diff --git a/nifty6/field.py b/nifty6/field.py
index 8d4d31970afa822cda43dda44b28e005b5c6e581..1e11768eedee0e615f94cf9370c5d762e8560e32 100644
--- a/nifty6/field.py
+++ b/nifty6/field.py
@@ -20,10 +20,10 @@ import numpy as np
from . import utilities
from .domain_tuple import DomainTuple
-from .operators.operator import Operator
+from .operand import Operand
-class Field(Operator):
+class Field(Operand):
"""The discrete representation of a continuous field over multiple spaces.
Stores data arrays and carries all the needed meta-information (i.e. the
diff --git a/nifty6/linearization.py b/nifty6/linearization.py
index 9b84b86e9637217f859b82611272f4db9723d186..ee40973874f4a37997050831d634156d8dd2df6e 100644
--- a/nifty6/linearization.py
+++ b/nifty6/linearization.py
@@ -19,10 +19,10 @@ import numpy as np
from .sugar import makeOp
from . import utilities
-from .operators.operator import Operator
+from .operand import Operand
-class Linearization(Operator):
+class Linearization(Operand):
"""Let `A` be an operator and `x` a field. `Linearization` stores the value
of the operator application (i.e. `A(x)`), the local Jacobian
(i.e. `dA(x)/dx`) and, optionally, the local metric.
diff --git a/nifty6/multi_field.py b/nifty6/multi_field.py
index a05bd644ab88c84107bbf254bc7602464d0a0412..fc3005f8ab55620fc1f144d1ce0b457ecae4d154 100644
--- a/nifty6/multi_field.py
+++ b/nifty6/multi_field.py
@@ -21,10 +21,10 @@ from . import utilities
from .field import Field
from .multi_domain import MultiDomain
from .domain_tuple import DomainTuple
-from .operators.operator import Operator
+from .operand import Operand
-class MultiField(Operator):
+class MultiField(Operand):
def __init__(self, domain, val):
"""The discrete representation of a continuous field over a sum space.
diff --git a/nifty6/operators/linear_operator.py b/nifty6/operators/linear_operator.py
index 652c24d3192e16fce23430b626b6cbbaf43fe3ff..60f86cd0c50e81ed36408f5fcd782ad11282f6f1 100644
--- a/nifty6/operators/linear_operator.py
+++ b/nifty6/operators/linear_operator.py
@@ -169,8 +169,8 @@ class LinearOperator(Operator):
raise NotImplementedError
def __call__(self, x):
- """Same as :meth:`times`"""
- from ..linearization import Linearization
+ if isinstance(x, Operator):
+ return self @ x
if x.jac is not None:
return x.new(self(x.fld), self).prepend_jac(x.jac)
if x.val is not None:
diff --git a/nifty6/operators/operator.py b/nifty6/operators/operator.py
index fa4e63501588df64471426108470cebd70926a80..0ff499691e40d27bb2116c03ad25444b48e5d92e 100644
--- a/nifty6/operators/operator.py
+++ b/nifty6/operators/operator.py
@@ -46,75 +46,6 @@ class Operator(metaclass=NiftyMeta):
"""
return self._target
- @property
- def fld(self):
- """The field associated with this object
- For "pure" operators this is `None`. For Field-like objects this
- is a `Field` or a `MultiField` matching the object's `target`.
-
- Returns
- -------
- None or Field or MultiField : the field object
- """
- return None
-
- @property
- def val(self):
- """The numerical value associated with this object
- For "pure" operators this is `None`. For Field-like objects this
- is a `numpy.ndarray` or a dictionary of `numpy.ndarray`s matching the
- object's `target`.
-
- Returns
- -------
- None or numpy.ndarray or dictionary of np.ndarrays : the numerical value
- """
- return None
-
- @property
- def jac(self):
- """The Jacobian associated with this object
- For "pure" operators this is `None`. For Field-like objects this
- can be `None` (in which case the object is a constant), or it can be a
- `LinearOperator` with `domain` and `target` matching the object's.
-
- Returns
- -------
- None or LinearOperator : the Jacobian
-
- Notes
- -----
- if `value` is None, this must be `None` as well!
- """
- return None
-
- @property
- def want_metric(self):
- """Whether a metric should be computed for the full expression.
- This is `False` whenever `jac` is `None`. In other cases it signals
- that operators processing this object should compute the metric.
-
- Returns
- -------
- bool : whether the metric should be computed
- """
- return False
-
- @property
- def metric(self):
- """The metric associated with the object.
- This is `None`, except when all the following conditions hold:
- - `want_metric` is `True`
- - `target` is the scalar domain
- - the operator chain contained an operator which could compute the
- metric
-
- Returns
- -------
- None or LinearOperator : the metric
- """
- return None
-
@staticmethod
def _check_domain_equality(dom_op, dom_field):
if dom_op != dom_field:
@@ -240,7 +171,8 @@ class Operator(metaclass=NiftyMeta):
def _check_input(self, x):
from .scaling_operator import ScalingOperator
- if not (isinstance(x, Operator) and x.val is not None):
+ from ..operand import Operand
+ if not isinstance(x, Operand):
raise TypeError
if x.jac is not None:
if not isinstance(x.jac, ScalingOperator):
@@ -250,13 +182,14 @@ class Operator(metaclass=NiftyMeta):
self._check_domain_equality(self._domain, x.domain)
def __call__(self, x):
- if not isinstance(x, Operator):
+ if isinstance(x, Operator):
+ return self @ x
+ from ..operand import Operand
+ if not isinstance(x, Operand):
raise TypeError
if x.jac is not None:
return self.apply(x.trivial_jac()).prepend_jac(x.jac)
- elif x.val is not None:
- return self.apply(x)
- return self @ x
+ return self.apply(x)
def ducktape(self, name):
from .simple_linear_operators import ducktape
diff --git a/nifty6/operators/scaling_operator.py b/nifty6/operators/scaling_operator.py
index b99d3380fa751f3f7bbfb8b15931efebd46ebad9..4706d42744ecb4e669ac2b0d7f8dcf173103b1ee 100644
--- a/nifty6/operators/scaling_operator.py
+++ b/nifty6/operators/scaling_operator.py
@@ -98,6 +98,9 @@ class ScalingOperator(EndomorphicOperator):
def __call__(self, other):
res = EndomorphicOperator.__call__(self, other)
+ from .operator import Operator
+ if isinstance(res, Operator):
+ return res
if np.isreal(self._factor) and self._factor >= 0:
if other.jac is not None:
if other.metric is not None: