propertification

docs/source/concepts/domains.rst

@@ -7,7 +7,15 @@ NIFTy's domain classes
 Abstract base class
 -------------------
 
-:class:`Domain` is the abstract ancestor for all of NIFTy's domains.
+One of the fundamental building blocks of the NIFTy4 framework is the *domain*.
+Its required capabilities are expressed by the abstract :class:`Domain` class.
+A domain must be able to answer the following queries:
+
+- its total number of data entries (pixels), which is accessible via the
+  :attr:`~Domain.size` property
+- the shape of the array that is supposed to hold these data entries
+  (obtainable by means of the :attr:`~Domain.shape` property)
+- equality comparison to another :class:`Domain` instance
 
 
 Unstructured domains
@@ -25,18 +33,31 @@ Structured domains
 ------------------
 
 In contrast to unstructured domains, these domains have an assigned geometry.
-NIFTy requires these domains to provide the volume elements of their grid cells.
-The additional methods are described in the abstract class
-:class:`StructuredDomain`.
-
-NIFTy comes with several concrete subclasses of :class:`StructuredDomain`.
-
-:class:`RGSpace` represents a regular Cartesian grid with an arbitrary
-number of dimensions, which is supposed to be periodic in each dimension.
-This domain can be constructed to represent either position or harmonic space.
-
-:class:`HPSpace` and :class:`GLSpace` describe pixelisations of the
-2-sphere; their counterpart in harmonic space is :class:`LMSpace`, which
-contains spherical harmonic coefficients.
-
-:class:`PowerSpace` is used to describe one-dimensional power spectra.
+NIFTy requires them to provide the volume elements of their grid cells.
+The additional methods are specified in the abstract class
+:class:`StructuredDomain`:
+
+- Methods :meth:`~StructuredDomain.scalar_dvol`,
+  :meth:`~StructuredDomain.dvol`, and  :meth:`~StructuredDomain.total_volume`
+  provide information about the domain's pixel volume(s) and its total volume.
+- The property :attr:`~StructuredDomain.harmonic` specifies whether a domain
+  is harmonic (i.e. describes a frequency space) or not
+- Iff the domain is harmonic, the methods
+  :meth:`~StructuredDomain.get_k_length_array`,
+  :meth:`~StructuredDomain.get_unique_k_lengths`, and
+  :meth:`~StructuredDomain.get_fft_smoothing_kernel_function` provide absolute
+  distances of the individual grid cells from the origin and assist with
+  Gaussian convolution.
+
+NIFTy comes with several concrete subclasses of :class:`StructuredDomain`:
+
+- :class:`RGSpace` represents a regular Cartesian grid with an arbitrary
+  number of dimensions, which is supposed to be periodic in each dimension.
+- :class:`HPSpace` and :class:`GLSpace` describe pixelisations of the
+  2-sphere; their counterpart in harmonic space is :class:`LMSpace`, which
+  contains spherical harmonic coefficients.
+- :class:`PowerSpace` is used to describe one-dimensional power spectra.
+
+Among these, :class:`RGSpace` can be harmonic or not (depending on constructor arguments), :class:`GLSpace`, :class:`HPSpace`, and :class:`PowerSpace` are
+pure position domains (i.e. nonharmonic), and :class:`LMSpace` is always
+harmonic.

docs/source/start.rst

@@ -42,9 +42,11 @@ One of the fundamental building blocks of the NIFTy4 framework is the *domain*.
 Its required capabilities are expressed by the abstract :class:`Domain` class.
 A domain must be able to answer the following queries:
 
-- its total number of data entries (pixels)
-- the shape of the array that is supposed to hold them
-- equality/inequality to another :class:`Domain` instance
+- its total number of data entries (pixels), which is accessible via the
+  :attr:`~Domain.size` property
+- the shape of the array that is supposed to hold these data entries
+  (obtainable by means of the :attr:`~Domain.shape` property)
+- equality comparison to another :class:`Domain` instance
 
 
 Unstructured domains

nifty4/domains/dof_space.py

@@ -39,9 +39,11 @@ class DOFSpace(StructuredDomain):
     def size(self):
         return len(self._dvol)
 
