Commit cb0eb935 authored by Martin Reinecke's avatar Martin Reinecke

Merge branch 'NIFTy_5' into parallizing_mirrored_samples

parents a4c1edea 53c0d64a
Pipeline #46642 passed with stages
in 8 minutes and 41 seconds
......@@ -74,7 +74,8 @@ from .minimization.metric_gaussian_kl import MetricGaussianKL
from .sugar import *
from .plot import Plot
from .library.smooth_linear_amplitude import SLAmplitude, CepstrumOperator
from .library.smooth_linear_amplitude import (
SLAmplitude, LinearSLAmplitude, CepstrumOperator)
from .library.inverse_gamma_operator import InverseGammaOperator
from .library.los_response import LOSResponse
from .library.dynamic_operator import (dynamic_operator,
......@@ -169,6 +169,18 @@ def SLAmplitude(*, target, n_pix, a, k0, sm, sv, im, iv, keys=['tau', 'phi']):
which returns on its target a power spectrum which consists out of a
smooth and a linear part.
return LinearSLAmplitude(target=target, n_pix=n_pix, a=a, k0=k0, sm=sm,
sv=sv, im=im, iv=iv, keys=keys).exp()
def LinearSLAmplitude(*, target, n_pix, a, k0, sm, sv, im, iv,
keys=['tau', 'phi']):
'''LinearOperator for parametrizing smooth log-amplitudes (square roots of
power spectra).
Logarithm of SLAmplitude
See documentation of SLAmplitude for more details
if not (isinstance(n_pix, int) and isinstance(target, PowerSpace)):
raise TypeError
......@@ -196,4 +208,4 @@ def SLAmplitude(*, target, n_pix, a, k0, sm, sv, im, iv, keys=['tau', 'phi']):
loglog_ampl = 0.5*(smooth + linear)
# Go from loglog-space to linear-linear-space
return et @ loglog_ampl.exp()
return et @ loglog_ampl
......@@ -20,6 +20,7 @@ import numpy as np
from .. import utilities
from ..domain_tuple import DomainTuple
from ..field import Field
from ..multi_field import MultiField
from ..linearization import Linearization
from ..sugar import makeDomain, makeOp
from .linear_operator import LinearOperator
......@@ -121,7 +122,7 @@ class GaussianEnergy(EnergyOperator):
def __init__(self, mean=None, covariance=None, domain=None):
if mean is not None and not isinstance(mean, Field):
if mean is not None and not isinstance(mean, (Field, MultiField)):
raise TypeError
if covariance is not None and not isinstance(covariance,
......@@ -307,7 +308,6 @@ class StandardHamiltonian(EnergyOperator):
Tells an internal :class:`SamplingEnabler` which convergence criterion
to use to draw Gaussian samples.
See also
`Encoding prior knowledge in the structure of the likelihood`,
......@@ -392,7 +392,10 @@ class _OpSum(Operator):
op = lin1._jac._myadd(lin2._jac, False)
res =, op(x.jac))
if lin1._metric is not None and lin2._metric is not None:
res = res.add_metric(self._op1(x)._metric + self._op2(x)._metric)
from .sandwich_operator import SandwichOperator
met = lin1._metric._myadd(lin2._metric, False)
met = SandwichOperator.make(x.jac, met)
res = res.add_metric(met)
return res
def _simplify_for_constant_input_nontrivial(self, c_inp):
......@@ -66,9 +66,12 @@ class ScalingOperator(EndomorphicOperator):
return x
if fct == 0.:
return full(self.domain, 0.)
if (mode & 10) != 0:
if (mode & MODES_WITH_ADJOINT) != 0:
fct = np.conj(fct)
if (mode & 12) != 0:
if (mode & MODES_WITH_INVERSE) != 0:
fct = 1./fct
return x*fct
......@@ -29,6 +29,7 @@ from .multi_field import MultiField
from .operators.block_diagonal_operator import BlockDiagonalOperator
from .operators.diagonal_operator import DiagonalOperator
from .operators.distributors import PowerDistributor
from .plot import Plot
__all__ = ['PS_field', 'power_analyze', 'create_power_operator',
'create_harmonic_smoothing_operator', 'from_random',
......@@ -37,7 +38,7 @@ __all__ = ['PS_field', 'power_analyze', 'create_power_operator',
'sin', 'cos', 'tan', 'sinh', 'cosh',
'absolute', 'one_over', 'clip', 'sinc',
'conjugate', 'get_signal_variance', 'makeOp', 'domain_union',
'get_default_codomain', 'single_plot']
def PS_field(pspace, func):
......@@ -434,3 +435,14 @@ def get_default_codomain(domainoid, space=None):
ret = [dom for dom in domainoid]
ret[space] = domainoid[space].get_default_codomain()
return DomainTuple.make(ret)
def single_plot(field, **kwargs):
"""Creates a single plot using `Plot`.
Keyword arguments are passed to both `Plot.add` and `Plot.output`.
p = Plot()
p.add(field, **kwargs)
if 'title' in kwargs:
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