bernoulli_demo.py 2.96 KB
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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/>.
#
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# Copyright(C) 2013-2019 Max-Planck-Society
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#
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# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
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import nifty5 as ift
import numpy as np


if __name__ == '__main__':
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    # FIXME ABOUT THIS CODE
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    np.random.seed(41)

    # Set up the position space of the signal
    #
    # # One dimensional regular grid with uniform exposure
    # position_space = ift.RGSpace(1024)
    # exposure = np.ones(position_space.shape)

    # Two-dimensional regular grid with inhomogeneous exposure
    position_space = ift.RGSpace([512, 512])

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    #  Sphere with uniform exposure
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    # position_space = ift.HPSpace(128)
    # exposure = ift.Field.full(position_space, 1.)

    # Defining harmonic space and transform
    harmonic_space = position_space.get_default_codomain()
    HT = ift.HarmonicTransformOperator(harmonic_space, position_space)

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    position = ift.from_random('normal', harmonic_space)
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    # Define power spectrum and amplitudes
    def sqrtpspec(k):
        return 1. / (20. + k**2)

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    A = ift.create_power_operator(harmonic_space, sqrtpspec)
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    # Set up a sky model
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    sky = ift.positive_tanh(HT(A))
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    GR = ift.GeometryRemover(position_space)
    # Set up instrumental response
    R = GR

    # Generate mock data
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    p = R(sky)
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    mock_position = ift.from_random('normal', harmonic_space)
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    tmp = p(mock_position).to_global_data().astype(np.float64)
    data = np.random.binomial(1, tmp)
    data = ift.Field.from_global_data(R.target, data)
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    # Compute likelihood and Hamiltonian
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    position = ift.from_random('normal', harmonic_space)
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    likelihood = ift.BernoulliEnergy(data)(p)
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    ic_newton = ift.DeltaEnergyController(name='Newton', iteration_limit=100,
                                          tol_rel_deltaE=1e-8)
    minimizer = ift.NewtonCG(ic_newton)
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    ic_sampling = ift.GradientNormController(iteration_limit=100)

    # Minimize the Hamiltonian
    H = ift.Hamiltonian(likelihood, ic_sampling)
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    H = ift.EnergyAdapter(position, H, want_metric=True)
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    # minimizer = ift.L_BFGS(ic_newton)
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    H, convergence = minimizer(H)
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    reconstruction = sky(H.position)
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    plot = ift.Plot()
    plot.add(reconstruction, title='reconstruction')
    plot.add(GR.adjoint_times(data), title='data')
    plot.add(sky(mock_position), title='truth')
    plot.output(nx=3, xsize=16, ysize=5, title="results",
                name="bernoulli.png")