From c9a6a709f469164228231a5c7b8f3301dce9de20 Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Wed, 8 Aug 2018 19:45:08 +0200
Subject: [PATCH] long overdue cleanup
---
demos/polynomial_fit.py | 8 ----
nifty5/library/amplitude_model.py | 8 ----
nifty5/library/correlated_fields.py | 8 ----
nifty5/library/inverse_gamma_model.py | 10 +----
nifty5/library/los_response.py | 8 ----
nifty5/multi_domain.py | 1 +
nifty5/operators/block_diagonal_operator.py | 4 --
nifty5/operators/central_zero_padder.py | 8 ----
nifty5/operators/chain_operator.py | 10 +----
nifty5/operators/diagonal_operator.py | 4 --
nifty5/operators/dof_distributor.py | 8 ----
nifty5/operators/domain_distributor.py | 8 ----
nifty5/operators/endomorphic_operator.py | 2 +-
nifty5/operators/energy_operators.py | 37 +++-------------
nifty5/operators/exp_transform.py | 8 ----
nifty5/operators/field_zero_padder.py | 8 ----
nifty5/operators/harmonic_operators.py | 34 +-------------
nifty5/operators/inversion_enabler.py | 5 +--
nifty5/operators/laplace_operator.py | 4 --
nifty5/operators/mask_operator.py | 8 ----
nifty5/operators/operator.py | 42 +++---------------
nifty5/operators/operator_adapter.py | 10 +----
nifty5/operators/qht_operator.py | 8 ----
nifty5/operators/relaxed_sum_operator.py | 8 ----
nifty5/operators/sampling_enabler.py | 5 +--
nifty5/operators/sandwich_operator.py | 5 +--
nifty5/operators/scaling_operator.py | 4 --
nifty5/operators/simple_linear_operators.py | 49 +--------------------
nifty5/operators/slope_operator.py | 8 ----
nifty5/operators/sum_operator.py | 8 ----
nifty5/operators/symmetrizing_operator.py | 4 --
31 files changed, 25 insertions(+), 317 deletions(-)
diff --git a/demos/polynomial_fit.py b/demos/polynomial_fit.py
index 108d9a06d..969e6822a 100644
--- a/demos/polynomial_fit.py
+++ b/demos/polynomial_fit.py
@@ -62,14 +62,6 @@ class PolynomialResponse(ift.LinearOperator):
out = self._mat.conj().T.dot(val)
return ift.from_global_data(self._tgt(mode), out)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/library/amplitude_model.py b/nifty5/library/amplitude_model.py
index f9ff03b79..77842d2ef 100644
--- a/nifty5/library/amplitude_model.py
+++ b/nifty5/library/amplitude_model.py
@@ -133,14 +133,6 @@ class AmplitudeModel(Operator):
self._smooth_op = sym(qht(ceps))
self._keys = tuple(keys)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
def apply(self, x):
smooth_spec = self._smooth_op(x[self._keys[0]])
phi = x[self._keys[1]] + self._norm_phi_mean
diff --git a/nifty5/library/correlated_fields.py b/nifty5/library/correlated_fields.py
index b2edc026c..07f4a90b3 100644
--- a/nifty5/library/correlated_fields.py
+++ b/nifty5/library/correlated_fields.py
@@ -50,14 +50,6 @@ class CorrelatedField(Operator):
(self._amplitude_model.domain,
MultiDomain.make({"xi": self._h_space})))
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._ht.target
-
def apply(self, x):
A = self._power_distributor(self._amplitude_model(x))
correlated_field_h = A * x["xi"]
diff --git a/nifty5/library/inverse_gamma_model.py b/nifty5/library/inverse_gamma_model.py
index 3cce6ae16..e40ac0960 100644
--- a/nifty5/library/inverse_gamma_model.py
+++ b/nifty5/library/inverse_gamma_model.py
@@ -30,18 +30,10 @@ from ..sugar import makeOp
class InverseGammaModel(Operator):
def __init__(self, domain, alpha, q):
- self._domain = domain
+ self._domain = self._target = domain
self._alpha = alpha
self._q = q
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._domain
-
def apply(self, x):
lin = isinstance(x, Linearization)
val = x.val.local_data if lin else x.local_data
diff --git a/nifty5/library/los_response.py b/nifty5/library/los_response.py
index ec946bdb7..f8c112f23 100644
--- a/nifty5/library/los_response.py
+++ b/nifty5/library/los_response.py
@@ -221,14 +221,6 @@ class LOSResponse(LinearOperator):
self._target = DomainTuple.make(UnstructuredDomain(nlos))
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/multi_domain.py b/nifty5/multi_domain.py
index a4fb03aec..f2257ba96 100644
--- a/nifty5/multi_domain.py
+++ b/nifty5/multi_domain.py
@@ -109,6 +109,7 @@ class MultiDomain(object):
@staticmethod
def union(inp):
res = {}
+ # FIXME speed up!
