getting_started_3.py 4.48 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/>.
#
# Copyright(C) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.

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import nifty5 as ift
import numpy as np


def get_random_LOS(n_los):
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    starts = list(np.random.uniform(0, 1, (n_los, 2)).T)
    ends = list(np.random.uniform(0, 1, (n_los, 2)).T)
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    return starts, ends

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if __name__ == '__main__':
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    # FIXME description of the tutorial
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    np.random.seed(42)
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    position_space = ift.RGSpace([128, 128])
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    # Setting up an amplitude model
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    A = ift.AmplitudeModel(position_space, 16, 1, 10, -4., 1, 0., 1.)
    dummy = ift.from_random('normal', A.domain)
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    # Building the model for a correlated signal
    harmonic_space = position_space.get_default_codomain()
    ht = ift.HarmonicTransformOperator(harmonic_space, position_space)
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    power_space = A.target[0]
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    power_distributor = ift.PowerDistributor(harmonic_space, power_space)
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    dummy = ift.Field.from_random('normal', harmonic_space)
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    domain = ift.MultiDomain.union((A.domain,
                                    ift.MultiDomain.make({
                                        'xi': harmonic_space
                                    })))
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    correlated_field = ht(power_distributor(A)*ift.FieldAdapter(domain, "xi"))
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    # alternatively to the block above one can do:
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    # correlated_field = ift.CorrelatedField(position_space, A)
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    # apply some nonlinearity
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    signal = ift.positive_tanh(correlated_field)
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    # Building the Line of Sight response
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    LOS_starts, LOS_ends = get_random_LOS(100)
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    R = ift.LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends)
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    # build signal response model and model likelihood
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    signal_response = R(signal)
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    # specify noise
    data_space = R.target
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    noise = .001
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    N = ift.ScalingOperator(noise, data_space)
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    # generate mock data
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    MOCK_POSITION = ift.from_random('normal', domain)
    data = signal_response(MOCK_POSITION) + N.draw_sample()
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    # set up model likelihood
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    likelihood = ift.GaussianEnergy(mean=data, covariance=N)(signal_response)
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    # set up minimization and inversion schemes
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    ic_sampling = ift.GradientNormController(iteration_limit=100)
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    ic_newton = ift.GradInfNormController(
        name='Newton', tol=1e-7, iteration_limit=1000)
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    minimizer = ift.NewtonCG(ic_newton)
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    # build model Hamiltonian
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    H = ift.Hamiltonian(likelihood, ic_sampling)
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    INITIAL_POSITION = ift.from_random('normal', domain)
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    position = INITIAL_POSITION
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    plot = ift.Plot()
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    plot.add(signal(MOCK_POSITION), title='Ground Truth')
    plot.add(R.adjoint_times(data), title='Data')
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    plot.add([A(MOCK_POSITION.extract(A.domain))], title='Power Spectrum')
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    plot.output(ny=1, nx=3, xsize=24, ysize=6, name="setup.png")
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    # number of samples used to estimate the KL
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    N_samples = 20
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    for i in range(2):
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        KL = ift.KL_Energy(position, H, N_samples)
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        KL, convergence = minimizer(KL)
        position = KL.position

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        plot = ift.Plot()
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        plot.add(signal(KL.position), title="reconstruction")
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        plot.add(
            [
                A(KL.position.extract(A.domain)),
                A(MOCK_POSITION.extract(A.domain))
            ],
            title="power")
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        plot.output(ny=1, ysize=6, xsize=16, name="loop.png")
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    KL = ift.KL_Energy(position, H, N_samples)
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    plot = ift.Plot()
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    sc = ift.StatCalculator()
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    for sample in KL.samples:
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        sc.add(signal(sample + KL.position))
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    plot.add(sc.mean, title="Posterior Mean")
    plot.add(ift.sqrt(sc.var), title="Posterior Standard Deviation")
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    powers = [A((s + KL.position).extract(A.domain)) for s in KL.samples]
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    plot.add(
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        [A(KL.position.extract(A.domain)),
         A(MOCK_POSITION.extract(A.domain))] + powers,
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        title="Sampled Posterior Power Spectrum")
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    plot.output(ny=1, nx=3, xsize=24, ysize=6, name="results.png")