+    @property
     def scalar_dvol(self):
         return None
 
+    @property
     def dvol(self):
         return np.array(self._dvol)
 

nifty4/domains/domain.py

@@ -25,6 +25,12 @@ import numpy as np
 class Domain(with_metaclass(
         NiftyMeta, type('NewBase', (object,), {}))):
     """The abstract class repesenting a (structured or unstructured) domain.
+
+    Attributes:
+    -----------
+    _needed_for_hash : list of str
+        the names of all members that are relevant for comparison against
+        other Domain objects.
     """
 
     def __init__(self):
@@ -39,8 +45,8 @@ class Domain(with_metaclass(
 
         Notes
         -----
-            Only members that are explicitly added to
-            :py:attr:`._needed_for_hash` will be used for hashing.
+        Only members that are explicitly added to
+        :attr:`._needed_for_hash` will be used for hashing.
         """
         result_hash = 0
         for key in self._needed_for_hash:
@@ -58,6 +64,15 @@ class Domain(with_metaclass(
         Returns
         -------
         bool : True iff `self` and x describe the same domain.
+
+        Notes
+        -----
+        Only members that are explicitly added to
+        :attr:`._needed_for_hash` will be used for comparisom.
+
+        Subclasses of Domain should not re-define :meth:`__eq__`,
+        :meth:`__ne__`, or :meth:`__hash__`; they should instead add their
+        relevant attributes' names to :attr:`._needed_for_hash`.
         """
         if self is x:  # shortcut for simple case
             return True
@@ -69,12 +84,12 @@ class Domain(with_metaclass(
         return True
 
     def __ne__(self, x):
-        """Returns the opposite of :py:meth:`.__eq__()`"""
+        """Returns the opposite of :meth:`.__eq__()`"""
         return not self.__eq__(x)
 
     @abc.abstractproperty
     def shape(self):
-        """tuple of ints: number of pixels along each axis
+        """tuple of int: number of pixels along each axis
 
         The shape of the array-like object required to store information
         living on the domain.

nifty4/domains/gl_space.py

@@ -67,17 +67,20 @@ class GLSpace(StructuredDomain):
     def size(self):
         return np.int((self.nlat * self.nlon))
 
+    @property
     def scalar_dvol(self):
         return None
 
     # MR FIXME: this is potentially wasteful, since the return array is
     #           blown up by a factor of self.nlon
+    @property
     def dvol(self):
         from pyHealpix import GL_weights
         if self._dvol is None:
             self._dvol = GL_weights(self.nlat, self.nlon)
         return np.repeat(self._dvol, self.nlon)
 
+    @property
     def total_volume(self):
         return 4*np.pi
 

nifty4/domains/hp_space.py

@@ -56,6 +56,7 @@ class HPSpace(StructuredDomain):
     def size(self):
         return np.int(12 * self.nside * self.nside)
 
+    @property
     def scalar_dvol(self):
         return np.pi / (3*self._nside*self._nside)
 

nifty4/domains/lm_space.py

@@ -74,6 +74,7 @@ class LMSpace(StructuredDomain):
         # minus two little triangles if mmax < lmax
         return (l+1)**2 - (l-m)*(l-m+1)
 
+    @property
     def scalar_dvol(self):
         return 1.
 

nifty4/domains/power_space.py

@@ -11,7 +11,7 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Copyright(C) 2013-2017 Max-Planck-Society
+# Copyright(C) 2013-2018 Max-Planck-Society
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
@@ -143,11 +143,11 @@ class PowerSpace(StructuredDomain):
         if not (isinstance(harmonic_partner, StructuredDomain) and
                 harmonic_partner.harmonic):
             raise ValueError("harmonic_partner must be a harmonic space.")
-        if harmonic_partner.scalar_dvol() is None:
+        if harmonic_partner.scalar_dvol is None:
             raise ValueError("harmonic partner must have "
                              "scalar volume factors")
         self._harmonic_partner = harmonic_partner
-        pdvol = harmonic_partner.scalar_dvol()
+        pdvol = harmonic_partner.scalar_dvol
 
         if binbounds is not None:
             binbounds = tuple(binbounds)
@@ -178,7 +178,10 @@ class PowerSpace(StructuredDomain):
                 weights=dobj.local_data(k_length_array.val).ravel(),
                 minlength=nbin).astype(np.float64, copy=False)
             temp_k_lengths = dobj.np_allreduce_sum(temp_k_lengths) / temp_rho
+            temp_k_lengths.flags.writeable = False
+            dobj.lock(temp_pindex)
             temp_dvol = temp_rho*pdvol
+            temp_dvol.flags.writeable = False
             self._powerIndexCache[key] = (binbounds, temp_pindex,
                                           temp_k_lengths, temp_dvol)
 
@@ -202,9 +205,11 @@ class PowerSpace(StructuredDomain):
     def size(self):
         return self.shape[0]
 
+    @property
     def scalar_dvol(self):
         return None
 
+    @property
     def dvol(self):
         return self._dvol
 

nifty4/domains/rg_space.py

@@ -11,7 +11,7 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Copyright(C) 2013-2017 Max-Planck-Society
+# Copyright(C) 2013-2018 Max-Planck-Society
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
@@ -35,10 +35,14 @@ class RGSpace(StructuredDomain):
     distances : None or float or tuple of float, optional
         Distance between two grid points along each axis
         (default: None).
-        If distances==None:
-        if harmonic==True, all distances will be set to 1
-        if harmonic==False, the distance along each axis will be
-        set to the inverse of the number of points along that axis.
+
+        If distances is None:
+
+          - if harmonic==True, all distances will be set to 1
+
+          - if harmonic==False, the distance along each axis will be
+            set to the inverse of the number of points along that axis.
+
     harmonic : bool, optional
         Whether the space represents a grid in position or harmonic space.
         (default: False).
@@ -84,6 +88,7 @@ class RGSpace(StructuredDomain):
     def size(self):
         return self._size
 
+    @property
     def scalar_dvol(self):
         return self._dvol
 
@@ -186,8 +191,8 @@ class RGSpace(StructuredDomain):
 
     @property
     def distances(self):
-        """Distance between grid points along each axis. It is a tuple
-        of positive floating point numbers with the n-th entry giving the
-        distance between neighboring grid points along the n-th dimension.
+        """tuple of float : Distance between grid points along each axis.
+        The n-th entry of the tuple is the distance between neighboring
+        grid points along the n-th dimension.
         """
         return self._distances

nifty4/domains/structured_domain.py

@@ -11,7 +11,7 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Copyright(C) 2013-2017 Max-Planck-Society
+# Copyright(C) 2013-2018 Max-Planck-Society
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
@@ -32,37 +32,33 @@ class StructuredDomain(Domain):
     def __init__(self):
         super(StructuredDomain, self).__init__()
 
-    @abc.abstractmethod
+    @abc.abstractproperty
     def scalar_dvol(self):
-        """Returns the volume factors of this domain as a floating
+        """float or None : uniform cell volume, if applicable
+
+        Returns the volume factors of this domain as a floating
         point scalar, if the volume factors are all identical, otherwise
         returns None.
-
-        Returns
-        -------
-        float or None: Volume factor
         """
         raise NotImplementedError
 
+    @property
     def dvol(self):
-        """Returns the volume factors of this domain, either as a floating
+        """float or numpy.ndarray(dtype=float): Volume factors
+
+        Returns the volume factors of this domain, either as a floating
         point scalar (if the volume factors are all identical) or as a
         floating point array with a shape of `self.shape`.
-
-        Returns
-        -------
-        float or numpy.ndarray(dtype=float): Volume factors
         """
-        return self.scalar_dvol()
+        return self.scalar_dvol
 
+    @property
     def total_volume(self):
-        """Returns the sum over all the domain's pixel volumes.
+        """float : Total domain volume
 
-        Returns
-        -------
-        float : sum of all pixel volume elements
+        Returns the sum over all the domain's pixel volumes.
         """
-        tmp = self.dvol()
+        tmp = self.dvol
         return self.size * tmp if np.isscalar(tmp) else np.sum(tmp)
 
     @abc.abstractproperty
@@ -71,7 +67,9 @@ class StructuredDomain(Domain):
         raise NotImplementedError
 
     def get_k_length_array(self):
-        """The length of the k vector for every pixel.
+        """k vector lengths, if applicable,
+
+        Returns the length of the k vector for every pixel.
         This method is only implemented for harmonic domains.
 
