DiagonalOperator takes and returns diagonal without volume factors

demos/critical_filtering.py

@@ -72,7 +72,7 @@ if __name__ == "__main__":
     R = AdjointFFTResponse(fft, Instrument)
 
     noise = 1.
-    N = ift.DiagonalOperator(ift.Field(s_space,noise).weight(1))
+    N = ift.DiagonalOperator(ift.Field(s_space,noise))
     n = ift.Field.from_random(domain=s_space,
                           random_type='normal',
                           std=np.sqrt(noise),

demos/log_normal_wiener_filter.py

@@ -38,7 +38,7 @@ if __name__ == "__main__":
     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)
+    ndiag = ift.Field(data_domain, mock_signal.var()/signal_to_noise)
     N = ift.DiagonalOperator(ndiag)
     noise = ift.Field.from_random(domain=data_domain, random_type='normal',
                               std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)

demos/paper_demos/cartesian_wiener_filter.py

@@ -62,7 +62,7 @@ if __name__ == "__main__":
     diagonal = mock_power.power_synthesize_special(spaces=(0, 1))**2
     diagonal = diagonal.real
 
-    S = ift.DiagonalOperator(diagonal)
+    S = ift.DiagonalOperator(diagonal.weight(-1))
 
 
     np.random.seed(10)
@@ -84,7 +84,7 @@ if __name__ == "__main__":
     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)
+    ndiag = ift.Field(data_domain, mock_signal.var()/signal_to_noise)
     N = ift.DiagonalOperator(ndiag)
     noise = ift.Field.from_random(domain=data_domain, random_type='normal',
                                   std=mock_signal.std()/np.sqrt(signal_to_noise),
@@ -93,7 +93,7 @@ if __name__ == "__main__":
 
     # Wiener filter
     j = R_harmonic.adjoint_times(N.inverse_times(data))
-    ctrl = ift.GradientNormController(verbose=True, iteration_limit=100)
+    ctrl = ift.GradientNormController(verbose=True, tol_abs_gradnorm=0.1)
     inverter = ift.ConjugateGradient(controller=ctrl)
     wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic, inverter=inverter)
 

demos/paper_demos/wiener_filter.py

@@ -37,7 +37,7 @@ if __name__ == "__main__":
     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)
+    ndiag = ift.Field(data_domain, mock_signal.var()/signal_to_noise)
     N = ift.DiagonalOperator(ndiag)
     noise = ift.Field.from_random(domain=data_domain, random_type='normal',
                                   std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)

demos/wiener_filter_via_curvature.py

@@ -47,7 +47,7 @@ if __name__ == "__main__":
     data_domain = R.target[0]
     R_harmonic = ift.ComposedOperator([fft, R])
 
-    N = ift.DiagonalOperator(ift.Field(data_domain,mock_signal.var()/signal_to_noise).weight(1))
+    N = ift.DiagonalOperator(ift.Field(data_domain,mock_signal.var()/signal_to_noise))
     noise = ift.Field.from_random(domain=data_domain,
                               random_type='normal',
                               std=mock_signal.std()/np.sqrt(signal_to_noise),

demos/wiener_filter_via_hamiltonian.py

@@ -62,7 +62,7 @@ if __name__ == "__main__":
     # Adding a harmonic transformation to the instrument
     R = AdjointFFTResponse(fft, Instrument)
     signal_to_noise = 1.
-    ndiag = ift.Field(s_space, ss.var()/signal_to_noise).weight(1)
+    ndiag = ift.Field(s_space, ss.var()/signal_to_noise)
     N = ift.DiagonalOperator(ndiag)
     n = ift.Field.from_random(domain=s_space,
                           random_type='normal',

nifty/field.py

@@ -420,7 +420,7 @@ class Field(object):
             res *= tmp
         return res
 
-    def weight(self, power=1, inplace=False, spaces=None):
+    def weight(self, power=1, spaces=None, out=None):
         """ Weights the pixels of `self` with their invidual pixel-volume.
 
         Parameters
@@ -428,20 +428,25 @@ class Field(object):
         power : number
             The pixels get weighted with the volume-factor**power.
 
-        inplace : boolean
-            If True, `self` will be weighted and returned. Otherwise, a copy
-            is made.
-
         spaces : tuple of ints
             Determines on which subspace the operation takes place.
 
+        out : Field or None
+            if not None, the result is returned in a new Field
+            otherwise the contents of "out" are overwritten with the result.
+            "out" may be identical to "self"!
+
         Returns
         -------
         out : Field
             The weighted field.
 
         """
-        new_field = self if inplace else self.copy()
+        if out is None:
+            out = self.copy()
+        else:
+            if out is not self:
+                out.copy_content_from(self)
 
         if spaces is None:
             spaces = range(len(self.domain))
@@ -458,12 +463,12 @@ class Field(object):
                 new_shape[self.domain.axes[ind][0]:
                           self.domain.axes[ind][-1]+1] = wgt.shape
                 wgt = wgt.reshape(new_shape)
-                new_field *= wgt**power
+                out *= wgt**power
         fct = fct**power
         if fct != 1.:
-            new_field *= fct
+            out *= fct
 
-        return new_field
+        return out
 
     def vdot(self, x=None, spaces=None):
         """ Computes the volume-factor-aware dot product of 'self' with x.
@@ -474,8 +479,8 @@ class Field(object):
             The domain of x must contain `self.domain`
 
         spaces : tuple of ints
-            If the domain of `self` and `x` are not the same, `spaces` specfies
-            the mapping.
+            If the domain of `self` and `x` are not the same, `spaces` defines
+            which domains of `x` are mapped to those of `self`.
 
         Returns
         -------
@@ -487,9 +492,9 @@ class Field(object):
                              "the NIFTy field class")
 
         # Compute the dot respecting the fact of discrete/continuous spaces
-        fct = 1.
         tmp = self.scalar_weight(spaces)
         if tmp is None:
+            fct = 1.
             y = self.weight(power=1)
         else:
             y = self
@@ -498,13 +503,14 @@ class Field(object):
         if spaces is None:
             return fct*dobj.vdot(y.val.ravel(), x.val.ravel())
         else:
-            # create a diagonal operator which is capable of taking care of the
-            # axes-matching
-            from .operators.diagonal_operator import DiagonalOperator
-            diag = DiagonalOperator(y.conjugate(), self.domain,
-                                    spaces=spaces, copy=False)
-            dotted = diag(x)
-            return fct*dotted.sum(spaces=spaces)
+            spaces = utilities.cast_iseq_to_tuple(spaces)
+            active_axes = []
+            for i in spaces:
+                active_axes += self.domain.axes[i]
+            res = 0.
+            for sl in utilities.get_slice_list(self.shape, active_axes):
+                res += dobj.vdot(y.val, x.val[sl])
+            return res*fct
 
     def norm(self):
         """ Computes the L2-norm of the field values.
@@ -515,7 +521,7 @@ class Field(object):
             The L2-norm of the field values.
 
         """
-        return dobj.sqrt(dobj.abs(self.vdot(x=self)))
+        return np.sqrt(np.abs(self.vdot(x=self)))
 
     def conjugate(self):
         """ Returns the complex conjugate of the field.
@@ -566,6 +572,10 @@ class Field(object):
     def sum(self, spaces=None):
         return self._contraction_helper('sum', spaces)
 
+    def integrate(self, spaces=None):
+        tmp = self.weight(1, spaces=spaces)
+        return tmp.sum(spaces)
+
     def prod(self, spaces=None):
         return self._contraction_helper('prod', spaces)
 

nifty/library/critical_filter/critical_power_energy.py

@@ -88,7 +88,7 @@ class CriticalPowerEnergy(Energy):
     @property
     def gradient(self):
         gradient = -self._theta.weight(-1)
-        gradient += (self._rho_prime).weight(-1)
+        gradient += self._rho_prime.weight(-1)
         gradient += self._Tt
         gradient = gradient.real
         return gradient

nifty/operators/diagonal_operator/diagonal_operator.py

@@ -37,8 +37,12 @@ class DiagonalOperator(EndomorphicOperator):
     ----------
     diagonal : Field
         The diagonal entries of the operator.
-    copy : boolean
-        Internal copy of the diagonal (default: True)
+    domain : tuple of DomainObjects, i.e. Spaces and FieldTypes
+        The domain on which the Operator's input Field lives.
+        If None, use the domain of "diagonal".
+    spaces : tuple of int
+        The elements of "domain" on which the operator acts.
+        If None, it acts on all elements.
 
     Attributes
     ----------
@@ -60,7 +64,7 @@ class DiagonalOperator(EndomorphicOperator):
 
     # ---Overwritten properties and methods---
 
-    def __init__(self, diagonal, domain=None, spaces=None, copy=True):
+    def __init__(self, diagonal, domain=None, spaces=None):
         super(DiagonalOperator, self).__init__()
 
         if not isinstance(diagonal, Field):
@@ -87,7 +91,7 @@ class DiagonalOperator(EndomorphicOperator):
                 if diagonal.domain[i] != self._domain[j]:
                     raise ValueError("domain mismatch")
 
-        self._diagonal = diagonal if not copy else diagonal.copy()
+        self._diagonal = diagonal.weight(1)
         self._self_adjoint = None
         self._unitary = None
 
@@ -103,21 +107,16 @@ class DiagonalOperator(EndomorphicOperator):
     def _adjoint_inverse_times(self, x):
         return self._times_helper(x, lambda z: z.conjugate().__rtruediv__)
 
-    def diagonal(self, copy=True):
+    def diagonal(self):
         """ Returns the diagonal of the Operator.
 
-        Parameters
-        ----------
-        copy : boolean
-            Whether the returned Field should be copied or not.
-
         Returns
         -------
         out : Field
             The diagonal of the Operator.
 
         """
-        return self._diagonal.copy() if copy else self._diagonal
+        return self._diagonal.weight(-1)
 
     # ---Mandatory properties and methods---
 

nifty/operators/laplace_operator/laplace_operator.py

@@ -111,7 +111,7 @@ class LaplaceOperator(EndomorphicOperator):
         ret /= np.sqrt(dposc)
         ret[prefix + (slice(None, 2),)] = 0.
         ret[prefix + (-1,)] = 0.
-        return Field(self.domain, val=ret).weight(-0.5, spaces=(self._space,))
+        return Field(self.domain, val=ret)
 
     def _adjoint_times(self, x):
         axes = x.domain.axes[self._space]
@@ -122,7 +122,7 @@ class LaplaceOperator(EndomorphicOperator):
         sl_r = prefix + (slice(1, None),)  # "right" slice
         dpos = self._dpos.reshape((1,)*axis + (nval-1,))
         dposc = self._dposc.reshape((1,)*axis + (nval,))
-        y = x.copy().weight(power=0.5, spaces=(self._space,)).val
+        y = x.val.copy()
         y /= np.sqrt(dposc)
         y[prefix + (slice(None, 2),)] = 0.
         y[prefix + (-1,)] = 0.
@@ -131,4 +131,4 @@ class LaplaceOperator(EndomorphicOperator):
         ret[sl_l] = deriv
         ret[prefix + (-1,)] = 0.
         ret[sl_r] -= deriv
-        return Field(self.domain, val=ret).weight(-1, spaces=(self._space,))
+        return Field(self.domain, val=ret)

nifty/operators/response_operator/response_operator.py

@@ -62,7 +62,7 @@ class ResponseOperator(LinearOperator):
                                                  space=spaces[x])
                             for x in range(nsigma)]
         kernel_exposure = [DiagonalOperator(Field(self._domain[spaces[x]],
-                                                  exposure[x]),
+                                                  exposure[x]).weight(-1),
                                             domain=self._domain,
                                             spaces=(spaces[x],))
                            for x in range(nsigma)]

nifty/sugar.py

@@ -26,11 +26,27 @@ from . import Space,\
                   FFTOperator,\
                   sqrt
 
-__all__ = ['create_power_operator',
+__all__ = ['create_power_field',
+           'create_power_operator',
            'generate_posterior_sample',
            'create_composed_fft_operator']
 
 
+def create_power_field(domain, power_spectrum, dtype=None):
+    if not callable(power_spectrum):
+        raise TypeError("power_spectrum must be callable")
+    power_domain = PowerSpace(domain)
+
+    fp = Field(power_domain, val=power_spectrum(power_domain.k_lengths),
+               dtype=dtype)
+    f = fp.power_synthesize_special()
+
+    if not issubclass(fp.dtype.type, np.complexfloating):
+        f = f.real
+
+    f **= 2
+    return f
+
 def create_power_operator(domain, power_spectrum, dtype=None):
     """ Creates a diagonal operator with the given power spectrum.
 
@@ -53,21 +69,7 @@ def create_power_operator(domain, power_spectrum, dtype=None):
     DiagonalOperator : An operator that implements the given power spectrum.
 
     """
-
-    if not callable(power_spectrum):
-        raise TypeError("power_spectrum must be callable")
-    power_domain = PowerSpace(domain)
-
-    fp = Field(power_domain, val=power_spectrum(power_domain.k_lengths),
-               dtype=dtype)
-    f = fp.power_synthesize_special()
-
-    if not issubclass(fp.dtype.type, np.complexfloating):
-        f = f.real
-
-    f **= 2
-    return DiagonalOperator(Field(domain,f).weight(1))
-
+    return DiagonalOperator(create_power_field(domain, power_spectrum, dtype))
 
 def generate_posterior_sample(mean, covariance):
     """ Generates a posterior sample from a Gaussian distribution with given
@@ -96,10 +98,10 @@ def generate_posterior_sample(mean, covariance):
     R = covariance.R
     N = covariance.N
 
-    power = sqrt(S.diagonal().power_analyze())
+    power = sqrt(S.diagonal().weight(1).power_analyze())
     mock_signal = power.power_synthesize(real_signal=True)
 
-    noise = N.diagonal().weight(-1)
+    noise = N.diagonal()
 
     mock_noise = Field.from_random(random_type="normal", domain=N.domain,
                                    dtype=noise.dtype.type)

test/test_field.py

@@ -32,7 +32,7 @@ from nifty2go import Field,\
                   DomainTuple,\
                   DiagonalOperator
 
-from nifty2go.sugar import create_power_operator
+from nifty2go.sugar import create_power_field
 
 from test.common import expand
 
@@ -80,8 +80,8 @@ class Test_Functionality(unittest.TestCase):
             sk = fp.power_synthesize(spaces=(0, 1), real_signal=True)
 
             sp = sk.power_analyze(spaces=(0, 1), keep_phase_information=False)
-            ps1 += sp.sum(spaces=1)/fp2.sum()
-            ps2 += sp.sum(spaces=0)/fp1.sum()
+            ps1 += sp.integrate(spaces=1)/fp2.sum()
+            ps2 += sp.integrate(spaces=0)/fp1.sum()
 
         assert_allclose(ps1.val/samples, fp1.val, rtol=0.2)
         assert_allclose(ps2.val/samples, fp2.val, rtol=0.2)
@@ -104,12 +104,10 @@ class Test_Functionality(unittest.TestCase):
         spec2 = lambda k: 42/(1+k)**3
         fp2 = Field(p2, val=spec2(p2.k_lengths))
 
-        S_1 = create_power_operator(space1, lambda x: np.sqrt(spec1(x)))
-        S_2 = create_power_operator(space2, lambda x: np.sqrt(spec2(x)))
-        S_1 = DiagonalOperator(S_1.diagonal().weight(-1),domain=fulldomain,
-                               spaces=0)
-        S_2 = DiagonalOperator(S_2.diagonal().weight(-1),domain=fulldomain,
-                               spaces=1)
+        S_1 = create_power_field(space1, lambda x: np.sqrt(spec1(x)))
+        S_1 = DiagonalOperator(S_1.weight(-1), domain=fulldomain, spaces=0)
+        S_2 = create_power_field(space2, lambda x: np.sqrt(spec2(x)))
+        S_2 = DiagonalOperator(S_2.weight(-1), domain=fulldomain, spaces=1)
 
         samples = 500
         ps1 = 0.
@@ -119,8 +117,8 @@ class Test_Functionality(unittest.TestCase):
             rand_k = Field.from_random('normal', domain=fulldomain)
             sk = S_1.times(S_2.times(rand_k))
             sp = sk.power_analyze(spaces=(0, 1), keep_phase_information=False)
-            ps1 += sp.sum(spaces=1)/fp2.sum()
-            ps2 += sp.sum(spaces=0)/fp1.sum()
+            ps1 += sp.integrate(spaces=1)/fp2.sum()
+            ps2 += sp.integrate(spaces=0)/fp1.sum()
 
         assert_allclose(ps1.val/samples, fp1.val, rtol=0.2)
         assert_allclose(ps2.val/samples, fp2.val, rtol=0.2)

test/test_minimization/test_minimizers.py

@@ -21,7 +21,7 @@ class Test_Minimizers(unittest.TestCase):
         starting_point = ift.Field.from_random('normal', domain=space)*10
         covariance_diagonal = ift.Field.from_random(
                                   'uniform', domain=space) + 0.5
-        covariance = ift.DiagonalOperator(covariance_diagonal)
+        covariance = ift.DiagonalOperator(covariance_diagonal.weight(-1))
         required_result = ift.Field(space, val=1.)
 
         IC = ift.GradientNormController(tol_abs_gradnorm=1e-5)

test/test_operators/test_diagonal_operator.py

@@ -17,8 +17,8 @@ from test.common import expand