Merge branch 'linear-amplitude-model' into 'NIFTy_5'

Linear amplitude model

See merge request !309

nifty5/__init__.py

@@ -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,

nifty5/library/smooth_linear_amplitude.py

@@ -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

nifty5/operators/energy_operators.py

@@ -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,
                                                      LinearOperator):
@@ -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`,