for dom in inp:
for key, subdom in zip(dom._keys, dom._domains):
if key in res:
diff --git a/nifty5/operators/block_diagonal_operator.py b/nifty5/operators/block_diagonal_operator.py
index 956be8dcb..9a601e15c 100644
--- a/nifty5/operators/block_diagonal_operator.py
+++ b/nifty5/operators/block_diagonal_operator.py
@@ -45,10 +45,6 @@ class BlockDiagonalOperator(EndomorphicOperator):
if op is not None:
self._cap &= op.capability
- @property
- def domain(self):
- return self._domain
-
@property
def capability(self):
return self._cap
diff --git a/nifty5/operators/central_zero_padder.py b/nifty5/operators/central_zero_padder.py
index 2a7f0649c..1f48c8d83 100644
--- a/nifty5/operators/central_zero_padder.py
+++ b/nifty5/operators/central_zero_padder.py
@@ -53,14 +53,6 @@ class CentralZeroPadder(LinearOperator):
self.slicer[i] = tuple(tmp)
self.slicer = tuple(self.slicer)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/chain_operator.py b/nifty5/operators/chain_operator.py
index 9f78bae75..548ba0474 100644
--- a/nifty5/operators/chain_operator.py
+++ b/nifty5/operators/chain_operator.py
@@ -36,6 +36,8 @@ class ChainOperator(LinearOperator):
self._capability = self._all_ops
for op in ops:
self._capability &= op.capability
+ self._domain = self._ops[-1].domain
+ self._target = self._ops[0].target
@staticmethod
def simplify(ops):
@@ -117,14 +119,6 @@ class ChainOperator(LinearOperator):
return ops[0]
return ChainOperator(ops, _callingfrommake=True)
- @property
- def domain(self):
- return self._ops[-1].domain
-
- @property
- def target(self):
- return self._ops[0].target
-
def _flip_modes(self, trafo):
ADJ = self.ADJOINT_BIT
INV = self.INVERSE_BIT
diff --git a/nifty5/operators/diagonal_operator.py b/nifty5/operators/diagonal_operator.py
index 48b06cff5..506964163 100644
--- a/nifty5/operators/diagonal_operator.py
+++ b/nifty5/operators/diagonal_operator.py
@@ -150,10 +150,6 @@ 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 domain(self):
- return self._domain
-
@property
def capability(self):
return self._all_ops
diff --git a/nifty5/operators/dof_distributor.py b/nifty5/operators/dof_distributor.py
index 31ce2eca4..ef864760c 100644
--- a/nifty5/operators/dof_distributor.py
+++ b/nifty5/operators/dof_distributor.py
@@ -143,14 +143,6 @@ class DOFDistributor(LinearOperator):
self._check_input(x, mode)
return self._times(x) if mode == self.TIMES else self._adjoint_times(x)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@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 eb1899c76..08939a45f 100644
--- a/nifty5/operators/domain_distributor.py
+++ b/nifty5/operators/domain_distributor.py
@@ -36,14 +36,6 @@ class DomainDistributor(LinearOperator):
if i in self._spaces]
self._domain = DomainTuple.make(self._domain)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
def apply(self, x, mode):
self._check_input(x, mode)
if mode == self.TIMES:
diff --git a/nifty5/operators/endomorphic_operator.py b/nifty5/operators/endomorphic_operator.py
index 081742441..56e65b039 100644
--- a/nifty5/operators/endomorphic_operator.py
+++ b/nifty5/operators/endomorphic_operator.py
@@ -38,7 +38,7 @@ class EndomorphicOperator(LinearOperator):
Returns `self.domain`, because this is also the target domain
for endomorphic operators."""
