replace InvertibleOperatorMixin with InversionEnabler; move...

replace InvertibleOperatorMixin with InversionEnabler; move PowerSpectrum-related functionality out of Field

nifty/field.py

@@ -19,13 +19,13 @@
 from __future__ import division, print_function
 from builtins import range
 import numpy as np
-from .spaces.power_space import PowerSpace
 from . import nifty_utilities as utilities
 from .random import Random
 from .domain_tuple import DomainTuple
 from functools import reduce
 from . import dobj
 
+
 class Field(object):
     """ The discrete representation of a continuous field over multiple spaces.
 
@@ -80,7 +80,8 @@ class Field(object):
                 raise ValueError("Domain mismatch")
             self._val = dobj.from_object(val.val, dtype=dtype, copy=copy)
         elif (np.isscalar(val)):
-            self._val = dobj.full(self.domain.shape, dtype=dtype, fill_value=val)
+            self._val = dobj.full(self.domain.shape, dtype=dtype,
+                                  fill_value=val)
         elif isinstance(val, dobj.data_object):
             if self.domain.shape == val.shape:
                 self._val = dobj.from_object(val, dtype=dtype, copy=copy)
@@ -180,10 +181,6 @@ class Field(object):
         -------
         out : Field
             The output object.
-
-        See Also
-        --------
-        power_synthesize
         """
 
         domain = DomainTuple.make(domain)
@@ -191,182 +188,6 @@ class Field(object):
         return Field(domain=domain, val=generator_function(dtype=dtype,
                      shape=domain.shape, **kwargs))
 