         Returns
@@ -82,14 +80,16 @@ class StructuredDomain(Domain):
         raise NotImplementedError
 
     def get_unique_k_lengths(self):
-        """ Returns an array of floats containing the unique k vector lengths
+        """Sorted unique k-vector lengths, if applicable.
+
+        Returns an array of floats containing the unique k vector lengths
         for this domain.
         This method is only implemented for harmonic domains.
         """
         raise NotImplementedError
 
     def get_fft_smoothing_kernel_function(self, sigma):
-        """This method returns a smoothing kernel function.
+        """Helper for Gaussian smoothing.
 
         This method, which is only implemented for harmonic domains, helps
         smoothing fields that live on a domain that has this domain as

nifty4/field.py

@@ -166,6 +166,13 @@ class Field(object):
     def fill(self, fill_value):
         self._val.fill(fill_value)
 
+    def lock(self):
+        dobj.lock(self._val)
+
+    @property
+    def locked(self):
+        return dobj.locked(self._val)
+
     @property
     def val(self):
         """ Returns the data object associated with this Field.
@@ -227,19 +234,36 @@ class Field(object):
         Returns
         -------
         Field
-            The output object. An identical copy of 'self'.
+            An identical copy of 'self'.
         """
         return Field(val=self, copy=True)
 
+    def locked_copy(self):
+        """ Returns a read-only version of the Field.
+
+        If `self` is locked, returns `self`. Otherwise returns a locked copy
+        of `self`.
+
+        Returns
+        -------
+        Field
+            A read-only version of 'self'.
+        """
+        if self.locked:
+            return self
+        res = Field(val=self, copy=True)
+        res.lock()
+        return res
+
     def scalar_weight(self, spaces=None):
         if np.isscalar(spaces):
-            return self._domain[spaces].scalar_dvol()
+            return self._domain[spaces].scalar_dvol
 
         if spaces is None:
             spaces = range(len(self._domain))
         res = 1.
         for i in spaces:
-            tmp = self._domain[i].scalar_dvol()
+            tmp = self._domain[i].scalar_dvol
             if tmp is None:
                 return None
             res *= tmp
@@ -247,13 +271,13 @@ class Field(object):
 
     def total_volume(self, spaces=None):
         if np.isscalar(spaces):
-            return self._domain[spaces].total_volume()
+            return self._domain[spaces].total_volume
 
         if spaces is None:
             spaces = range(len(self._domain))
         res = 1.
         for i in spaces:
-            res *= self._domain[i].total_volume()
+            res *= self._domain[i].total_volume
         return res
 
     def weight(self, power=1, spaces=None, out=None):
@@ -287,7 +311,7 @@ class Field(object):
 
         fct = 1.
         for ind in spaces:
-            wgt = self._domain[ind].dvol()
+            wgt = self._domain[ind].dvol
             if np.isscalar(wgt):
                 fct *= wgt
             else:

nifty4/operators/chain_operator.py

@@ -60,7 +60,7 @@ class ChainOperator(LinearOperator):
             # try to absorb the factor into a DiagonalOperator
             for i in range(len(opsnew)):
                 if isinstance(opsnew[i], DiagonalOperator):
-                    opsnew[i] = DiagonalOperator(opsnew[i].diagonal()*fct,
+                    opsnew[i] = DiagonalOperator(opsnew[i].diagonal*fct,
                                                  domain=opsnew[i].domain,
                                                  spaces=opsnew[i]._spaces)
                     fct = 1.
@@ -76,8 +76,8 @@ class ChainOperator(LinearOperator):
                     isinstance(opsnew[-1], DiagonalOperator) and
                     isinstance(op, DiagonalOperator) and
                     op._spaces == opsnew[-1]._spaces):
-                opsnew[-1] = DiagonalOperator(opsnew[-1].diagonal() *
-                                              op.diagonal(),
+                opsnew[-1] = DiagonalOperator(opsnew[-1].diagonal *
+                                              op.diagonal,
                                               domain=opsnew[-1].domain,
                                               spaces=opsnew[-1]._spaces)
             else:

nifty4/operators/diagonal_operator.py

@@ -44,7 +44,7 @@ class DiagonalOperator(EndomorphicOperator):
         The elements of "domain" on which the operator acts.
         If None, it acts on all elements.
 
