bernoulli_demo.py 3.06 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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#####################################################################
# Bernoulli reconstruction
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# Reconstruct an event probability field with values between 0 and 1
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# from the observed events
# 1D (set mode=0), 2D (mode=1), or on the sphere (mode=2)
#####################################################################
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import numpy as np

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import nifty6 as ift
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if __name__ == '__main__':
    # Set up the position space of the signal
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    mode = 2
    if mode == 0:
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        # One-dimensional regular grid
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        position_space = ift.RGSpace(1024)
    elif mode == 1:
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        # Two-dimensional regular grid
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        position_space = ift.RGSpace([512, 512])
    else:
        # Sphere
        position_space = ift.HPSpace(128)
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    # Define harmonic space and transform
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    harmonic_space = position_space.get_default_codomain()
    HT = ift.HarmonicTransformOperator(harmonic_space, position_space)

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    position = ift.from_random(harmonic_space, 'normal')
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    # Define power spectrum and amplitudes
    def sqrtpspec(k):
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        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 operator and instrumental response
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    sky = ift.sigmoid(HT(A))
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    GR = ift.GeometryRemover(position_space)
    R = GR

    # Generate mock data
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    p = R(sky)
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    mock_position = ift.from_random(harmonic_space, 'normal')
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    tmp = p(mock_position).val.astype(np.float64)
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    data = ift.random.current_rng().binomial(1, tmp)
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    data = ift.Field.from_raw(R.target, data)
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    # Compute likelihood and Hamiltonian
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    position = ift.from_random(harmonic_space, 'normal')
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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)
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    minimizer = ift.NewtonCG(ic_newton)
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    ic_sampling = ift.GradientNormController(iteration_limit=100)

    # Minimize the Hamiltonian
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    H = ift.StandardHamiltonian(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')
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    plot.output(nx=3, xsize=16, ysize=9, title="results", name="bernoulli.png")