try operators with fixed input domain

demos/critical_filtering.py

@@ -16,17 +16,17 @@ def plot_parameters(m, t, p, p_d):
 
 
 class AdjointFFTResponse(ift.LinearOperator):
-    def __init__(self, FFT, R, default_spaces=None):
-        super(AdjointFFTResponse, self).__init__(default_spaces)
+    def __init__(self, FFT, R):
+        super(AdjointFFTResponse, self).__init__()
         self._domain = FFT.target
         self._target = R.target
         self.R = R
         self.FFT = FFT
 
-    def _times(self, x, spaces=None):
+    def _times(self, x):
         return self.R(self.FFT.adjoint_times(x))
 
-    def _adjoint_times(self, x, spaces=None):
+    def _adjoint_times(self, x):
         return self.FFT(self.R.adjoint_times(x))
 
     @property

demos/log_normal_wiener_filter.py

@@ -35,7 +35,7 @@ if __name__ == "__main__":
     #mask.val[N10*5:N10*9, N10*5:N10*9] = 0.
     R = ift.ResponseOperator(signal_space, sigma=(response_sigma,), exposure=(mask,)) #|\label{code:wf_response}|
     data_domain = R.target[0]
-    R_harmonic = ift.ComposedOperator([fft, R], default_spaces=[0, 0])
+    R_harmonic = ift.ComposedOperator([fft, R])
 
     # Setting up the noise covariance and drawing a random noise realization
     ndiag = ift.Field(data_domain, mock_signal.var()/signal_to_noise).weight(1)

demos/paper_demos/cartesian_wiener_filter.py

@@ -22,7 +22,17 @@ if __name__ == "__main__":
 
     signal_space_1 = ift.RGSpace([N_pixels_1], distances=L_1/N_pixels_1)
     harmonic_space_1 = signal_space_1.get_default_codomain()
-    fft_1 = ift.FFTOperator(harmonic_space_1, target=signal_space_1)
+    # Setting up the geometry |\label{code:wf_geometry}|
+    L_2 = 2. # Total side-length of the domain
+    N_pixels_2 = 512 # Grid resolution (pixels per axis)
+    signal_space_2 = ift.RGSpace([N_pixels_2], distances=L_2/N_pixels_2)
+    harmonic_space_2 = signal_space_2.get_default_codomain()
+
+    signal_domain = ift.DomainTuple.make((signal_space_1, signal_space_2))
+    mid_domain = ift.DomainTuple.make((signal_space_1, harmonic_space_2))
+    harmonic_domain = ift.DomainTuple.make((harmonic_space_1, harmonic_space_2))
+
+    fft_1 = ift.FFTOperator(harmonic_domain, space=0)
     power_space_1 = ift.PowerSpace(harmonic_space_1)
 
     mock_power_1 = ift.Field(power_space_1, val=power_spectrum_1(power_space_1.k_lengths))
@@ -39,13 +49,7 @@ if __name__ == "__main__":
         a = 4 * correlation_length_2 * field_variance_2**2
         return a / (1 + k * correlation_length_2) ** 2.5
 
-    # Setting up the geometry |\label{code:wf_geometry}|
-    L_2 = 2. # Total side-length of the domain
-    N_pixels_2 = 512 # Grid resolution (pixels per axis)
-
-    signal_space_2 = ift.RGSpace([N_pixels_2], distances=L_2/N_pixels_2)
-    harmonic_space_2 = signal_space_2.get_default_codomain()
-    fft_2 = ift.FFTOperator(harmonic_space_2, target=signal_space_2)
+    fft_2 = ift.FFTOperator(mid_domain, space=1)
     power_space_2 = ift.PowerSpace(harmonic_space_2)
 
     mock_power_2 = ift.Field(power_space_2, val=power_spectrum_2(power_space_2.k_lengths))
@@ -73,11 +77,11 @@ if __name__ == "__main__":
     mask_2 = ift.Field(signal_space_2, val=1.)
     mask_2.val[N2_10*7:N2_10*9] = 0.
 
