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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) 2013-2017 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 numpy as np

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from . import Space,\
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                  PowerSpace,\
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                  Field,\
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                  ComposedOperator,\
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                  DiagonalOperator,\
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                  FFTOperator,\
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                  sqrt
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__all__ = ['create_power_field',
           'create_power_operator',
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           'generate_posterior_sample',
           'create_composed_fft_operator']
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def create_power_field(domain, power_spectrum, dtype=None):
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    if not callable(power_spectrum):  # we have a Field living on a PowerSpace
        if not isinstance(power_spectrum, Field):
            raise TypeError("Field object expected")
        if len(power_spectrum.domain) != 1:
            raise ValueError("exactly one domain required")
        if not isinstance(power_spectrum.domain[0], PowerSpace):
            raise TypeError("PowerSpace required")
        power_domain = power_spectrum.domain[0]
        fp = Field(power_domain, val=power_spectrum.val, dtype=dtype)
    else:
        power_domain = PowerSpace(domain)
        fp = Field(power_domain, val=power_spectrum(power_domain.k_lengths),
                   dtype=dtype)
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    f = fp.power_synthesize_special()

    if not issubclass(fp.dtype.type, np.complexfloating):
        f = f.real

    f **= 2
    return f

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def create_power_operator(domain, power_spectrum, dtype=None):
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    """ Creates a diagonal operator with the given power spectrum.
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    Constructs a diagonal operator that lives over the specified domain.
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    Parameters
    ----------
    domain : DomainObject
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        Domain over which the power operator shall live.
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    power_spectrum : callable
        A method that implements the square root of a power spectrum as a
        function of k.
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    dtype : type *optional*
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        dtype that the field holding the power spectrum shall use
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        (default : None).
        if dtype == None: the dtype of `power_spectrum` will be used.

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    Returns
    -------
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    DiagonalOperator : An operator that implements the given power spectrum.
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    """
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    return DiagonalOperator(create_power_field(domain, power_spectrum, dtype))
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def generate_posterior_sample(mean, covariance):
    """ Generates a posterior sample from a Gaussian distribution with given
    mean and covariance
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    This method generates samples by setting up the observation and
    reconstruction of a mock signal in order to obtain residuals of the right
    correlation which are added to the given mean.
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    Parameters
    ----------
    mean : Field
        the mean of the posterior Gaussian distribution
    covariance : WienerFilterCurvature
        The posterior correlation structure consisting of a
        response operator, noise covariance and prior signal covariance

    Returns
    -------
    sample : Field
        Returns the a sample from the Gaussian of given mean and covariance.

    """

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    S = covariance.S
    R = covariance.R
    N = covariance.N
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    power = sqrt(S.diagonal().weight(1).power_analyze())
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    mock_signal = power.power_synthesize(real_signal=True)

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    noise = N.diagonal()
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    mock_noise = Field.from_random(random_type="normal", domain=N.domain,
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                                   dtype=noise.dtype.type)
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    mock_noise *= sqrt(noise)

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    mock_data = R(mock_signal) + mock_noise
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    mock_j = R.adjoint_times(N.inverse_times(mock_data))
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    mock_m = covariance.inverse_times(mock_j)
    sample = mock_signal - mock_m + mean
    return sample
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def create_composed_fft_operator(domain, codomain=None, all_to='other'):
    fft_op_list = []

    if codomain is None:
        codomain = [None]*len(domain)
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    interdomain = list(domain.domains)
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    for i, space in enumerate(domain):
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        cospace = codomain[i]
        if not isinstance(space, Space):
            continue
        if (all_to == 'other' or
                (all_to == 'position' and space.harmonic) or
                (all_to == 'harmonic' and not space.harmonic)):
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            if codomain[i] is None:
                interdomain[i] = domain[i].get_default_codomain()
            else:
                interdomain[i] = codomain[i]
            fft_op_list += [FFTOperator(domain=domain, target=interdomain,
                                        space=i)]
        domain = interdomain
    return ComposedOperator(fft_op_list)