diff --git a/demos/polynomial_fit.py b/demos/polynomial_fit.py
index 969e6822ad9d30de7d59037d6ae6166e8bcef83e..020c030c4bf550f32ba4f7a7b4d1b98d75bfa568 100644
--- a/demos/polynomial_fit.py
+++ b/demos/polynomial_fit.py
@@ -38,13 +38,13 @@ class PolynomialResponse(ift.LinearOperator):
"""
def __init__(self, domain, sampling_points):
- super(PolynomialResponse, self).__init__()
if not (isinstance(domain, ift.UnstructuredDomain)
and isinstance(x, np.ndarray)):
raise TypeError
self._domain = ift.DomainTuple.make(domain)
tgt = ift.UnstructuredDomain(sampling_points.shape)
self._target = ift.DomainTuple.make(tgt)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
sh = (self.target.size, domain.size)
self._mat = np.empty(sh)
@@ -62,10 +62,6 @@ class PolynomialResponse(ift.LinearOperator):
out = self._mat.conj().T.dot(val)
return ift.from_global_data(self._tgt(mode), out)
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
-
# Generate some mock data
N_params = 10
diff --git a/nifty5/domains/dof_space.py b/nifty5/domains/dof_space.py
index 151c45f6249a5070a8c3b5d9f65af8aa9f967d4a..5d42ad197aef49612f9689103cb179f5d882cc17 100644
--- a/nifty5/domains/dof_space.py
+++ b/nifty5/domains/dof_space.py
@@ -40,7 +40,6 @@ class DOFSpace(StructuredDomain):
_needed_for_hash = ["_dvol"]
def __init__(self, dof_weights):
- super(DOFSpace, self).__init__()
self._dvol = tuple(dof_weights)
@property
diff --git a/nifty5/domains/domain.py b/nifty5/domains/domain.py
index fd7c5ed5f9c909fab6f883186e7765431f1a90e2..a807595dbc3732c133b8b5660ed5ac57c533ee42 100644
--- a/nifty5/domains/domain.py
+++ b/nifty5/domains/domain.py
@@ -25,10 +25,6 @@ from ..utilities import NiftyMetaBase
class Domain(NiftyMetaBase()):
"""The abstract class repesenting a (structured or unstructured) domain.
"""
-
- def __init__(self):
- self._hash = None
-
def __repr__(self):
raise NotImplementedError
@@ -40,7 +36,9 @@ class Domain(NiftyMetaBase()):
Only members that are explicitly added to
:attr:`._needed_for_hash` will be used for hashing.
"""
- if self._hash is None:
+ try:
+ return self._hash
+ except AttributeError:
h = 0
for key in self._needed_for_hash:
h ^= hash(vars(self)[key])
diff --git a/nifty5/domains/gl_space.py b/nifty5/domains/gl_space.py
index fff20b0dcc33f87ee413efef05598ca169e1a550..529dea48d1823f2d405dca041b0356dd5962637b 100644
--- a/nifty5/domains/gl_space.py
+++ b/nifty5/domains/gl_space.py
@@ -43,8 +43,6 @@ class GLSpace(StructuredDomain):
_needed_for_hash = ["_nlat", "_nlon"]
def __init__(self, nlat, nlon=None):
- super(GLSpace, self).__init__()
-
self._nlat = int(nlat)
if self._nlat < 1:
raise ValueError("nlat must be a positive number.")
diff --git a/nifty5/domains/hp_space.py b/nifty5/domains/hp_space.py
index 9df48287c4809458639f49feb3013420d5e15da9..a8a99b9546996ba32cd26bc8ffb4293e65c21430 100644
--- a/nifty5/domains/hp_space.py
+++ b/nifty5/domains/hp_space.py
@@ -40,7 +40,6 @@ class HPSpace(StructuredDomain):
_needed_for_hash = ["_nside"]
def __init__(self, nside):
- super(HPSpace, self).__init__()
self._nside = int(nside)
if self._nside < 1:
raise ValueError("nside must be >=1.")
diff --git a/nifty5/domains/lm_space.py b/nifty5/domains/lm_space.py
index 54a046849c60415916f049a6fd032d541941a13f..d40d0c1029ce5166b05526d7f628b8b616b278f0 100644
--- a/nifty5/domains/lm_space.py
+++ b/nifty5/domains/lm_space.py
@@ -48,7 +48,6 @@ class LMSpace(StructuredDomain):
_needed_for_hash = ["_lmax", "_mmax"]
def __init__(self, lmax, mmax=None):
- super(LMSpace, self).__init__()
self._lmax = np.int(lmax)
if self._lmax < 0:
raise ValueError("lmax must be >=0.")
