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# the Free Software Foundation, either version 3 of the License, or
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#
# 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.
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# You should have received a copy of the GNU General Public License
# along with this program. If not, see .
#
# Copyright(C) 2013-2019 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
import numpy as np
from ..domain_tuple import DomainTuple
from ..domains.power_space import PowerSpace
from ..field import Field
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
from ..sugar import makeOp
def _ceps_kernel(dof_space, k, a, k0):
return a**2/(1 + (k/k0)**2)**2
def CepstrumOperator(target, a, k0):
'''Turns a white Gaussian random field into a smooth field on a LogRGSpace.
Composed out of three operators:
sym @ qht @ diag(sqrt_ceps),
where sym is a :class:`SymmetrizingOperator`, qht is a :class:`QHTOperator`
and ceps is the so-called cepstrum:
.. math::
\\mathrm{sqrt\_ceps}(k) = \\frac{a}{1+(k/k0)^2}
These operators are combined in this fashion in order to generate:
- A field which is smooth, i.e. second derivatives are punished (note
that the sqrt-cepstrum is essentially proportional to 1/k**2).
- A field which is symmetric around the pixel in the middle of the space.
This is result of the :class:`SymmetrizingOperator` and needed in order to
decouple the degrees of freedom at the beginning and the end of the
amplitude whenever :class:`CepstrumOperator` is used as in
:class:`SLAmplitude`.
FIXME The prior on the zero mode is ...
Parameters
----------
target : LogRGSpace
Target domain of the operator, needs to be non-harmonic and
one-dimensional.
a : float
Strength of smoothness prior (positive only). FIXME
k0 : float
Cutoff of smothness prior in quefrency space (positive only). FIXME
'''
a, k0 = float(a), float(k0)
target = DomainTuple.make(target)
if a <= 0 or k0 <= 0:
raise ValueError
if len(target) > 1 or target[0].harmonic or len(target[0].shape) > 1:
raise TypeError
qht = QHTOperator(target)
dom = qht.domain[0]
sym = SymmetrizingOperator(target)
# Compute cepstrum field
dim = len(dom.shape)
shape = dom.shape
q_array = dom.get_k_array()
# Fill all non-zero modes
no_zero_modes = (slice(1, None),)*dim
ks = q_array[(slice(None),) + no_zero_modes]
cepstrum_field = np.zeros(shape)
cepstrum_field[no_zero_modes] = _ceps_kernel(dom, ks, a, k0)
# Fill zero-mode subspaces
for i in range(dim):
fst_dims = (slice(None),)*i
sl = fst_dims + (slice(1, None),)
sl2 = fst_dims + (0,)
cepstrum_field[sl2] = np.sum(cepstrum_field[sl], axis=i)
cepstrum = Field.from_global_data(dom, cepstrum_field)
return sym @ qht @ makeOp(cepstrum.sqrt())
def SLAmplitude(target, n_pix, a, k0, sm, sv, im, iv, keys=['tau', 'phi']):
'''Operator for parametrizing smooth amplitudes (square roots of power
spectra).
The general guideline for setting up generative models in IFT is to
transform the problem into the eigenbase of the prior and formulate the
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
double-logarithmic scale).
This function assembles an :class:`Operator` which maps two a-priori white
Gaussian random fields to a smooth amplitude which is composed out of
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)
This is then exponentiated and exponentially binned (in this order).
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`).
Parameters
----------
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`).
sm : float
Expected exponent of power law. FIXME
sv : float
Prior standard deviation of exponent of power law.
im : float
Expected y-intercept of power law. FIXME
iv : float
Prior standard deviation of y-intercept of power law.
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.
'''
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)
if sv <= 0 or iv <= 0:
raise ValueError
et = ExpTransform(target, n_pix)
dom = et.domain[0]
# Smooth component
dct = {'a': a, 'k0': k0}
smooth = CepstrumOperator(dom, **dct).ducktape(keys[0])
# Linear component
sl = SlopeOperator(dom)
mean = np.array([sm, im + sm*dom.t_0[0]])
sig = np.array([sv, iv])
mean = Field.from_global_data(sl.domain, mean)
sig = Field.from_global_data(sl.domain, sig)
linear = (sl @ OffsetOperator(mean) @ makeOp(sig)).ducktape(keys[1])
# Combine linear and smooth component
loglog_ampl = 0.5*(smooth + linear)
# Go from loglog-space to linear-linear-space
return et @ loglog_ampl.exp()