-    R = ift.ResponseOperator((signal_space_1, signal_space_2),
+    R = ift.ResponseOperator(signal_domain,spaces=(0,1),
                              sigma=(response_sigma_1, response_sigma_2),
                              exposure=(mask_1, mask_2)) #|\label{code:wf_response}|
     data_domain = R.target
-    R_harmonic = ift.ComposedOperator([fft, R], default_spaces=(0, 1, 0, 1))
+    R_harmonic = ift.ComposedOperator([fft, R])
 
     # Setting up the noise covariance and drawing a random noise realization
     ndiag = ift.Field(data_domain, mock_signal.var()/signal_to_noise).weight(1)

demos/paper_demos/wiener_filter.py

@@ -34,7 +34,7 @@ if __name__ == "__main__":
     mask.val[N10*5:N10*9, N10*5:N10*9] = 0.
     R = ift.ResponseOperator(signal_space, sigma=(response_sigma,), exposure=(mask,))  #|\label{code:wf_response}|
     data_domain = R.target[0]
-    R_harmonic = ift.ComposedOperator([fft, R], default_spaces=[0, 0])
+    R_harmonic = ift.ComposedOperator([fft, R])
 
     # Setting up the noise covariance and drawing a random noise realization
     ndiag = ift.Field(data_domain, mock_signal.var()/signal_to_noise).weight(1)

demos/wiener_filter_via_curvature.py

@@ -45,7 +45,7 @@ if __name__ == "__main__":
 
     R = ift.ResponseOperator(signal_space, sigma=(response_sigma,))
     data_domain = R.target[0]
-    R_harmonic = ift.ComposedOperator([fft, R], default_spaces=[0, 0])
+    R_harmonic = ift.ComposedOperator([fft, R])
 
     N = ift.DiagonalOperator(ift.Field(data_domain,mock_signal.var()/signal_to_noise).weight(1))
     noise = ift.Field.from_random(domain=data_domain,

demos/wiener_filter_via_hamiltonian.py

@@ -5,17 +5,17 @@ np.random.seed(42)
 
 
 class AdjointFFTResponse(ift.LinearOperator):
-    def __init__(self, FFT, R, default_spaces=None):
-        super(AdjointFFTResponse, self).__init__(default_spaces)
+    def __init__(self, FFT, R):
+        super(AdjointFFTResponse, self).__init__()
         self._domain = FFT.target
         self._target = R.target
         self.R = R
         self.FFT = FFT
 
-    def _times(self, x, spaces=None):
+    def _times(self, x):
         return self.R(self.FFT.adjoint_times(x))
 
-    def _adjoint_times(self, x, spaces=None):
+    def _adjoint_times(self, x):
         return self.FFT(self.R.adjoint_times(x))
 
     @property

nifty/domain_tuple.py

@@ -104,3 +104,9 @@ class DomainTuple(object):
         if self is x:
             return False
         return self._dom != x._dom
+
+    def __str__(self):
+        res = "DomainTuple, len: " + str(len(self.domains))
+        for i in self.domains:
+            res += "\n" + str(i)
+        return res

nifty/field.py

@@ -510,8 +510,9 @@ class Field(object):
             # create a diagonal operator which is capable of taking care of the
             # axes-matching
             from .operators.diagonal_operator import DiagonalOperator
-            diag = DiagonalOperator(y.conjugate(), copy=False)
-            dotted = diag(x, spaces=spaces)
+            diag = DiagonalOperator(y.conjugate(), self.domain,
+                                    spaces=spaces, copy=False)
+            dotted = diag(x)
             return fct*dotted.sum(spaces=spaces)
 
     def norm(self):

nifty/library/critical_filter/critical_power_curvature.py

@@ -32,7 +32,7 @@ class CriticalPowerCurvature(InvertibleOperatorMixin, EndomorphicOperator):
                                                  preconditioner=preconditioner,
                                                  **kwargs)
 
-    def _times(self, x, spaces):
+    def _times(self, x):
         return self.T(x) + self.theta(x)
 
     # ---Mandatory properties and methods---

nifty/library/log_normal_wiener_filter/log_normal_wiener_filter_curvature.py

@@ -58,7 +58,7 @@ class LogNormalWienerFilterCurvature(InvertibleOperatorMixin,
 
     # ---Added properties and methods---
 
-    def _times(self, x, spaces):
+    def _times(self, x):
         part1 = self.S.inverse_times(x)
         # part2 = self._exppRNRexppd * x
         part3 = self._fft.adjoint_times(self._expp_sspace * self._fft(x))

nifty/library/wiener_filter/wiener_filter_curvature.py

@@ -48,7 +48,7 @@ class WienerFilterCurvature(InvertibleOperatorMixin, EndomorphicOperator):
 
     # ---Added properties and methods---
 
-    def _times(self, x, spaces):
+    def _times(self, x):
         res = self.R.adjoint_times(self.N.inverse_times(self.R(x)))
         res += self.S.inverse_times(x)
         return res

nifty/operators/composed_operator/composed_operator.py

@@ -29,9 +29,6 @@ class ComposedOperator(LinearOperator):
     ----------
     operators : tuple of NIFTy Operators
         The tuple of LinearOperators.
-    default_spaces : tuple of ints *optional*
-        Defines on which space(s) of a given field the Operator acts by
-        default (default: None)
 
 
     Attributes
@@ -48,7 +45,7 @@ class ComposedOperator(LinearOperator):
     TypeError
         Raised if
             * an element of the operator list is not an instance of the
-              LinearOperator-baseclass.
+              LinearOperator base class.
 
     Notes
     -----
@@ -64,8 +61,8 @@ class ComposedOperator(LinearOperator):
     >>> x2 = RGSpace(10)
     >>> k1 = RGRGTransformation.get_codomain(x1)
     >>> k2 = RGRGTransformation.get_codomain(x2)
-    >>> FFT1 = FFTOperator(domain=x1, target=k1)
-    >>> FFT2 = FFTOperator(domain=x2, target=k2)
+    >>> FFT1 = FFTOperator(domain=(x1,x2), target=(k1,x2), space=0)
+    >>> FFT2 = FFTOperator(domain=(k1,x2), target=(k1,k2), space=1)
     >>> FFT = ComposedOperator((FFT1, FFT2)
     >>> f = Field.from_random('normal', domain=(x1,x2))
     >>> FFT.times(f)
@@ -73,80 +70,50 @@ class ComposedOperator(LinearOperator):
     """
 
     # ---Overwritten properties and methods---
-    def __init__(self, operators, default_spaces=None):
-        super(ComposedOperator, self).__init__(default_spaces)
+    def __init__(self, operators):
+        super(ComposedOperator, self).__init__()
 
+        for i in range(1, len(operators)):
+            if operators[i].domain != operators[i-1].target:
+                raise ValueError("incompatible domains")
         self._operator_store = ()
         for op in operators:
             if not isinstance(op, LinearOperator):
                 raise TypeError("The elements of the operator list must be"
-                                "instances of the LinearOperator-baseclass")
+                                "instances of the LinearOperator base class")
             self._operator_store += (op,)
 
-    def _check_input_compatibility(self, x, spaces, inverse=False):
-        """
-        The input check must be disabled for the ComposedOperator, since it
-        is not easily forecasteable what the output of an operator-call
-        will look like.
-        """
-        if spaces is None:
-            spaces = self.default_spaces
-        return spaces
-
     # ---Mandatory properties and methods---
     @property
     def domain(self):
-        if not hasattr(self, '_domain'):
-            dom = ()
-            for op in self._operator_store:
-                dom += op.domain.domains
-            self._domain = DomainTuple.make(dom)
-        return self._domain
+        return self._operator_store[0].domain
 
     @property
     def target(self):
-        if not hasattr(self, '_target'):
-            tgt = ()
-            for op in self._operator_store:
-                tgt += op.target.domains
-            self._target = DomainTuple.make(tgt)
-        return self._target
+        return self._operator_store[-1].target
 
     @property
     def unitary(self):
         return False
 
-    def _times(self, x, spaces):
-        return self._times_helper(x, spaces, func='times')
+    def _times(self, x):
+        return self._times_helper(x, func='times')
 
-    def _adjoint_times(self, x, spaces):
-        return self._inverse_times_helper(x, spaces, func='adjoint_times')
+    def _adjoint_times(self, x):
+        return self._inverse_times_helper(x, func='adjoint_times')
 
-    def _inverse_times(self, x, spaces):
-        return self._inverse_times_helper(x, spaces, func='inverse_times')
+    def _inverse_times(self, x):
+        return self._inverse_times_helper(x, func='inverse_times')
 
-    def _adjoint_inverse_times(self, x, spaces):
-        return self._times_helper(x, spaces, func='adjoint_inverse_times')
+    def _adjoint_inverse_times(self, x):
+        return self._times_helper(x, func='adjoint_inverse_times')
 
-    def _times_helper(self, x, spaces, func):
-        space_index = 0
-        if spaces is None:
-            spaces = range(len(self.domain))
+    def _times_helper(self, x, func):
         for op in self._operator_store:
-            active_spaces = spaces[space_index:space_index+len(op.domain)]
-            space_index += len(op.domain)
-
-            x = getattr(op, func)(x, spaces=active_spaces)
+            x = getattr(op, func)(x)
         return x
 
-    def _inverse_times_helper(self, x, spaces, func):
-        space_index = 0
-        if spaces is None:
-            spaces = range(len(self.target))
-        rev_spaces = spaces[::-1]
+    def _inverse_times_helper(self, x, func):
         for op in reversed(self._operator_store):
-            active_spaces = rev_spaces[space_index:space_index+len(op.target)]
-            space_index += len(op.target)
-
-            x = getattr(op, func)(x, spaces=active_spaces[::-1])
+            x = getattr(op, func)(x)
         return x

nifty/operators/diagonal_operator/diagonal_operator.py

@@ -23,7 +23,7 @@ import numpy as np
 from ...field import Field
 from ...domain_tuple import DomainTuple
 from ..endomorphic_operator import EndomorphicOperator
-
+from ...nifty_utilities import cast_iseq_to_tuple
 
 class DiagonalOperator(EndomorphicOperator):
     """ NIFTY class for diagonal operators.
@@ -39,9 +39,6 @@ class DiagonalOperator(EndomorphicOperator):
         The diagonal entries of the operator.
     copy : boolean
         Internal copy of the diagonal (default: True)
-    default_spaces : tuple of ints *optional*
-        Defines on which space(s) of a given field the Operator acts by
-        default (default: None)
 
     Attributes
     ----------
@@ -55,9 +52,6 @@ class DiagonalOperator(EndomorphicOperator):
     self_adjoint : boolean
         Indicates whether the operator is self_adjoint or not.
 
-    Raises
-    ------
-
     See Also
     --------
     EndomorphicOperator
@@ -66,30 +60,48 @@ class DiagonalOperator(EndomorphicOperator):
 
     # ---Overwritten properties and methods---
 
-    def __init__(self, diagonal, copy=True, default_spaces=None):
-        super(DiagonalOperator, self).__init__(default_spaces)
+    def __init__(self, diagonal, domain=None, spaces=None, copy=True):
+        super(DiagonalOperator, self).__init__()
 
         if not isinstance(diagonal, Field):
             raise TypeError("Field object required")
+        if domain is None:
+            self._domain = diagonal.domain
+        else:
+            self._domain = DomainTuple.make(domain)
+        if spaces is None:
+            self._spaces = None
+            if diagonal.domain != self._domain:
+                raise ValueError("domain mismatch")
+        else:
+            self._spaces = cast_iseq_to_tuple(spaces)
+            nspc = len(self._spaces)
+            if nspc != len(diagonal.domain.domains):
+                raise ValueError("spaces and domain must have the same length")
+            if nspc > len(self._domain.domains):
+                raise ValueError("too many spaces")
+            if nspc > len(set(self._spaces)):
+                raise ValueError("non-unique space indices")
+            # if nspc==len(self.diagonal.domain.domains, we could do some optimization
+            for i, j  in enumerate(self._spaces):
+                if diagonal.domain[i] != self._domain[j]:
+                    raise ValueError("domain mismatch")
+
         self._diagonal = diagonal if not copy else diagonal.copy()
         self._self_adjoint = None
         self._unitary = None
 
