test_power.py 2.91 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) 2013-2018 Max-Planck-Society
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#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.

import unittest
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import nifty5 as ift
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import numpy as np
from itertools import product
from test.common import expand
from numpy.testing import assert_allclose


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def _flat_PS(k):
    return np.ones_like(k)


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class Energy_Tests(unittest.TestCase):
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    @expand(product([ift.RGSpace(64, distances=.789),
                     ift.RGSpace([32, 32], distances=.789)],
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                    [ift.library.Exponential, ift.library.Linear],
                    [132, 42, 3]))
    def testNonlinearPower(self, space, nonlinearity, seed):
        np.random.seed(seed)
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        f = nonlinearity()
        dim = len(space.shape)
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        hspace = space.get_default_codomain()
        ht = ift.HarmonicTransformOperator(hspace, space)
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        binbounds = ift.PowerSpace.useful_binbounds(hspace, logarithmic=True)
        pspace = ift.PowerSpace(hspace, binbounds=binbounds)
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        Dist = ift.PowerDistributor(target=hspace, power_space=pspace)
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        xi = ift.Field.from_random(domain=hspace, random_type='normal')

        def pspec(k): return 1 / (1 + k**2)**dim
        tau0 = ift.PS_field(pspace, pspec)
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        A = Dist(ift.sqrt(tau0))
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        n = ift.Field.from_random(domain=space, random_type='normal')
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        s = ht(xi * A)
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        R = ift.ScalingOperator(10., space)
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        diag = ift.full(space, 1.)
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        N = ift.DiagonalOperator(diag)
        d = R(f(s)) + n

        IC = ift.GradientNormController(
            iteration_limit=100,
            tol_abs_gradnorm=1e-5)

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        S = ift.create_power_operator(hspace, power_spectrum=_flat_PS)
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        D = ift.library.NonlinearWienerFilterEnergy(
            position=xi,
            d=d,
            Instrument=R,
            nonlinearity=f,
            power=A,
            N=N,
            S=S,
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            ht=ht,
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            iteration_controller=IC).curvature
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        energy = ift.library.NonlinearPowerEnergy(
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            position=tau0,
            d=d,
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            xi=xi,
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            D=D,
            Instrument=R,
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            Distributor=Dist,
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            nonlinearity=f,
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            ht=ht,
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            N=N,
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            samples=10)
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        ift.extra.check_value_gradient_consistency(energy, ntries=10)