diff --git a/nifty5/domains/log_rg_space.py b/nifty5/domains/log_rg_space.py
index 3f0d7abee08dd1b3dd2026caa99074a930d22b6c..6f3833ec952a39151b1faa33e55adda468113a44 100644
--- a/nifty5/domains/log_rg_space.py
+++ b/nifty5/domains/log_rg_space.py
@@ -32,8 +32,6 @@ class LogRGSpace(StructuredDomain):
_needed_for_hash = ['_shape', '_bindistances', '_t_0', '_harmonic']
def __init__(self, shape, bindistances, t_0, harmonic=False):
- super(LogRGSpace, self).__init__()
-
self._harmonic = bool(harmonic)
if np.isscalar(shape):
diff --git a/nifty5/domains/power_space.py b/nifty5/domains/power_space.py
index 75a905c4fd9c7c2f6c4a2125c52bbb5cdb1d04f4..1cd6d9523a835417377e1c6902091ca483e04f14 100644
--- a/nifty5/domains/power_space.py
+++ b/nifty5/domains/power_space.py
@@ -158,8 +158,6 @@ class PowerSpace(StructuredDomain):
return PowerSpace.linear_binbounds(nbin, lbound, rbound)
def __init__(self, harmonic_partner, binbounds=None):
- super(PowerSpace, self).__init__()
-
if not (isinstance(harmonic_partner, StructuredDomain) and
harmonic_partner.harmonic):
raise ValueError("harmonic_partner must be a harmonic space.")
diff --git a/nifty5/domains/rg_space.py b/nifty5/domains/rg_space.py
index 3375117518c3b1561cc6cc9820241020a7570dc1..14b9c9a10f1ce692ce86ae09a8245e68b2894ddb 100644
--- a/nifty5/domains/rg_space.py
+++ b/nifty5/domains/rg_space.py
@@ -51,8 +51,6 @@ class RGSpace(StructuredDomain):
_needed_for_hash = ["_distances", "_shape", "_harmonic"]
def __init__(self, shape, distances=None, harmonic=False):
- super(RGSpace, self).__init__()
-
self._harmonic = bool(harmonic)
if np.isscalar(shape):
shape = (shape,)
diff --git a/nifty5/domains/unstructured_domain.py b/nifty5/domains/unstructured_domain.py
index 5c5c6d34d3e32e09d4f72342bfb6373a871ed24d..489b7777337a87c7d368ebacaf5074968818a009 100644
--- a/nifty5/domains/unstructured_domain.py
+++ b/nifty5/domains/unstructured_domain.py
@@ -38,7 +38,6 @@ class UnstructuredDomain(Domain):
_needed_for_hash = ["_shape"]
def __init__(self, shape):
- super(UnstructuredDomain, self).__init__()
try:
self._shape = tuple([int(i) for i in shape])
except TypeError:
diff --git a/nifty5/library/los_response.py b/nifty5/library/los_response.py
index f8c112f230520032c2990c83ff0df16e7d115f5d..2dd414f1db943e0d583b49e78117adef29206ff6 100644
--- a/nifty5/library/los_response.py
+++ b/nifty5/library/los_response.py
@@ -134,9 +134,8 @@ class LOSResponse(LinearOperator):
"""
def __init__(self, domain, starts, ends, sigmas_low=None, sigmas_up=None):
-
- super(LOSResponse, self).__init__()
self._domain = DomainTuple.make(domain)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
if ((not isinstance(self.domain[0], RGSpace)) or
(len(self._domain) != 1)):
@@ -221,10 +220,6 @@ class LOSResponse(LinearOperator):
self._target = DomainTuple.make(UnstructuredDomain(nlos))
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
-
def apply(self, x, mode):
self._check_input(x, mode)
if mode == self.TIMES:
diff --git a/nifty5/minimization/descent_minimizer.py b/nifty5/minimization/descent_minimizer.py
index 3ddfefd0dbfad08e6b268106de9ee4b64c506851..963159f189d2c72465c338134dd7e82e611b0fe7 100644
--- a/nifty5/minimization/descent_minimizer.py
+++ b/nifty5/minimization/descent_minimizer.py
@@ -42,7 +42,6 @@ class DescentMinimizer(Minimizer):
"""
def __init__(self, controller, line_searcher=LineSearchStrongWolfe()):
- super(DescentMinimizer, self).__init__()
self._controller = controller
self.line_searcher = line_searcher
diff --git a/nifty5/minimization/energy.py b/nifty5/minimization/energy.py
index d14df4af4c065bb8ecd939cb3d067ae780f6c84d..c213a6e8f71396e3a508e8abb778b5649a275db4 100644
--- a/nifty5/minimization/energy.py
+++ b/nifty5/minimization/energy.py
@@ -47,7 +47,6 @@ class Energy(NiftyMetaBase()):
"""
def __init__(self, position):
- super(Energy, self).__init__()
self._position = position
self._gradnorm = None
diff --git a/nifty5/minimization/gradient_norm_controller.py b/nifty5/minimization/gradient_norm_controller.py
index c7d98be961ea5adc4e10343b71e375017b13ae7a..6c6a49c245273bb0dca73c7bd87dd8d3a17597dc 100644
--- a/nifty5/minimization/gradient_norm_controller.py
+++ b/nifty5/minimization/gradient_norm_controller.py
@@ -47,7 +47,6 @@ class GradientNormController(IterationController):
def __init__(self, tol_abs_gradnorm=None, tol_rel_gradnorm=None,
convergence_level=1, iteration_limit=None, name=None):
- super(GradientNormController, self).__init__()
self._tol_abs_gradnorm = tol_abs_gradnorm
self._tol_rel_gradnorm = tol_rel_gradnorm
self._convergence_level = convergence_level
diff --git a/nifty5/minimization/line_energy.py b/nifty5/minimization/line_energy.py
index f6fd528d2da8e514d29915719e762a5ed083ad08..b7fbe1393d51179b3ac9228c703fbc19d1e8b31e 100644
--- a/nifty5/minimization/line_energy.py
+++ b/nifty5/minimization/line_energy.py
@@ -50,7 +50,6 @@ class LineEnergy(object):
"""
def __init__(self, line_position, energy, line_direction, offset=0.):
- super(LineEnergy, self).__init__()
self._line_position = float(line_position)
self._line_direction = line_direction
diff --git a/nifty5/minimization/scipy_minimizer.py b/nifty5/minimization/scipy_minimizer.py
index 9fad98daa7e056ff7a286398c0c939c72a8b2c6f..057355b0dfeabadf0625671eddb82d33be3590be 100644
--- a/nifty5/minimization/scipy_minimizer.py
+++ b/nifty5/minimization/scipy_minimizer.py
@@ -74,7 +74,6 @@ class ScipyMinimizer(Minimizer):
"""
def __init__(self, method, options, need_hessp, bounds):
- super(ScipyMinimizer, self).__init__()
if not dobj.is_numpy():
raise NotImplementedError
self._method = method
@@ -130,7 +129,6 @@ def L_BFGS_B(ftol, gtol, maxiter, maxcor=10, disp=False, bounds=None):
class ScipyCG(Minimizer):
def __init__(self, tol, maxiter):
- super(ScipyCG, self).__init__()
if not dobj.is_numpy():
raise NotImplementedError
self._tol = tol
diff --git a/nifty5/operators/block_diagonal_operator.py b/nifty5/operators/block_diagonal_operator.py
index 9a601e15c6bdd965b50a4fcdf2127996ffbdba39..eaa0b4a7ad0ddd281c0a64f3e00fe366045bad8f 100644
--- a/nifty5/operators/block_diagonal_operator.py
+++ b/nifty5/operators/block_diagonal_operator.py
@@ -33,21 +33,16 @@ class BlockDiagonalOperator(EndomorphicOperator):
dictionary with operators domain names as keys and
LinearOperators as items
"""
- super(BlockDiagonalOperator, self).__init__()
if not isinstance(domain, MultiDomain):
raise TypeError("MultiDomain expected")
if not isinstance(operators, tuple):
raise TypeError("tuple expected")
self._domain = domain
self._ops = operators
- self._cap = self._all_ops
+ self._capability = self._all_ops
for op in self._ops:
if op is not None:
- self._cap &= op.capability
-
- @property
- def capability(self):
- return self._cap
+ self._capability &= op.capability
def apply(self, x, mode):
self._check_input(x, mode)
diff --git a/nifty5/operators/central_zero_padder.py b/nifty5/operators/central_zero_padder.py
index 1f48c8d83a6d2f7a4646146f0e2b4449e0fdb723..499b09f794e8bd3788e001155527a265d049182b 100644
--- a/nifty5/operators/central_zero_padder.py
+++ b/nifty5/operators/central_zero_padder.py
@@ -16,8 +16,6 @@ from .. import dobj
# highest frequency.
class CentralZeroPadder(LinearOperator):
def __init__(self, domain, new_shape, space=0):
- super(CentralZeroPadder, self).__init__()
-
self._domain = DomainTuple.make(domain)
self._space = utilities.infer_space(self._domain, space)
dom = self._domain[self._space]
@@ -35,6 +33,7 @@ class CentralZeroPadder(LinearOperator):
self._target = list(self._domain)
self._target[self._space] = tgt
self._target = DomainTuple.make(self._target)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
slicer = []
axes = self._target.axes[self._space]
@@ -53,10 +52,6 @@ class CentralZeroPadder(LinearOperator):
self.slicer[i] = tuple(tmp)
self.slicer = tuple(self.slicer)
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
-
def apply(self, x, mode):
self._check_input(x, mode)
x = x.val
diff --git a/nifty5/operators/chain_operator.py b/nifty5/operators/chain_operator.py
index 548ba0474e26f88e1e1dda3cbf3086e18c722454..16365a7b3919482b65c1c037fea5bb97079d6d4a 100644
--- a/nifty5/operators/chain_operator.py
+++ b/nifty5/operators/chain_operator.py
@@ -31,7 +31,6 @@ class ChainOperator(LinearOperator):
def __init__(self, ops, _callingfrommake=False):
if not _callingfrommake:
raise NotImplementedError
- super(ChainOperator, self).__init__()
self._ops = ops
self._capability = self._all_ops
for op in ops:
@@ -132,10 +131,6 @@ class ChainOperator(LinearOperator):
return self.make([op._flip_modes(trafo) for op in self._ops])
raise ValueError("invalid operator transformation")
- @property
- def capability(self):
- return self._capability
-
def apply(self, x, mode):
self._check_mode(mode)
t_ops = self._ops if mode & self._backwards else reversed(self._ops)
diff --git a/nifty5/operators/diagonal_operator.py b/nifty5/operators/diagonal_operator.py
index 506964163c3d44da2f0005c4f4eae9e9af3338cc..5864ae038acead4c04fbd381dcf802ba685bd1cf 100644
--- a/nifty5/operators/diagonal_operator.py
+++ b/nifty5/operators/diagonal_operator.py
@@ -57,8 +57,6 @@ class DiagonalOperator(EndomorphicOperator):
"""
def __init__(self, diagonal, domain=None, spaces=None):
- super(DiagonalOperator, self).__init__()
-
if not isinstance(diagonal, Field):
raise TypeError("Field object required")
if domain is None:
@@ -99,6 +97,7 @@ class DiagonalOperator(EndomorphicOperator):
def _fill_rest(self):
self._ldiag.flags.writeable = False
self._complex = utilities.iscomplextype(self._ldiag.dtype)
+ self._capability = self._all_ops
if not self._complex:
lmin = self._ldiag.min() if self._ldiag.size > 0 else 1.
self._diagmin = dobj.np_allreduce_min(np.array(lmin))[()]
@@ -150,10 +149,6 @@ class DiagonalOperator(EndomorphicOperator):
return Field.from_local_data(x.domain, x.local_data*xdiag)
return Field.from_local_data(x.domain, x.local_data/xdiag)
- @property
- def capability(self):
- return self._all_ops
-
def _flip_modes(self, trafo):
xdiag = self._ldiag
if self._complex and (trafo & self.ADJOINT_BIT):
diff --git a/nifty5/operators/dof_distributor.py b/nifty5/operators/dof_distributor.py
index ef864760cbb34b9bbcde2d3ec2851f1902e30546..4f7538317122fd6599579da967161c5339ecb432 100644
--- a/nifty5/operators/dof_distributor.py
+++ b/nifty5/operators/dof_distributor.py
@@ -54,8 +54,6 @@ class DOFDistributor(LinearOperator):
"""
def __init__(self, dofdex, target=None, space=None):
- super(DOFDistributor, self).__init__()
-
if target is None:
target = dofdex.domain
self._target = DomainTuple.make(target)
@@ -98,6 +96,7 @@ class DOFDistributor(LinearOperator):
dom = list(self._target)
dom[self._space] = other_space
self._domain = DomainTuple.make(dom)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
if dobj.default_distaxis() in self._domain.axes[self._space]:
dofdex = dobj.local_data(dofdex)
@@ -142,7 +141,3 @@ class DOFDistributor(LinearOperator):
def apply(self, x, mode):
self._check_input(x, mode)
return self._times(x) if mode == self.TIMES else self._adjoint_times(x)
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/domain_distributor.py b/nifty5/operators/domain_distributor.py
index 08939a45fc079396dc47ddea569bb63a4662557f..6044e6e32161f1eb24fefddd8aa4cf5d20d1d15e 100644
--- a/nifty5/operators/domain_distributor.py
+++ b/nifty5/operators/domain_distributor.py
@@ -35,6 +35,7 @@ class DomainDistributor(LinearOperator):
self._domain = [tgt for i, tgt in enumerate(self._target)
if i in self._spaces]
self._domain = DomainTuple.make(self._domain)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
@@ -49,7 +50,3 @@ class DomainDistributor(LinearOperator):
else:
return x.sum([s for s in range(len(x.domain))
if s not in self._spaces])
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/energy_operators.py b/nifty5/operators/energy_operators.py
index dc5c75e94bd04bd7b334f0edb91a073d5f99f673..a355989919275b0f63ad18bbd768a47e0cfd87bc 100644
--- a/nifty5/operators/energy_operators.py
+++ b/nifty5/operators/energy_operators.py
@@ -36,7 +36,6 @@ class EnergyOperator(Operator):
class SquaredNormOperator(EnergyOperator):
def __init__(self, domain):
- super(SquaredNormOperator, self).__init__()
self._domain = domain
def apply(self, x):
@@ -50,7 +49,6 @@ class SquaredNormOperator(EnergyOperator):
class QuadraticFormOperator(EnergyOperator):
def __init__(self, op):
from .endomorphic_operator import EndomorphicOperator
- super(QuadraticFormOperator, self).__init__()
if not isinstance(op, EndomorphicOperator):
raise TypeError("op must be an EndomorphicOperator")
self._op = op
@@ -67,7 +65,6 @@ class QuadraticFormOperator(EnergyOperator):
class GaussianEnergy(EnergyOperator):
def __init__(self, mean=None, covariance=None, domain=None):
- super(GaussianEnergy, self).__init__()
self._domain = None
if mean is not None:
self._checkEquivalence(mean.domain)
@@ -132,7 +129,6 @@ class BernoulliEnergy(EnergyOperator):
class Hamiltonian(EnergyOperator):
def __init__(self, lh, ic_samp=None):
- super(Hamiltonian, self).__init__()
self._lh = lh
self._prior = GaussianEnergy(domain=lh.domain)
self._ic_samp = ic_samp
@@ -156,7 +152,6 @@ class SampledKullbachLeiblerDivergence(EnergyOperator):
h: Hamiltonian
N: Number of samples to be used
"""
- super(SampledKullbachLeiblerDivergence, self).__init__()
self._h = h
self._domain = h.domain
self._res_samples = tuple(res_samples)
diff --git a/nifty5/operators/exp_transform.py b/nifty5/operators/exp_transform.py
index db12ba311ba508687bbf3fb5cb125b9753ae9289..9d7d04b5cb28205b7499f09ef8827ded77be4733 100644
--- a/nifty5/operators/exp_transform.py
+++ b/nifty5/operators/exp_transform.py
@@ -33,6 +33,7 @@ from ..utilities import infer_space, special_add_at
class ExpTransform(LinearOperator):
def __init__(self, target, dof, space=0):
self._target = DomainTuple.make(target)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
self._space = infer_space(self._target, space)
tgt = self._target[self._space]
if not ((isinstance(tgt, RGSpace) and tgt.harmonic) or
@@ -115,7 +116,3 @@ class ExpTransform(LinearOperator):
if d == ax:
x = dobj.redistribute(x, dist=ax)
return Field(self._tgt(mode), val=x)
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/field_zero_padder.py b/nifty5/operators/field_zero_padder.py
index b0f67daa4f1888ccf4be7a9ec9a3a210986dba53..afe53e03f6dc3fa6c040b0b7abbd9de11de9f7d1 100644
--- a/nifty5/operators/field_zero_padder.py
+++ b/nifty5/operators/field_zero_padder.py
@@ -13,7 +13,6 @@ from .. import utilities
class FieldZeroPadder(LinearOperator):
def __init__(self, domain, new_shape, space=0):
- super(FieldZeroPadder, self).__init__()
self._domain = DomainTuple.make(domain)
self._space = utilities.infer_space(self._domain, space)
dom = self._domain[self._space]
@@ -30,10 +29,7 @@ class FieldZeroPadder(LinearOperator):
self._target = list(self._domain)
self._target[self._space] = tgt
self._target = DomainTuple.make(self._target)
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
+ self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
diff --git a/nifty5/operators/harmonic_operators.py b/nifty5/operators/harmonic_operators.py
index e07d3da107e7d8bf4a06bf241dd326fb4be9dc6c..480540848da3eb06ee8fd0dfd8ed976705c71ba9 100644
--- a/nifty5/operators/harmonic_operators.py
+++ b/nifty5/operators/harmonic_operators.py
@@ -57,10 +57,9 @@ class FFTOperator(LinearOperator):
"""
def __init__(self, domain, target=None, space=None):
- super(FFTOperator, self).__init__()
-
# Initialize domain and target
self._domain = DomainTuple.make(domain)
+ self._capability = self._all_ops
self._space = utilities.infer_space(self._domain, space)
adom = self._domain[self._space]
@@ -127,10 +126,6 @@ class FFTOperator(LinearOperator):
fct *= self._target[self._space].scalar_dvol
return Tval if fct == 1 else Tval*fct
- @property
- def capability(self):
- return self._all_ops
-
class HartleyOperator(LinearOperator):
"""Transforms between a pair of position and harmonic RGSpaces.
@@ -162,10 +157,9 @@ class HartleyOperator(LinearOperator):
"""
def __init__(self, domain, target=None, space=None):
- super(HartleyOperator, self).__init__()
-
# Initialize domain and target
self._domain = DomainTuple.make(domain)
+ self._capability = self._all_ops
self._space = utilities.infer_space(self._domain, space)
adom = self._domain[self._space]
@@ -239,10 +233,6 @@ class HartleyOperator(LinearOperator):
fct = self._target[self._space].scalar_dvol
return Tval if fct == 1 else Tval*fct
- @property
- def capability(self):
- return self._all_ops
-
class SHTOperator(LinearOperator):
"""Transforms between a harmonic domain on the sphere and a position
@@ -272,10 +262,9 @@ class SHTOperator(LinearOperator):
"""
def __init__(self, domain, target=None, space=None):
- super(SHTOperator, self).__init__()
-
# Initialize domain and target
self._domain = DomainTuple.make(domain)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
self._space = utilities.infer_space(self._domain, space)
hspc = self._domain[self._space]
@@ -351,10 +340,6 @@ class SHTOperator(LinearOperator):
odat = dobj.redistribute(odat, dist=dobj.distaxis(x.val))
return Field(tdom, odat)
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
-
class HarmonicTransformOperator(LinearOperator):
"""Transforms between a harmonic domain and a position domain counterpart.