-    def _times(self, x, spaces):
-        return self._times_helper(x, spaces, operation=lambda z: z.__mul__)
+    def _times(self, x):
+        return self._times_helper(x, lambda z: z.__mul__)
 
-    def _adjoint_times(self, x, spaces):
-        return self._times_helper(x, spaces,
-                                  operation=lambda z: z.conjugate().__mul__)
+    def _adjoint_times(self, x):
+        return self._times_helper(x, lambda z: z.conjugate().__mul__)
 
-    def _inverse_times(self, x, spaces):
-        return self._times_helper(x, spaces,
-                                  operation=lambda z: z.__rtruediv__)
+    def _inverse_times(self, x):
+        return self._times_helper(x, lambda z: z.__rtruediv__)
 
-    def _adjoint_inverse_times(self, x, spaces):
-        return self._times_helper(x, spaces,
-                                  operation=lambda z:
-                                      z.conjugate().__rtruediv__)
+    def _adjoint_inverse_times(self, x):
+        return self._times_helper(x, lambda z: z.conjugate().__rtruediv__)
 
     def diagonal(self, copy=True):
         """ Returns the diagonal of the Operator.
@@ -111,7 +123,7 @@ class DiagonalOperator(EndomorphicOperator):
 
     @property
     def domain(self):
-        return self._diagonal.domain
+        return self._domain
 
     @property
     def self_adjoint(self):
@@ -130,19 +142,13 @@ class DiagonalOperator(EndomorphicOperator):
 
     # ---Added properties and methods---
 
-    def _times_helper(self, x, spaces, operation):
-        # if the domain matches directly
-        # -> multiply the fields directly
-        if x.domain == self.domain:
-            # here the actual multiplication takes place
+    def _times_helper(self, x, operation):
+        if self._spaces is None:
             return operation(self._diagonal)(x)
 
-        if spaces is None:
-            active_axes = range(len(x.shape))
-        else:
-            active_axes = []
-            for space_index in spaces:
-                active_axes += x.domain.axes[space_index]
+        active_axes = []
+        for space_index in self._spaces:
+            active_axes += x.domain.axes[space_index]
 
         reshaper = [x.shape[i] if i in active_axes else 1
                     for i in range(len(x.shape))]

nifty/operators/endomorphic_operator/endomorphic_operator.py

@@ -28,12 +28,6 @@ class EndomorphicOperator(LinearOperator):
     LinearOperator. By definition, domain and target are the same in
     EndomorphicOperator.
 
-    Parameters
-    ----------
-    default_spaces : tuple of ints *optional*
-        Defines on which space(s) of a given field the Operator acts by
-        default (default: None)
-
     Attributes
     ----------
     domain : tuple of DomainObjects, i.e. Spaces and FieldTypes
@@ -56,37 +50,29 @@ class EndomorphicOperator(LinearOperator):
 
     # ---Overwritten properties and methods---
 
-    def inverse_times(self, x, spaces=None):
+    def inverse_times(self, x):
         if self.self_adjoint and self.unitary:
-            return self.times(x, spaces)
+            return self.times(x)
         else:
-            return super(EndomorphicOperator, self).inverse_times(
-                                                              x=x,
-                                                              spaces=spaces)
+            return super(EndomorphicOperator, self).inverse_times(x)
 
-    def adjoint_times(self, x, spaces=None):
+    def adjoint_times(self, x):
         if self.self_adjoint:
-            return self.times(x, spaces)
+            return self.times(x)
         else:
-            return super(EndomorphicOperator, self).adjoint_times(
-                                                                x=x,
-                                                                spaces=spaces)
+            return super(EndomorphicOperator, self).adjoint_times(x)
 
-    def adjoint_inverse_times(self, x, spaces=None):
+    def adjoint_inverse_times(self, x):
         if self.self_adjoint:
-            return self.inverse_times(x, spaces)
+            return self.inverse_times(x)
         else:
-            return super(EndomorphicOperator, self).adjoint_inverse_times(
-                                                                x=x,
-                                                                spaces=spaces)
+            return super(EndomorphicOperator, self).adjoint_inverse_times(x)
 
-    def inverse_adjoint_times(self, x, spaces=None):
+    def inverse_adjoint_times(self, x):
         if self.self_adjoint:
-            return self.inverse_times(x, spaces)
+            return self.inverse_times(x)
         else:
-            return super(EndomorphicOperator, self).inverse_adjoint_times(
-                                                                x=x,
-                                                                spaces=spaces)
+            return super(EndomorphicOperator, self).inverse_adjoint_times(x)
 
     # ---Mandatory properties and methods---
 

nifty/operators/fft_operator/fft_operator.py

@@ -46,6 +46,8 @@ class FFTOperator(LinearOperator):
     domain: Space or single-element tuple of Spaces
         The domain of the data that is input by "times" and output by
         "adjoint_times".
+    space: the index of the space on which the operator should act
+        If None, it is set to 0 if domain contains exactly one space
     target: Space or single-element tuple of Spaces (optional)
         The domain of the data that is output by "times" and input by
         "adjoint_times".
@@ -58,10 +60,10 @@ class FFTOperator(LinearOperator):
 
     Attributes
     ----------
-    domain: Tuple of Spaces (with one entry)
+    domain: Tuple of Spaces
         The domain of the data that is input by "times" and output by
         "adjoint_times".
-    target: Tuple of Spaces (with one entry)
+    target: Tuple of Spaces
         The domain of the data that is output by "times" and input by
         "adjoint_times".
     unitary: bool
@@ -72,7 +74,6 @@ class FFTOperator(LinearOperator):
     ------
     ValueError:
         if "domain" or "target" are not of the proper type.
-
     """
 
     # ---Class attributes---
@@ -92,62 +93,53 @@ class FFTOperator(LinearOperator):
 
     # ---Overwritten properties and methods---
 
-    def __init__(self, domain, target=None, default_spaces=None):
-        super(FFTOperator, self).__init__(default_spaces)
+    def __init__(self, domain, target=None, space=None):
+        super(FFTOperator, self).__init__()
 
         # Initialize domain and target
         self._domain = DomainTuple.make(domain)
-        if len(self.domain) != 1:
-            raise ValueError("TransformationOperator accepts only exactly one "
-                             "space as input domain.")
-
+        if space is None:
+            if len(self._domain.domains) != 1:
+                raise ValueError("need a Field with exactly one domain")
+            space = 0
+        space = int(space)
+        if (space<0) or space>=len(self._domain.domains):
+            raise ValueError("space index out of range")
+        self._space = space
+
+        adom = self.domain[self._space]
         if target is None:
-            target = (self.domain[0].get_default_codomain(), )
+            target = [ dom for dom in self.domain ]
+            target[self._space] = adom.get_default_codomain()
+
         self._target = DomainTuple.make(target)
-        if len(self.target) != 1:
-            raise ValueError("TransformationOperator accepts only exactly one "
-                             "space as output target.")
-        self.domain[0].check_codomain(self.target[0])
-        self.target[0].check_codomain(self.domain[0])
+        atgt = self._target[self._space]
+        adom.check_codomain(atgt)
+        atgt.check_codomain(adom)
 
         # Create transformation instances
         forward_class = self.transformation_dictionary[
-                (self.domain[0].__class__, self.target[0].__class__)]
+                (adom.__class__, atgt.__class__)]
         backward_class = self.transformation_dictionary[
-                (self.target[0].__class__, self.domain[0].__class__)]
-
-        self._forward_transformation = forward_class(
-            self.domain[0], self.target[0])
-
-        self._backward_transformation = backward_class(
-            self.target[0], self.domain[0])
-
-    def _times_helper(self, x, spaces, other, trafo):
-        if spaces is None:
-            # this case means that x lives on only one space, which is
-            # identical to the space in the domain of `self`. Otherwise the
-            # input check of LinearOperator would have failed.
-            axes = x.domain.axes[0]
-            result_domain