- return self.domain
+ return self._domain
def draw_sample(self, from_inverse=False, dtype=np.float64):
"""Generate a zero-mean sample
diff --git a/nifty5/operators/energy_operators.py b/nifty5/operators/energy_operators.py
index bf779728d..dc5c75e94 100644
--- a/nifty5/operators/energy_operators.py
+++ b/nifty5/operators/energy_operators.py
@@ -33,20 +33,12 @@ from .simple_linear_operators import VdotOperator
class EnergyOperator(Operator):
_target = DomainTuple.scalar_domain()
- @property
- def target(self):
- return EnergyOperator._target
-
class SquaredNormOperator(EnergyOperator):
def __init__(self, domain):
super(SquaredNormOperator, self).__init__()
self._domain = domain
- @property
- def domain(self):
- return self._domain
-
def apply(self, x):
if isinstance(x, Linearization):
val = Field(self._target, x.val.vdot(x.val))
@@ -62,10 +54,7 @@ class QuadraticFormOperator(EnergyOperator):
if not isinstance(op, EndomorphicOperator):
raise TypeError("op must be an EndomorphicOperator")
self._op = op
-
- @property
- def domain(self):
- return self._op.domain
+ self._domain = op.domain
def apply(self, x):
if isinstance(x, Linearization):
@@ -102,10 +91,6 @@ class GaussianEnergy(EnergyOperator):
if self._domain is not newdom:
raise ValueError("domain mismatch")
- @property
- def domain(self):
- return self._domain
-
def apply(self, x):
residual = x if self._mean is None else x-self._mean
res = self._op(residual)
@@ -118,10 +103,7 @@ class GaussianEnergy(EnergyOperator):
class PoissonianEnergy(EnergyOperator):
def __init__(self, op, d):
self._op, self._d = op, d
-
- @property
- def domain(self):
- return self._op.domain
+ self._domain = d.domain
def apply(self, x):
x = self._op(x)
@@ -136,10 +118,7 @@ class BernoulliEnergy(EnergyOperator):
def __init__(self, p, d):
self._p = p
self._d = d
-
- @property
- def domain(self):
- return self._p.domain
+ self._domain = d.domain
def apply(self, x):
x = self._p(x)
@@ -157,10 +136,7 @@ class Hamiltonian(EnergyOperator):
self._lh = lh
self._prior = GaussianEnergy(domain=lh.domain)
self._ic_samp = ic_samp
-
- @property
- def domain(self):
- return self._lh.domain
+ self._domain = lh.domain
def apply(self, x):
if self._ic_samp is None or not isinstance(x, Linearization):
@@ -182,12 +158,9 @@ class SampledKullbachLeiblerDivergence(EnergyOperator):
"""
super(SampledKullbachLeiblerDivergence, self).__init__()
self._h = h
+ self._domain = h.domain
self._res_samples = tuple(res_samples)
- @property
- def domain(self):
- return self._h.domain
-
def apply(self, x):
res = (utilities.my_sum(map(lambda v: self._h(x+v), self._res_samples)) *
(1./len(self._res_samples)))
diff --git a/nifty5/operators/exp_transform.py b/nifty5/operators/exp_transform.py
index c4fbcad89..db12ba311 100644
--- a/nifty5/operators/exp_transform.py
+++ b/nifty5/operators/exp_transform.py
@@ -84,14 +84,6 @@ class ExpTransform(LinearOperator):
self._domain[self._space] = log_space
self._domain = DomainTuple.make(self._domain)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
def apply(self, x, mode):
self._check_input(x, mode)
x = x.val
diff --git a/nifty5/operators/field_zero_padder.py b/nifty5/operators/field_zero_padder.py
index d3afb12bb..b0f67daa4 100644
--- a/nifty5/operators/field_zero_padder.py
