spectrum 1')
+ plot.add([A2.force(mock_position)], title='Power Spectrum 2')
+ plot.output(ny=2, nx=2, xsize=10, ysize=10, name=filename.format("setup"))
+
+ # number of samples used to estimate the KL
+ N_samples = 20
+
+ # Draw new samples to approximate the KL five times
+ for i in range(10):
+ # Draw new samples and minimize KL
+ KL = ift.MetricGaussianKL(mean, H, N_samples)
+ KL, convergence = minimizer(KL)
+ mean = KL.position
+
+ # Plot current reconstruction
+ plot = ift.Plot()
+ plot.add(signal(mock_position), title="ground truth")
+ plot.add(signal(KL.position), title="reconstruction")
+ plot.add([A1.force(KL.position), A1.force(mock_position)], title="power1")
+ plot.add([A2.force(KL.position), A2.force(mock_position)], title="power2")
+ plot.output(nx = 2, ny=2, ysize=10, xsize=10,
+ name=filename.format("loop_{:02d}".format(i)))
+
+ # Draw posterior samples
+ Nsamples = 20
+ KL = ift.MetricGaussianKL(mean, H, N_samples)
+ sc = ift.StatCalculator()
+ scA1 = ift.StatCalculator()
+ scA2 = ift.StatCalculator()
+ powers1 = []
+ powers2 = []
+ for sample in KL.samples:
+ sc.add(signal(sample + KL.position))
+ p1 = A1.force(sample + KL.position)
+ p2 = A2.force(sample + KL.position)
+ scA1.add(p1)
+ powers1.append(p1)
+ scA2.add(p2)
+ powers2.append(p2)
+
+ # Plotting
+ filename_res = filename.format("results")
+ plot = ift.Plot()
+ plot.add(sc.mean, title="Posterior Mean")
+ plot.add(ift.sqrt(sc.var), title="Posterior Standard Deviation")
+
+ powers1 = [A1.force(s + KL.position) for s in KL.samples]
+ powers2 = [A2.force(s + KL.position) for s in KL.samples]
+ plot.add(
+ powers1 + [scA1.mean,
+ A1.force(mock_position)],
+ title="Sampled Posterior Power Spectrum 1",
+ linewidth=[1.]*len(powers1) + [3., 3.])
+ plot.add(
+ powers2 + [scA2.mean,
+ A2.force(mock_position)],
+ title="Sampled Posterior Power Spectrum 2",
+ linewidth=[1.]*len(powers2) + [3., 3.])
+ plot.output(ny=2, nx=2, xsize=15, ysize=15, name=filename_res)
+ print("Saved results as '{}'.".format(filename_res))