smooth_linear_amplitude.py 5.24 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 numpy as np
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from ..domains.power_space import PowerSpace
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from ..field import Field
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from ..operators.exp_transform import ExpTransform
from ..operators.offset_operator import OffsetOperator
from ..operators.qht_operator import QHTOperator
from ..operators.slope_operator import SlopeOperator
from ..operators.symmetrizing_operator import SymmetrizingOperator
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from ..sugar import makeOp
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def _ceps_kernel(dof_space, k, a, k0):
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    return a**2/(1 + (k/k0)**2)**2
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def _create_cepstrum_amplitude_field(domain, cepstrum):
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    dim = len(domain.shape)
    shape = domain.shape
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    q_array = domain.get_k_array()
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    # Fill all non-zero modes
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    no_zero_modes = (slice(1, None),)*dim
    ks = q_array[(slice(None),) + no_zero_modes]
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    cepstrum_field = np.zeros(shape)
    cepstrum_field[no_zero_modes] = cepstrum(ks)

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    # Fill zero-mode subspaces
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    for i in range(dim):
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        fst_dims = (slice(None),)*i
        sl = fst_dims + (slice(1, None),)
        sl2 = fst_dims + (0,)
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        cepstrum_field[sl2] = np.sum(cepstrum_field[sl], axis=i)
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    return Field.from_global_data(domain, cepstrum_field)
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def CepstrumOperator(domain, a, k0):
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    '''
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    .. math::
        C(k) = \\left(\\frac{a}{1+(k/k0)^2}\\right)^2
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    '''
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    if a <= 0 or k0 <= 0:
        raise ValueError

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    qht = QHTOperator(target=domain)
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    dof_space = qht.domain[0]
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    sym = SymmetrizingOperator(domain)
    kern = lambda k: _ceps_kernel(dof_space, k, a, k0)
    cepstrum = _create_cepstrum_amplitude_field(dof_space, kern)
    return sym @ qht @ makeOp(cepstrum.sqrt())


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def SLAmplitude(target, n_pix, a, k0, sm, sv, im, iv, keys=['tau', 'phi']):
    '''Operator for parametrizing smooth amplitudes (square roots of power
    spectra).
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    The general guideline for setting up generative models in IFT is to
    transform the problem into the eigenbase of the prior and formulate the
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    generative model in this base. This is done here for the case of an
    amplitude which is smooth and has a linear component (both on
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    double-logarithmic scale).

    This function assembles an :class:`Operator` which maps two a-priori white
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    Gaussian random fields to a smooth amplitude which is composed out of
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    a linear and a smooth component.

    On double-logarithmic scale, i.e. both x and y-axis on logarithmic scale,
    the output of the generated operator is:

        AmplitudeOperator = 0.5*(smooth_component + linear_component)
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    This is then exponentiated and exponentially binned (in this order).
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    The prior on the linear component is parametrized by four real numbers,
    being expected value and prior variance on the slope and the y-intercept
    of the linear function.

    The prior on the smooth component is parametrized by two real numbers: the
    strength and the cutoff of the smoothness prior (see :class:`CepstrumOperator`).
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    Parameters
    ----------
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    n_pix : int
        Number of pixels of the space in which the .
    target : PowerSpace
        Target of the Operator.
    a : float
        Strength of smoothness prior (see :class:`CepstrumOperator`).
    k0 : float
        Cutoff of smothness prior in quefrency space (see :class:`CepstrumOperator`).
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    sm : float
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        Expected exponent of power law. FIXME
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    sv : float
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        Prior variance of exponent of power law.
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    im : float
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        Expected y-intercept of power law. FIXME
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    iv : float
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        Prior variance of y-intercept of power law.
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    Returns
    -------
    Operator
        Operator which is defined on the space of white excitations fields and
        which returns on its target a power spectrum which consists out of a
        smooth and a linear part.
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    '''
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    if not (isinstance(n_pix, int) and isinstance(target, PowerSpace)):
        raise TypeError

    a, k0 = float(a), float(k0)
    sm, sv, im, iv = float(sm), float(sv), float(im), float(iv)
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    if sv <= 0 or iv <= 0:
        raise ValueError
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    et = ExpTransform(target, n_pix)
    dom = et.domain[0]

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    # Smooth component
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    dct = {'a': a, 'k0': k0}
    smooth = CepstrumOperator(dom, **dct).ducktape(keys[0])
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    # Linear component
    phi_mean = np.array([sm, im + sm*dom.t_0[0]])
    phi_sig = np.array([sv, iv])
    slope = SlopeOperator(dom)
    phi_mean = Field.from_global_data(slope.domain, phi_mean)
    phi_sig = Field.from_global_data(slope.domain, phi_sig)
    linear = slope(OffsetOperator(phi_mean)(makeOp(phi_sig))).ducktape(keys[1])

    # Combine linear and smooth component
    loglog_ampl = 0.5*(smooth + linear)
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    # Go from loglog-space to linear-linear-space
    return et(loglog_ampl.exp())