+++ b/nifty5/operators/field_zero_padder.py
@@ -31,14 +31,6 @@ class FieldZeroPadder(LinearOperator):
self._target[self._space] = tgt
self._target = DomainTuple.make(self._target)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/harmonic_operators.py b/nifty5/operators/harmonic_operators.py
index 6eeb5b434..e07d3da10 100644
--- a/nifty5/operators/harmonic_operators.py
+++ b/nifty5/operators/harmonic_operators.py
@@ -127,14 +127,6 @@ class FFTOperator(LinearOperator):
fct *= self._target[self._space].scalar_dvol
return Tval if fct == 1 else Tval*fct
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self._all_ops
@@ -247,14 +239,6 @@ class HartleyOperator(LinearOperator):
fct = self._target[self._space].scalar_dvol
return Tval if fct == 1 else Tval*fct
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self._all_ops
@@ -367,14 +351,6 @@ class SHTOperator(LinearOperator):
odat = dobj.redistribute(odat, dist=dobj.distaxis(x.val))
return Field(tdom, odat)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
@@ -421,19 +397,13 @@ class HarmonicTransformOperator(LinearOperator):
self._op = HartleyOperator(domain, target, space)
else:
self._op = SHTOperator(domain, target, space)
+ self._domain = self._op.domain
+ self._target = self._op.target
def apply(self, x, mode):
self._check_input(x, mode)
return self._op.apply(x, mode)
- @property
- def domain(self):
- return self._op.domain
-
- @property
- def target(self):
- return self._op.target
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/inversion_enabler.py b/nifty5/operators/inversion_enabler.py
index ba9fff48a..9b7cc7654 100644
--- a/nifty5/operators/inversion_enabler.py
+++ b/nifty5/operators/inversion_enabler.py
@@ -55,10 +55,7 @@ class InversionEnabler(EndomorphicOperator):
self._op = op
self._ic = iteration_controller
self._approximation = approximation
-
- @property
- def domain(self):
- return self._op.domain
+ self._domain = op.domain
@property
def capability(self):
diff --git a/nifty5/operators/laplace_operator.py b/nifty5/operators/laplace_operator.py
index 0725d8ea3..a779b9279 100644
--- a/nifty5/operators/laplace_operator.py
+++ b/nifty5/operators/laplace_operator.py
@@ -68,10 +68,6 @@ class LaplaceOperator(EndomorphicOperator):
self._dposc[1:] += self._dpos
self._dposc *= 0.5
- @property
- def domain(self):
- return self._domain
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
diff --git a/nifty5/operators/mask_operator.py b/nifty5/operators/mask_operator.py
index 84b15e3cb..6cc700f2f 100644
--- a/nifty5/operators/mask_operator.py
+++ b/nifty5/operators/mask_operator.py
@@ -59,11 +59,3 @@ class MaskOperator(LinearOperator):
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
-
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
diff --git a/nifty5/operators/operator.py b/nifty5/operators/operator.py
index 88efbbf79..24b5e4357 100644
--- a/nifty5/operators/operator.py
+++ b/nifty5/operators/operator.py
@@ -15,14 +15,14 @@ class Operator(NiftyMetaBase()):
"""DomainTuple or MultiDomain : the operator's input domain
The domain on which the Operator's input Field lives."""
- raise NotImplementedError
+ return self._domain
@property
def target(self):
"""DomainTuple or MultiDomain : the operator's output domain
The domain on which the Operator's output Field lives."""