@@ -384,8 +369,6 @@ class HarmonicTransformOperator(LinearOperator):
"""
def __init__(self, domain, target=None, space=None):
- super(HarmonicTransformOperator, self).__init__()
-
domain = DomainTuple.make(domain)
space = utilities.infer_space(domain, space)
@@ -399,15 +382,12 @@ class HarmonicTransformOperator(LinearOperator):
self._op = SHTOperator(domain, target, space)
self._domain = self._op.domain
self._target = self._op.target
+ self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
return self._op.apply(x, mode)
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
-
def HarmonicSmoothingOperator(domain, sigma, space=None):
""" This function returns an operator that carries out a smoothing with
diff --git a/nifty5/operators/inversion_enabler.py b/nifty5/operators/inversion_enabler.py
index 9b7cc7654a871e12f40dfa5eed61dcf72b914a73..1be154473f6997d03a23f889d6cff4bbc70c24d2 100644
--- a/nifty5/operators/inversion_enabler.py
+++ b/nifty5/operators/inversion_enabler.py
@@ -51,15 +51,11 @@ class InversionEnabler(EndomorphicOperator):
"""
def __init__(self, op, iteration_controller, approximation=None):
- super(InversionEnabler, self).__init__()
self._op = op
self._ic = iteration_controller
self._approximation = approximation
self._domain = op.domain
-
- @property
- def capability(self):
- return self._addInverse[self._op.capability]
+ self._capability = self._addInverse[self._op.capability]
def apply(self, x, mode):
self._check_mode(mode)
diff --git a/nifty5/operators/laplace_operator.py b/nifty5/operators/laplace_operator.py
index a779b9279f3ee8ad44e4c72c35eabdba56324cd3..21a63e743f2b4bc58ccb695c9c630385e3638b4c 100644
--- a/nifty5/operators/laplace_operator.py
+++ b/nifty5/operators/laplace_operator.py
@@ -47,8 +47,8 @@ class LaplaceOperator(EndomorphicOperator):
"""
def __init__(self, domain, space=None, logarithmic=True):
- super(LaplaceOperator, self).__init__()
self._domain = DomainTuple.make(domain)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
self._space = infer_space(self._domain, space)
if not isinstance(self._domain[self._space], PowerSpace):
@@ -68,10 +68,6 @@ class LaplaceOperator(EndomorphicOperator):
self._dposc[1:] += self._dpos
self._dposc *= 0.5
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
-
def _times(self, x):
axes = x.domain.axes[self._space]
axis = axes[0]
diff --git a/nifty5/operators/linear_operator.py b/nifty5/operators/linear_operator.py
index a74b4292eba9ea0df18561de0d1f651adc17a1f0..4cca76fa39e5994e5d5d1be194acdecd4b35a72a 100644
--- a/nifty5/operators/linear_operator.py
+++ b/nifty5/operators/linear_operator.py
@@ -145,7 +145,7 @@ class LinearOperator(Operator):
:attr:`INVERSE_TIMES`, and :attr:`ADJOINT_INVERSE_TIMES`,
joined together by the "|" operator.
"""
- raise NotImplementedError
+ return self._capability
def apply(self, x, mode):
""" Applies the Operator to a given `x`, in a specified `mode`.
diff --git a/nifty5/operators/mask_operator.py b/nifty5/operators/mask_operator.py
index 6cc700f2f56eede76da405c927b5b7dbbc77b8af..abd9ed1115383a6fee8f47cf3f9f913e7b802569 100644
--- a/nifty5/operators/mask_operator.py
+++ b/nifty5/operators/mask_operator.py
@@ -38,6 +38,7 @@ class MaskOperator(LinearOperator):
self._domain = DomainTuple.make(mask.domain)
self._mask = np.logical_not(mask.to_global_data())
self._target = DomainTuple.make(UnstructuredDomain(self._mask.sum()))
+ self._capability = self.TIMES | self.ADJOINT_TIMES
def data_indices(self):
if len(self.domain.shape) == 1:
@@ -55,7 +56,3 @@ class MaskOperator(LinearOperator):
res[self._mask] = x
res[~self._mask] = 0
return Field.from_global_data(self.domain, res)
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/operator_adapter.py b/nifty5/operators/operator_adapter.py
index 6d4d79c38a519b04cd8571575a4fe927e77cdc5e..338f087c1ab6568fab2310ee990df42993012e30 100644
--- a/nifty5/operators/operator_adapter.py
+++ b/nifty5/operators/operator_adapter.py
@@ -45,17 +45,13 @@ class OperatorAdapter(LinearOperator):
"""
def __init__(self, op, op_transform):
- super(OperatorAdapter, self).__init__()
self._op = op
self._trafo = int(op_transform)
if self._trafo < 1 or self._trafo > 3:
raise ValueError("invalid operator transformation")
self._domain = self._op._dom(1 << self._trafo)
self._target = self._op._tgt(1 << self._trafo)
-
- @property
- def capability(self):
- return self._capTable[self._trafo][self._op.capability]
+ self._capability = self._capTable[self._trafo][self._op.capability]
def _flip_modes(self, trafo):
newtrafo = trafo ^ self._trafo
diff --git a/nifty5/operators/qht_operator.py b/nifty5/operators/qht_operator.py
index b2d0f07b374e4e81988b9b65f1baaba548fff011..2bd7919d70c627ec548683f594cb20767f1bf708 100644
--- a/nifty5/operators/qht_operator.py
+++ b/nifty5/operators/qht_operator.py
@@ -59,6 +59,7 @@ class QHTOperator(LinearOperator):
self._domain[self._space] = \
self._target[self._space].get_default_codomain()
self._domain = DomainTuple.make(self._domain)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
@@ -76,7 +77,3 @@ class QHTOperator(LinearOperator):
if i == ax:
x = dobj.redistribute(x, dist=ax)
return Field(self._tgt(mode), val=x)
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/relaxed_sum_operator.py b/nifty5/operators/relaxed_sum_operator.py
index fb4d6117d6d59f0f3083bf0ba0917bd2f3530591..53530e2c593d580e2340364d62ba02ea1a1b0602 100644
--- a/nifty5/operators/relaxed_sum_operator.py
+++ b/nifty5/operators/relaxed_sum_operator.py
@@ -32,7 +32,6 @@ class RelaxedSumOperator(LinearOperator):
"""Class representing sums of operators with compatible domains."""