-    # ---Powerspectral methods---
-
-    def power_analyze(self, spaces=None, binbounds=None,
-                      keep_phase_information=False):
-        """ Computes the square root power spectrum for a subspace of `self`.
-
-        Creates a PowerSpace for the space addressed by `spaces` with the given
-        binning and computes the power spectrum as a Field over this
-        PowerSpace. This can only be done if the subspace to  be analyzed is a
-        harmonic space. The resulting field has the same units as the initial
-        field, corresponding to the square root of the power spectrum.
-
-        Parameters
-        ----------
-        spaces : int *optional*
-            The subspace for which the powerspectrum shall be computed.
-            (default : None).
-        binbounds : array-like *optional*
-            Inner bounds of the bins (default : None).
-            if binbounds==None : bins are inferred.
-        keep_phase_information : boolean, *optional*
-            If False, return a real-valued result containing the power spectrum
-            of the input Field.
-            If True, return a complex-valued result whose real component
-            contains the power spectrum computed from the real part of the
-            input Field, and whose imaginary component contains the power
-            spectrum computed from the imaginary part of the input Field.
-            The absolute value of this result should be identical to the output
-            of power_analyze with keep_phase_information=False.
-            (default : False).
-
-        Raise
-        -----
-        TypeError
-            Raised if any of the input field's domains is not harmonic
-
-        Returns
-        -------
-        out : Field
-            The output object. Its domain is a PowerSpace and it contains
-            the power spectrum of 'self's field.
-
-        See Also
-        --------
-        power_synthesize, PowerSpace
-
-        """
-
-        # check if all spaces in `self.domain` are either harmonic or
-        # power_space instances
-        for sp in self.domain:
-            if not sp.harmonic and not isinstance(sp, PowerSpace):
-                print("WARNING: Field has a space in `domain` which is "
-                      "neither harmonic nor a PowerSpace.")
-
-        # check if the `spaces` input is valid
-        if spaces is None:
-            spaces = range(len(self.domain))
-        else:
-            spaces = utilities.cast_iseq_to_tuple(spaces)
-
-        if len(spaces) == 0:
-            raise ValueError("No space for analysis specified.")
-
-        if keep_phase_information:
-            parts = [self.real*self.real, self.imag*self.imag]
-        else:
-            parts = [self.real*self.real + self.imag*self.imag]
-
-        parts = [ part.weight(1,spaces) for part in parts ]
-        for space_index in spaces:
-            parts = [self._single_power_analyze(field=part,
-                                                idx=space_index,
-                                                binbounds=binbounds)
-                     for part in parts]
-
-        return parts[0] + 1j*parts[1] if keep_phase_information else parts[0]
-
-    @staticmethod
-    def _single_power_analyze(field, idx, binbounds):
-        from .operators.power_projection_operator import PowerProjectionOperator
-        power_domain = PowerSpace(field.domain[idx], binbounds)
-        ppo = PowerProjectionOperator(field.domain, power_domain, idx)
-        return ppo(field.weight(-1))
-
-    def _compute_spec(self, spaces):
-        from .operators.power_projection_operator import PowerProjectionOperator
-        from .basic_arithmetics import sqrt
-        if spaces is None:
-            spaces = range(len(self.domain))
-        else:
-            spaces = utilities.cast_iseq_to_tuple(spaces)
-
-        # create the result domain
-        result_domain = list(self.domain)
-
-        spec = sqrt(self)
-        for i in spaces:
-            result_domain[i] = self.domain[i].harmonic_partner
-            ppo = PowerProjectionOperator(result_domain, self.domain[i], i)
-            spec = ppo.adjoint_times(spec)
-
-        return spec
-
-    def power_synthesize(self, spaces=None, real_power=True, real_signal=True):
-        """ Yields a sampled field with `self`**2 as its power spectrum.
-
-        This method draws a Gaussian random field in the harmonic partner
-        domain of this field's domains, using this field as power spectrum.
-
-        Parameters
-        ----------
-        spaces : {tuple, int, None} *optional*
-            Specifies the subspace containing all the PowerSpaces which
-            should be converted (default : None).
-            if spaces==None : Tries to convert the whole domain.
-        real_power : boolean *optional*
-            Determines whether the power spectrum is treated as intrinsically
-            real or complex (default : True).
-        real_signal : boolean *optional*
-            True will result in a purely real signal-space field
-            (default : True).
-
-        Returns
-        -------
-        out : Field
-            The output object. A random field created with the power spectrum
-            stored in the `spaces` in `self`.
-
-        Notes
-        -----
-        For this the spaces specified by `spaces` must be a PowerSpace.
-        This expects this field to be the square root of a power spectrum, i.e.
-        to have the unit of the field to be sampled.
-
-        See Also
-        --------
-        power_analyze
-
-        Raises
-        ------
-        ValueError : If domain specified by `spaces` is not a PowerSpace.
-
-        """
-
-        spec = self._compute_spec(spaces)
-
-        # create random samples: one or two, depending on whether the
-        # power spectrum is real or complex
-        result = [self.from_random('normal', mean=0., std=1.,
-                                   domain=spec.domain,
-                                   dtype=np.float if real_signal
-                                   else np.complex)
-                  for x in range(1 if real_power else 2)]
-
-        # MR: dummy call - will be removed soon
-        if real_signal:
-            self.from_random('normal', mean=0., std=1.,
-                             domain=spec.domain, dtype=np.float)
-
-        # apply the rescaler to the random fields
-        result[0] *= spec.real
-        if not real_power:
-            result[1] *= spec.imag
-
-        return result[0] if real_power else result[0] + 1j*result[1]
-
-    def power_synthesize_special(self, spaces=None):
-        spec = self._compute_spec(spaces)
-
-        # MR: dummy call - will be removed soon
-        self.from_random('normal', mean=0., std=1.,
-                         domain=spec.domain, dtype=np.complex)
-
-        return spec.real
-
     # ---Properties---
 
     @property

nifty/library/critical_filter/critical_power_curvature.py

 from ...operators.endomorphic_operator import EndomorphicOperator
-from ...operators.invertible_operator_mixin import InvertibleOperatorMixin
 from ...operators.diagonal_operator import DiagonalOperator
 
 
-class CriticalPowerCurvature(InvertibleOperatorMixin, EndomorphicOperator):
+class CriticalPowerCurvature(EndomorphicOperator):
     """The curvature of the CriticalPowerEnergy.
 
     This operator implements the second derivative of the
     CriticalPowerEnergy used in some minimization algorithms or
-    for error estimates of the powerspectrum.
+    for error estimates of the power spectrum.
 
 
     Parameters
@@ -21,15 +20,14 @@ class CriticalPowerCurvature(InvertibleOperatorMixin, EndomorphicOperator):
 
     # ---Overwritten properties and methods---
 
-    def __init__(self, theta, T, inverter, preconditioner=None, **kwargs):
+    def __init__(self, theta, T):
         self.theta = DiagonalOperator(theta)
         self.T = T
-        if preconditioner is None:
-            preconditioner = self.theta.inverse_times
-        super(CriticalPowerCurvature, self).__init__(
-                                                 inverter=inverter,
-                                                 preconditioner=preconditioner,
-                                                 **kwargs)
+        super(CriticalPowerCurvature, self).__init__()
+
+    @property
+    def preconditioner(self):
+        return self.theta.inverse_times
 
     def _times(self, x):
         return self.T(x) + self.theta(x)

nifty/library/critical_filter/critical_power_energy.py

 from ...energies.energy import Energy
 from ...operators.smoothness_operator import SmoothnessOperator
+from ...operators.inversion_enabler import InversionEnabler
 from . import CriticalPowerCurvature
 from ...memoization import memo
 
-from ...sugar import generate_posterior_sample
+from ...sugar import generate_posterior_sample, power_analyze
 from ... import Field, exp
 
 
@@ -95,8 +96,9 @@ class CriticalPowerEnergy(Energy):
 
     @property
     def curvature(self):
-        curvature = CriticalPowerCurvature(theta=self._theta.weight(-1),
-                                           T=self.T, inverter=self._inverter)
+        curvature = InversionEnabler(CriticalPowerCurvature(
+                    theta=self._theta.weight(-1),
+                    T=self.T), inverter=self._inverter)
         return curvature
 
     # ---Added properties and methods---
@@ -119,8 +121,9 @@ class CriticalPowerEnergy(Energy):
                     # self.logger.info("Drawing sample %i" % i)
                     posterior_sample = generate_posterior_sample(
                                                             self.m, self.D)
-                    projected_sample = posterior_sample.power_analyze(
-                     binbounds=self.position.domain[0].binbounds)
+                    projected_sample = power_analyze(
+                        posterior_sample,
+                        binbounds=self.position.domain[0].binbounds)
                     w += (projected_sample) * self.rho
                 w /= float(self.samples)
             else:

nifty/library/log_normal_wiener_filter/log_normal_wiener_filter_curvature.py

-from ...operators import EndomorphicOperator,\
-                            InvertibleOperatorMixin
+from ...operators import EndomorphicOperator
 from ...memoization import memo
 from ...basic_arithmetics import exp
 from ...sugar import create_composed_fft_operator
 
 
-class LogNormalWienerFilterCurvature(InvertibleOperatorMixin,
-                                     EndomorphicOperator):
+class LogNormalWienerFilterCurvature(EndomorphicOperator):
     """The curvature of the LogNormalWienerFilterEnergy.
 
     This operator implements the second derivative of the
@@ -24,7 +22,7 @@ class LogNormalWienerFilterCurvature(InvertibleOperatorMixin,
        The prior signal covariance
     """
 
-    def __init__(self, R, N, S, d, position, inverter, fft4exp=None, **kwargs):
+    def __init__(self, R, N, S, d, position, fft4exp=None):
         self.R = R
         self.N = N
         self.S = S
@@ -37,9 +35,7 @@ class LogNormalWienerFilterCurvature(InvertibleOperatorMixin,
         else:
             self._fft = fft4exp
 
-        super(LogNormalWienerFilterCurvature, self).__init__(
-                                                 inverter=inverter,
-                                                 **kwargs)
+        super(LogNormalWienerFilterCurvature, self).__init__()
 
     @property
     def domain(self):

nifty/library/log_normal_wiener_filter/log_normal_wiener_filter_energy.py

@@ -2,7 +2,7 @@ from ...energies.energy import Energy
 from ...memoization import memo
 from . import LogNormalWienerFilterCurvature
 from ...sugar import create_composed_fft_operator
-
+from ...operators.inversion_enabler import InversionEnabler
 
 class LogNormalWienerFilterEnergy(Energy):
     """The Energy for the log-normal Wiener filter.
@@ -47,20 +47,21 @@ class LogNormalWienerFilterEnergy(Energy):
     @memo
     def value(self):
         return 0.5*(self.position.vdot(self._Sp) +
-                    self.curvature._Rexppd.vdot(self.curvature._NRexppd))
+                    self.curvature.op._Rexppd.vdot(self.curvature.op._NRexppd))
 
     @property
     @memo
     def gradient(self):
-        return self._Sp + self.curvature._exppRNRexppd
+        return self._Sp + self.curvature.op._exppRNRexppd
 
     @property
     @memo
     def curvature(self):
-        return LogNormalWienerFilterCurvature(R=self.R, N=self.N, S=self.S,
-                                              d=self.d, position=self.position,
-                                              fft4exp=self._fft,
-                                              inverter=self._inverter)
+        return InversionEnabler(
+            LogNormalWienerFilterCurvature(R=self.R, N=self.N, S=self.S,
+                                           d=self.d, position=self.position,
+                                           fft4exp=self._fft),
+            inverter=self._inverter)
 
     @property
     @memo

nifty/library/wiener_filter/wiener_filter_curvature.py

-from ...operators import EndomorphicOperator,\
-                         InvertibleOperatorMixin
+from ...operators import EndomorphicOperator
 
 
-class WienerFilterCurvature(InvertibleOperatorMixin, EndomorphicOperator):
+class WienerFilterCurvature(EndomorphicOperator):
     """The curvature of the WienerFilterEnergy.
 