- raise NotImplementedError
+ return self._target
def scale(self, factor):
if factor == 1:
@@ -73,17 +73,9 @@ for f in ["sqrt", "exp", "log", "tanh", "positive_tanh"]:
class _FunctionApplier(Operator):
def __init__(self, domain, funcname):
from ..sugar import makeDomain
- self._domain = makeDomain(domain)
+ self._domain = self._target = makeDomain(domain)
self._funcname = funcname
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._domain
-
def apply(self, x):
return getattr(x, self._funcname)()
@@ -114,14 +106,8 @@ class _CombinedOperator(Operator):
class _OpChain(_CombinedOperator):
def __init__(self, ops, _callingfrommake=False):
super(_OpChain, self).__init__(ops, _callingfrommake)
-
- @property
- def domain(self):
- return self._ops[-1].domain
-
- @property
- def target(self):
- return self._ops[0].target
+ self._domain = self._ops[-1].domain
+ self._target = self._ops[0].target
def apply(self, x):
for op in reversed(self._ops):
@@ -132,14 +118,8 @@ class _OpChain(_CombinedOperator):
class _OpProd(_CombinedOperator):
def __init__(self, ops, _callingfrommake=False):
super(_OpProd, self).__init__(ops, _callingfrommake)
-
- @property
- def domain(self):
- return self._ops[0].domain
-
- @property
- def target(self):
- return self._ops[0].target
+ self._domain = self._ops[0].domain
+ self._target = self._ops[0].target
def apply(self, x):
return my_product(map(lambda op: op(x), self._ops))
@@ -151,13 +131,5 @@ class _OpSum(_CombinedOperator):
self._domain = domain_union([op.domain for op in self._ops])
self._target = domain_union([op.target for op in self._ops])
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
def apply(self, x):
raise NotImplementedError
diff --git a/nifty5/operators/operator_adapter.py b/nifty5/operators/operator_adapter.py
index 310a56b66..6d4d79c38 100644
--- a/nifty5/operators/operator_adapter.py
+++ b/nifty5/operators/operator_adapter.py
@@ -50,14 +50,8 @@ class OperatorAdapter(LinearOperator):
self._trafo = int(op_transform)
if self._trafo < 1 or self._trafo > 3:
raise ValueError("invalid operator transformation")
-
- @property
- def domain(self):
- return self._op._dom(1 << self._trafo)
-
- @property
- def target(self):
- return self._op._tgt(1 << self._trafo)
+ self._domain = self._op._dom(1 << self._trafo)
+ self._target = self._op._tgt(1 << self._trafo)
@property
def capability(self):
diff --git a/nifty5/operators/qht_operator.py b/nifty5/operators/qht_operator.py
index d60bb1248..b2d0f07b3 100644
--- a/nifty5/operators/qht_operator.py
+++ b/nifty5/operators/qht_operator.py
@@ -60,14 +60,6 @@ class QHTOperator(LinearOperator):
self._target[self._space].get_default_codomain()
self._domain = DomainTuple.make(self._domain)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
def apply(self, x, mode):
self._check_input(x, mode)
dom = self._domain[self._space]
diff --git a/nifty5/operators/relaxed_sum_operator.py b/nifty5/operators/relaxed_sum_operator.py
index 4b52cea43..fb4d6117d 100644
--- a/nifty5/operators/relaxed_sum_operator.py
+++ b/nifty5/operators/relaxed_sum_operator.py
@@ -46,14 +46,6 @@ class RelaxedSumOperator(LinearOperator):
# else:
# self._ops += [op]
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def adjoint(self):
return RelaxedSumOperator([op.adjoint for op in self._ops])
diff --git a/nifty5/operators/sampling_enabler.py b/nifty5/operators/sampling_enabler.py
index f2666db20..728f4ad86 100644
--- a/nifty5/operators/sampling_enabler.py
+++ b/nifty5/operators/sampling_enabler.py
@@ -56,6 +56,7 @@ class SamplingEnabler(EndomorphicOperator):
self._prior = prior
self._ic = iteration_controller
self._approximation = approximation
+ self._domain = self._op.domain
def draw_sample(self, from_inverse=False, dtype=np.float64):
try:
@@ -76,10 +77,6 @@ class SamplingEnabler(EndomorphicOperator):
energy, convergence = inverter(energy)
return energy.position
- @property
- def domain(self):
- return self._op.domain
-
@property
def capability(self):
return self._op.capability
diff --git a/nifty5/operators/sandwich_operator.py b/nifty5/operators/sandwich_operator.py
index 480e098db..76ea55a50 100644
--- a/nifty5/operators/sandwich_operator.py