def __init__(self, ops):
- super(RelaxedSumOperator, self).__init__()
self._ops = ops
self._domain = domain_union([op.domain for op in ops])
self._target = domain_union([op.target for op in ops])
@@ -50,10 +49,6 @@ class RelaxedSumOperator(LinearOperator):
def adjoint(self):
return RelaxedSumOperator([op.adjoint for op in self._ops])
- @property
- def capability(self):
- return self._capability
-
def apply(self, x, mode):
self._check_input(x, mode)
res = None
diff --git a/nifty5/operators/sampling_enabler.py b/nifty5/operators/sampling_enabler.py
index 728f4ad864044a97bb8a2d4a7fb1805d68d21ca3..66933b2cb2a9cae8e9bc747d1fac367a5f79a219 100644
--- a/nifty5/operators/sampling_enabler.py
+++ b/nifty5/operators/sampling_enabler.py
@@ -50,13 +50,13 @@ class SamplingEnabler(EndomorphicOperator):
def __init__(self, likelihood, prior, iteration_controller,
approximation=None):
- super(SamplingEnabler, self).__init__()
self._op = likelihood + prior
self._likelihood = likelihood
self._prior = prior
self._ic = iteration_controller
self._approximation = approximation
self._domain = self._op.domain
+ self._capability = self._op.capability
def draw_sample(self, from_inverse=False, dtype=np.float64):
try:
@@ -77,9 +77,5 @@ class SamplingEnabler(EndomorphicOperator):
energy, convergence = inverter(energy)
return energy.position
- @property
- def capability(self):
- return self._op.capability
-
def apply(self, x, mode):
return self._op.apply(x, mode)
diff --git a/nifty5/operators/sandwich_operator.py b/nifty5/operators/sandwich_operator.py
index 76ea55a504b87a67c9390564736277ced014dfbb..6895bd7a62507870d6747004f01ece73b2cc2d90 100644
--- a/nifty5/operators/sandwich_operator.py
+++ b/nifty5/operators/sandwich_operator.py
@@ -34,11 +34,11 @@ class SandwichOperator(EndomorphicOperator):
def __init__(self, bun, cheese, op, _callingfrommake=False):
if not _callingfrommake:
raise NotImplementedError
- super(SandwichOperator, self).__init__()
self._bun = bun
self._cheese = cheese
self._op = op
self._domain = op.domain
+ self._capability = op._capability
@staticmethod
def make(bun, cheese=None):
@@ -66,10 +66,6 @@ class SandwichOperator(EndomorphicOperator):
return op
return SandwichOperator(bun, cheese, op, _callingfrommake=True)
- @property
- def capability(self):
- return self._op.capability
-
def apply(self, x, mode):
return self._op.apply(x, mode)
diff --git a/nifty5/operators/scaling_operator.py b/nifty5/operators/scaling_operator.py
index b3d5e5d426b491d9a6ef76ded38b2c471f5e7bbd..3f8c5ff4e9b41ae32151b764d81972fa140dc70f 100644
--- a/nifty5/operators/scaling_operator.py
+++ b/nifty5/operators/scaling_operator.py
@@ -54,12 +54,12 @@ class ScalingOperator(EndomorphicOperator):
def __init__(self, factor, domain):
from ..sugar import makeDomain
- super(ScalingOperator, self).__init__()
if not np.isscalar(factor):
raise TypeError("Scalar required")
self._factor = factor
self._domain = makeDomain(domain)
+ self._capability = self._all_ops
def apply(self, x, mode):
self._check_input(x, mode)
@@ -82,10 +82,6 @@ class ScalingOperator(EndomorphicOperator):
fct = 1./fct
return ScalingOperator(fct, self._domain)
- @property
- def capability(self):
- return self._all_ops
-
def draw_sample(self, from_inverse=False, dtype=np.float64):
fct = self._factor
if fct.imag != 0. or fct.real < 0.:
diff --git a/nifty5/operators/simple_linear_operators.py b/nifty5/operators/simple_linear_operators.py
index cb047d3247b7c204b705ef36088dfb3903be2337..f5b5573db49f82a850a5629f247b5e12673a494b 100644
--- a/nifty5/operators/simple_linear_operators.py
+++ b/nifty5/operators/simple_linear_operators.py
@@ -33,14 +33,10 @@ from ..multi_field import MultiField
class VdotOperator(LinearOperator):
def __init__(self, field):
- super(VdotOperator, self).__init__()
self._field = field
self._domain = field.domain
self._target = DomainTuple.scalar_domain()
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
+ self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
@@ -51,13 +47,9 @@ class VdotOperator(LinearOperator):
class SumReductionOperator(LinearOperator):
def __init__(self, domain):
- super(SumReductionOperator, self).__init__()
self._domain = domain
self._target = DomainTuple.scalar_domain()
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
+ self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
@@ -68,12 +60,8 @@ class SumReductionOperator(LinearOperator):
class ConjugationOperator(EndomorphicOperator):
def __init__(self, domain):
- super(ConjugationOperator, self).__init__()
self._domain = domain
-
- @property
- def capability(self):