     This operator implements the second derivative of the
@@ -20,16 +19,15 @@ class WienerFilterCurvature(InvertibleOperatorMixin, EndomorphicOperator):
        The prior signal covariance
     """
 
-    def __init__(self, R, N, S, inverter, preconditioner=None, **kwargs):
+    def __init__(self, R, N, S):
         self.R = R
         self.N = N
         self.S = S
-        if preconditioner is None:
-            preconditioner = self.S.times
-        super(WienerFilterCurvature, self).__init__(
-                                                 inverter=inverter,
-                                                 preconditioner=preconditioner,
-                                                 **kwargs)
+        super(WienerFilterCurvature, self).__init__()
+
+    @property
+    def preconditioner(self):
+        return self.S.times
 
     @property
     def domain(self):

nifty/library/wiener_filter/wiener_filter_energy.py

 from ...energies.energy import Energy
 from ...memoization import memo
+from ...operators.inversion_enabler import InversionEnabler
 from . import WienerFilterCurvature
 
 
@@ -49,8 +50,9 @@ class WienerFilterEnergy(Energy):
     @property
     @memo
     def curvature(self):
-        return WienerFilterCurvature(R=self.R, N=self.N, S=self.S,
-                                     inverter=self._inverter)
+        return InversionEnabler(WienerFilterCurvature(R=self.R, N=self.N,
+                                                      S=self.S),
+                                inverter=self._inverter)
 
     @property
     @memo

nifty/operators/__init__.py

@@ -11,7 +11,7 @@ from .direct_smoothing_operator import DirectSmoothingOperator
 
 from .fft_operator import FFTOperator
 
-from .invertible_operator_mixin import InvertibleOperatorMixin
+from .inversion_enabler import InversionEnabler
 
 from .composed_operator import ComposedOperator
 

nifty/operators/invertible_operator_mixin.py → nifty/operators/inversion_enabler.py

@@ -16,60 +16,82 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from builtins import object
 from ..energies import QuadraticEnergy
 from ..field import Field
+from .linear_operator import LinearOperator
 
 
-class InvertibleOperatorMixin(object):
-    """ Mixin class to invert implicit defined operators.
+class InversionEnabler(LinearOperator):
 
-    This class provides the functionality necessary to invert the application
-    of a given implicitly defined operator on a field. Inheriting
-    functionality from this class provides the derived class with the
-    operations inverse to the defined operator applications
-    (e.g. .inverse_times if .times is defined and
-    .adjoint_times if .adjoint_inverse_times is defined)
+    def __init__(self, op, inverter, preconditioner=None):
+        self._op = op
+        self._inverter = inverter
+        if preconditioner is None and hasattr(op, "preconditioner"):
+            self._preconditioner = op.preconditioner
+        else:
+            self._preconditioner = preconditioner
+        super(InversionEnabler, self).__init__()
 
-    Parameters
-    ----------
-    inverter : Inverter
-        An instance of an Inverter class.
-    """
+    @property
+    def domain(self):
+        return self._op.domain
 
-    def __init__(self, inverter, preconditioner=None, *args, **kwargs):
-        self.__inverter = inverter
-        self._preconditioner = preconditioner
-        super(InvertibleOperatorMixin, self).__init__(*args, **kwargs)
+    @property
+    def target(self):
+        return self._op.target
+
+    @property
+    def unitary(self):
+        return self._op.unitary
+
+    @property
+    def op(self):
+        return self._op
 
     def _times(self, x):
-        x0 = Field.zeros(self.target, dtype=x.dtype)
-        (result, convergence) = self.__inverter(QuadraticEnergy(
-                                           A=self.inverse_times,
+        try:
+            res = self._op._times(x)
+        except NotImplementedError:
+            x0 = Field.zeros(self.target, dtype=x.dtype)
+            (result, convergence) = self._inverter(QuadraticEnergy(
+                                           A=self._op.inverse_times,