+++ b/nifty5/operators/sandwich_operator.py
@@ -38,6 +38,7 @@ class SandwichOperator(EndomorphicOperator):
self._bun = bun
self._cheese = cheese
self._op = op
+ self._domain = op.domain
@staticmethod
def make(bun, cheese=None):
@@ -65,10 +66,6 @@ class SandwichOperator(EndomorphicOperator):
return op
return SandwichOperator(bun, cheese, op, _callingfrommake=True)
- @property
- def domain(self):
- return self._op.domain
-
@property
def capability(self):
return self._op.capability
diff --git a/nifty5/operators/scaling_operator.py b/nifty5/operators/scaling_operator.py
index 371b5b440..b3d5e5d42 100644
--- a/nifty5/operators/scaling_operator.py
+++ b/nifty5/operators/scaling_operator.py
@@ -82,10 +82,6 @@ class ScalingOperator(EndomorphicOperator):
fct = 1./fct
return ScalingOperator(fct, self._domain)
- @property
- def domain(self):
- return self._domain
-
@property
def capability(self):
return self._all_ops
diff --git a/nifty5/operators/simple_linear_operators.py b/nifty5/operators/simple_linear_operators.py
index bef589f75..cb047d324 100644
--- a/nifty5/operators/simple_linear_operators.py
+++ b/nifty5/operators/simple_linear_operators.py
@@ -35,16 +35,9 @@ 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 domain(self):
- return self._field.domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
@@ -62,14 +55,6 @@ class SumReductionOperator(LinearOperator):
self._domain = domain
self._target = DomainTuple.scalar_domain()
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
@@ -86,10 +71,6 @@ class ConjugationOperator(EndomorphicOperator):
super(ConjugationOperator, self).__init__()
self._domain = domain
- @property
- def domain(self):
- return self._domain
-
@property
def capability(self):
return self._all_ops
@@ -104,10 +85,6 @@ class Realizer(EndomorphicOperator):
super(Realizer, self).__init__()
self._domain = domain
- @property
- def domain(self):
- return self._domain
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
@@ -123,14 +100,6 @@ class FieldAdapter(LinearOperator):
self._name = name_dom
self._target = dom[name_dom]
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
@@ -167,14 +136,6 @@ class GeometryRemover(LinearOperator):
target_list = [UnstructuredDomain(dom.shape) for dom in self._domain]
self._target = DomainTuple.make(target_list)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def capability(self):
return self.TIMES | self.ADJOINT_TIMES
@@ -216,14 +177,6 @@ class NullOperator(LinearOperator):
return self._nullfield(self._target)
return self._nullfield(self._domain)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@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 dadee40c0..618b022c8 100644
--- a/nifty5/operators/slope_operator.py
+++ b/nifty5/operators/slope_operator.py
@@ -56,14 +56,6 @@ class SlopeOperator(LinearOperator):
self.pos[i] += tmp.reshape(
(1,)*i + (shape[i],) + (1,)*(self.ndim-i-1))
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
def apply(self, x, mode):
self._check_input(x, mode)
diff --git a/nifty5/operators/sum_operator.py b/nifty5/operators/sum_operator.py
index a2b9ffe6c..de43f1650 100644
--- a/nifty5/operators/sum_operator.py
+++ b/nifty5/operators/sum_operator.py
@@ -159,14 +159,6 @@ class SumOperator(LinearOperator):
return ops[0]
return SumOperator(ops, neg, dom, tgt, _callingfrommake=True)
- @property
- def domain(self):
- return self._domain
-
- @property
- def target(self):
- return self._target
-
@property
def adjoint(self):
return self.make([op.adjoint for op in self._ops], self._neg)
diff --git a/nifty5/operators/symmetrizing_operator.py b/nifty5/operators/symmetrizing_operator.py
index a8c9e49ed..5c4d6276a 100644
--- a/nifty5/operators/symmetrizing_operator.py
+++ b/nifty5/operators/symmetrizing_operator.py
@@ -35,10 +35,6 @@ class SymmetrizingOperator(EndomorphicOperator):
if not (isinstance(dom, LogRGSpace) and not dom.harmonic):
raise TypeError("nonharmonic LogRGSpace needed")
- @property
- def domain(self):
- return self._domain
-
def apply(self, x, mode):
self._check_input(x, mode)
tmp = x.val.copy()
--
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