- return self._all_ops
+ self._capability = self._all_ops
def apply(self, x, mode):
self._check_input(x, mode)
@@ -82,12 +70,8 @@ class ConjugationOperator(EndomorphicOperator):
class Realizer(EndomorphicOperator):
def __init__(self, domain):
- super(Realizer, self).__init__()
self._domain = domain
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
+ self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
@@ -99,10 +83,7 @@ class FieldAdapter(LinearOperator):
self._domain = MultiDomain.make(dom)
self._name = name_dom
self._target = dom[name_dom]
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
+ self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
@@ -131,14 +112,10 @@ class GeometryRemover(LinearOperator):
"""
def __init__(self, domain):
- super(GeometryRemover, self).__init__()
self._domain = DomainTuple.make(domain)
target_list = [UnstructuredDomain(dom.shape) for dom in self._domain]
self._target = DomainTuple.make(target_list)
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
+ self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
@@ -162,6 +139,7 @@ class NullOperator(LinearOperator):
from ..sugar import makeDomain
self._domain = makeDomain(domain)
self._target = makeDomain(target)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
@staticmethod
def _nullfield(dom):
@@ -176,7 +154,3 @@ class NullOperator(LinearOperator):
if mode == self.TIMES:
return self._nullfield(self._target)
return self._nullfield(self._domain)
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/slope_operator.py b/nifty5/operators/slope_operator.py
index 618b022c875d128df7dd1b0787cc6f20afadb576..06d33e4adf382549c9ef689d1dc8b903193e90da 100644
--- a/nifty5/operators/slope_operator.py
+++ b/nifty5/operators/slope_operator.py
@@ -37,6 +37,7 @@ class SlopeOperator(LinearOperator):
self._domain = DomainTuple.make(domain)
self._target = DomainTuple.make(target)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
if self.domain[0].shape != (len(self.target[0].shape) + 1,):
raise AssertionError("Shape mismatch!")
@@ -74,7 +75,3 @@ class SlopeOperator(LinearOperator):
for i in range(self.ndim):
res[i] = np.sum(self.pos[i] * xglob) * self._sigmas[i]
return Field.from_global_data(self.domain, res)
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/sum_operator.py b/nifty5/operators/sum_operator.py
index de43f1650b9cef0ce002346d17ac6a8f0576f92f..0dcf73d7bcef809ea984826d0a39e3b587a77c04 100644
--- a/nifty5/operators/sum_operator.py
+++ b/nifty5/operators/sum_operator.py
@@ -31,7 +31,6 @@ class SumOperator(LinearOperator):
def __init__(self, ops, neg, dom, tgt, _callingfrommake=False):
if not _callingfrommake:
raise NotImplementedError
- super(SumOperator, self).__init__()
self._ops = ops
self._neg = neg
self._domain = dom
@@ -163,10 +162,6 @@ class SumOperator(LinearOperator):
def adjoint(self):
return self.make([op.adjoint for op in self._ops], self._neg)
- @property
- def capability(self):
- return self._capability
-
def apply(self, x, mode):
self._check_mode(mode)
res = None
diff --git a/nifty5/operators/symmetrizing_operator.py b/nifty5/operators/symmetrizing_operator.py
index 5c4d6276a64454a958978a48b6adc30837cc490b..b8f5370eed61199b093027e3baedb55acf2e8ee6 100644
--- a/nifty5/operators/symmetrizing_operator.py
+++ b/nifty5/operators/symmetrizing_operator.py
@@ -30,6 +30,7 @@ from .. import utilities
class SymmetrizingOperator(EndomorphicOperator):
def __init__(self, domain, space=0):
self._domain = DomainTuple.make(domain)
+ self._capability = self.TIMES | self.ADJOINT_TIMES
self._space = utilities.infer_space(self._domain, space)
dom = self._domain[self._space]
if not (isinstance(dom, LogRGSpace) and not dom.harmonic):
@@ -48,7 +49,3 @@ class SymmetrizingOperator(EndomorphicOperator):
if i == ax:
tmp = dobj.redistribute(tmp, dist=ax)
return Field(self.target, val=tmp)
-
- @property
- def capability(self):
- return self.TIMES | self.ADJOINT_TIMES
diff --git a/test/test_minimization/test_minimizers.py b/test/test_minimization/test_minimizers.py
index 057d45ad1826968f9b2b915291f7cbcad3546a17..35be855cae96a36c8aa168787dd3016fd412a6f9 100644
--- a/test/test_minimization/test_minimizers.py
+++ b/test/test_minimization/test_minimizers.py
@@ -99,15 +99,12 @@ class Test_Minimizers(unittest.TestCase):
class RBCurv(ift.EndomorphicOperator):
def __init__(self, loc):
self._loc = loc.to_global_data_rw()
+ self._capability = self.TIMES
@property
def domain(self):
return space
- @property
- def capability(self):
- return self.TIMES
-
def apply(self, x, mode):
self._check_input(x, mode)
inp = x.to_global_data_rw()