-    NOTE: the fields given to __init__ and returned from .diagonal() are
+    NOTE: the fields given to __init__ and returned from .diagonal are
     considered to be non-bare, i.e. during operator application, no additional
     volume factors are applied!
     """
@@ -74,7 +74,7 @@ class DiagonalOperator(EndomorphicOperator):
             if self._spaces == tuple(range(len(self._domain))):
                 self._spaces = None  # shortcut
 
-        self._diagonal = diagonal.copy()
+        self._diagonal = diagonal.locked_copy()
 
         if self._spaces is not None:
             active_axes = []
@@ -111,9 +111,10 @@ class DiagonalOperator(EndomorphicOperator):
             else:
                 return Field(x.domain, val=x.val/self._ldiag.conj())
 
+    @property
     def diagonal(self):
         """ Returns the diagonal of the Operator."""
-        return self._diagonal.copy()
+        return self._diagonal
 
     @property
     def domain(self):

nifty4/operators/dof_distributor.py

@@ -47,12 +47,12 @@ class DOFDistributor(LinearOperator):
             raise ValueError("incorrect dofdex domain")
 
         nbin = dofdex.max()
-        if partner.scalar_dvol() is not None:
+        if partner.scalar_dvol is not None:
             wgt = np.bincount(dobj.local_data(dofdex.val).ravel(),
                               minlength=nbin)
-            wgt = wgt*partner.scalar_dvol()
+            wgt = wgt*partner.scalar_dvol
         else:
-            dvol = dobj.local_data(partner.dvol())
+            dvol = dobj.local_data(partner.dvol)
             wgt = np.bincount(dobj.local_data(dofdex.val).ravel(),
                               minlength=nbin, weights=dvol)
         # The explicit conversion to float64 is necessary because bincount

nifty4/operators/fft_operator.py

@@ -116,9 +116,9 @@ class FFTOperator(LinearOperator):
             tmp = dobj.from_local_data(x.val.shape, ldat2, distaxis=0)
         Tval = Field(tdom, tmp)
         if mode & (LinearOperator.TIMES | LinearOperator.ADJOINT_TIMES):
-            fct = self._domain[self._space].scalar_dvol()
+            fct = self._domain[self._space].scalar_dvol
         else:
-            fct = self._target[self._space].scalar_dvol()
+            fct = self._target[self._space].scalar_dvol
         if fct != 1:
             Tval *= fct
 

nifty4/operators/harmonic_transform_operator.py

@@ -142,7 +142,7 @@ class HarmonicTransformOperator(LinearOperator):
             ldat2 = dobj.local_data(tmp).reshape(ldat.shape)
             tmp = dobj.from_local_data(x.val.shape, ldat2, distaxis=0)
         Tval = Field(tdom, tmp)
-        fct = self._domain[self._space].scalar_dvol()
+        fct = self._domain[self._space].scalar_dvol
         if fct != 1:
             Tval *= fct
 

nifty4/operators/linear_operator.py

@@ -148,12 +148,11 @@ class LinearOperator(with_metaclass(
             depending on mode.
 
         mode : int
-            LinearOperator.TIMES: normal application
-            LinearOperator.ADJOINT_TIMES: adjoint application
-            LinearOperator.INVERSE_TIMES: inverse application
-            LinearOperator.ADJOINT_INVERSE_TIMES or
-            LinearOperator.INVERSE_ADJOINT_TIMES:
-            adjoint inverse application
+            - :attr:`TIMES`: normal application
+            - :attr:`ADJOINT_TIMES`: adjoint application
+            - :attr:`INVERSE_TIMES`: inverse application
+            - :attr:`ADJOINT_INVERSE_TIMES` or
+              :attr:`INVERSE_ADJOINT_TIMES`: adjoint inverse application
 
         Returns
         -------

nifty4/operators/sum_operator.py

@@ -71,7 +71,7 @@ class SumOperator(LinearOperator):
             for i in range(len(opsnew)):
                 if isinstance(opsnew[i], DiagonalOperator):
                     sum *= (-1 if negnew[i] else 1)
-                    opsnew[i] = DiagonalOperator(opsnew[i].diagonal()+sum,
+                    opsnew[i] = DiagonalOperator(opsnew[i].diagonal+sum,
                                                  domain=opsnew[i].domain,
                                                  spaces=opsnew[i]._spaces)
                     sum = 0.
@@ -89,11 +89,11 @@ class SumOperator(LinearOperator):
         for i in range(len(ops)):
             if not processed[i]:
                 if isinstance(ops[i], DiagonalOperator):
-                    diag = ops[i].diagonal()*(-1 if neg[i] else 1)
+                    diag = ops[i].diagonal*(-1 if neg[i] else 1)
                     for j in range(i+1, len(ops)):
                         if (isinstance(ops[j], DiagonalOperator) and
                                 ops[i]._spaces == ops[j]._spaces):
-                            diag += ops[j].diagonal()*(-1 if neg[j] else 1)
+                            diag += ops[j].diagonal*(-1 if neg[j] else 1)
                             processed[j] = True
                     opsnew.append(DiagonalOperator(diag, ops[i].domain,
                                                    ops[i]._spaces))

test/test_operators/test_diagonal_operator.py

@@ -11,7 +11,7 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Copyright(C) 2013-2017 Max-Planck-Society
+# Copyright(C) 2013-2018 Max-Planck-Society
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
@@ -90,6 +90,6 @@ class DiagonalOperator_Tests(unittest.TestCase):
     def test_diagonal(self, space):
         diag = ift.Field.from_random('normal', domain=space)
         D = ift.DiagonalOperator(diag)
-        diag_op = D.diagonal()
+        diag_op = D.diagonal
         assert_allclose(ift.dobj.to_global_data(diag.val),
                         ift.dobj.to_global_data(diag_op.val))

test/test_spaces/test_gl_space.py

@@ -11,7 +11,7 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Copyright(C) 2013-2017 Max-Planck-Society
+# Copyright(C) 2013-2018 Max-Planck-Society
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
@@ -73,4 +73,4 @@ class GLSpaceFunctionalityTests(unittest.TestCase):
 
     @expand(get_dvol_configs())
     def test_dvol(self, power, expected):
-        assert_almost_equal(GLSpace(2).dvol(), expected)
+        assert_almost_equal(GLSpace(2).dvol, expected)

test/test_spaces/test_hp_space.py

@@ -11,7 +11,7 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Copyright(C) 2013-2017 Max-Planck-Society
+# Copyright(C) 2013-2018 Max-Planck-Society
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
@@ -69,4 +69,4 @@ class HPSpaceFunctionalityTests(unittest.TestCase):
                 assert_equal(getattr(h, key), value)
 
     def test_dvol(self):
-        assert_almost_equal(HPSpace(2).dvol(), np.pi/12)
+        assert_almost_equal(HPSpace(2).dvol, np.pi/12)

test/test_spaces/test_lm_space.py

@@ -11,7 +11,7 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Copyright(C) 2013-2017 Max-Planck-Society
+# Copyright(C) 2013-2018 Max-Planck-Society
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
@@ -87,7 +87,7 @@ class LMSpaceFunctionalityTests(unittest.TestCase):
                 assert_equal(getattr(l, key), value)
 
     def test_dvol(self):
-        assert_allclose(ift.LMSpace(5).dvol(), 1.)
+        assert_allclose(ift.LMSpace(5).dvol, 1.)
 
     @expand(get_k_length_array_configs())
     def test_k_length_array(self, lmax, expected):

test/test_spaces/test_power_space.py

@@ -11,7 +11,7 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Copyright(C) 2013-2017 Max-Planck-Society
+# Copyright(C) 2013-2018 Max-Planck-Society
 #
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik