living -> defined

docs/source/code.rst

@@ -123,7 +123,7 @@ Some examples are:
 Consequently, NIFTy defines a class called :class:`DomainTuple` holding
 a sequence of :class:`Domain` objects, which is used to specify full field
 domains. In principle, a :class:`DomainTuple` can even be empty, which implies
-that the field living on it is a scalar.
+that the field defined on it is a scalar.
 
 A :class:`DomainTuple` supports iteration and indexing, and also provides the
 properties :attr:`~DomainTuple.shape`, :attr:`~DomainTuple.size` in analogy to
@@ -152,7 +152,7 @@ Fields support a wide range of arithmetic operations, either involving two
 fields with equal domains, or a field and a scalar.
 Contractions (like summation, integration, minimum/maximum, computation of
 statistical moments) can be carried out either over an entire field (producing
-a scalar result) or over sub-domains (resulting in a field living on a smaller
+a scalar result) or over sub-domains (resulting in a field defined on a smaller
 domain). Scalar products of two fields can also be computed easily.
 
 There is also a set of convenience functions to generate fields with constant
@@ -250,7 +250,7 @@ provided by NIFTy's :class:`InversionEnabler` class, which is itself a linear
 operator.
 
 Direct multiplication and adjoint inverse multiplication transform a field
-living on the operator's :attr:`~LinearOperator.domain` to one living on the operator's :attr:`~LinearOperator.target`, whereas adjoint multiplication
+defined on the operator's :attr:`~LinearOperator.domain` to one defined on the operator's :attr:`~LinearOperator.target`, whereas adjoint multiplication
 and inverse multiplication transform from :attr:`~LinearOperator.target` to :attr:`~LinearOperator.domain`.
 
 Operators with identical domain and target can be derived from
@@ -364,7 +364,7 @@ These hold the prescription how to calculate the function's
 (optionally) :attr:`~Energy.metric` at any given :attr:`~Energy.position`
 in parameter space.
 Function values are floating-point scalars, gradients have the form of fields
-living on the energy's position domain, and metrics are represented by
+defined on the energy's position domain, and metrics are represented by
 linear operator objects.
 
 Energies are classes that typically have to be provided by the user when

nifty5/domain_tuple.py

@@ -103,7 +103,7 @@ class DomainTuple(object):
         """tuple of int: number of pixels along each axis
 
         The shape of the array-like object required to store information
-        living on the DomainTuple.
+        defined on the DomainTuple.
         """
         return self._shape
 
@@ -112,7 +112,7 @@ class DomainTuple(object):
         """tuple of int: number of pixels along each axis on the local task
 
         The shape of the array-like object required to store information
-        living on part of the domain which is stored on the local MPI task.
+        defined on part of the domain which is stored on the local MPI task.
         """
         from .dobj import local_shape
         return local_shape(self._shape)

nifty5/domains/domain.py

@@ -80,7 +80,7 @@ class Domain(NiftyMetaBase()):
         """tuple of int: number of pixels along each axis
 
         The shape of the array-like object required to store information
-        living on the domain.
+        defined on the domain.
         """
         raise NotImplementedError
 
@@ -89,7 +89,7 @@ class Domain(NiftyMetaBase()):
         """tuple of int: number of pixels along each axis on the local task
 
         The shape of the array-like object required to store information
-        living on part of the domain which is stored on the local MPI task.
+        defined on part of the domain which is stored on the local MPI task.
         """
         from ..dobj import local_shape
         return local_shape(self.shape)

nifty5/field.py

@@ -159,7 +159,7 @@ class Field(object):
         Returns
         -------
         Field
-            Field living on `new_domain`, but with the same data as `self`.
+            Field defined on `new_domain`, but with the same data as `self`.
         """
         return Field(DomainTuple.make(new_domain), self._val)
 

nifty5/minimization/energy_adapter.py

@@ -25,11 +25,11 @@ class EnergyAdapter(Energy):
 
     Parameters
     -----------
-    position: Field or MultiField living on the operator's input domain.
-        The position where the minimization process is started
     op: Operator with a scalar target domain
         The expression computing the energy from the input data
     constants: list of strings (default: [])
+    position: Field or MultiField
+        The position where the minimization process is started.
         The component names of the operator's input domain which are assumed
         to be constant during the minimization process.
         If the operator's input domain is not a MultiField, this must be empty.

nifty5/multi_domain.py

@@ -22,8 +22,8 @@ from .utilities import frozendict, indent
 class MultiDomain(object):
     """A tuple of domains corresponding to a direct sum.
 
-    This class is the domain of the direct sum of fields living
-    over (possibly different) domains. To make an instance
+    This class is the domain of the direct sum of fields defined
+    on (possibly different) domains. To make an instance
     of this class, call `MultiDomain.make(inp)`.
     """
     _domainCache = {}

nifty5/operators/contraction_operator.py

@@ -35,7 +35,7 @@ class ContractionOperator(LinearOperator):
     spaces : int or tuple of int
         The elements of "domain" which are contracted.
     weight : int, default=0
-        if nonzero, the fields living on self.domain are weighted with the
+        if nonzero, the fields defined on self.domain are weighted with the
         specified power.
     """
 

nifty5/operators/linear_operator.py

@@ -148,7 +148,7 @@ class LinearOperator(Operator):
         Parameters
         ----------
         x : Field
-            The input Field, living on the Operator's domain or target,
+            The input Field, defined on the Operator's domain or target,
             depending on mode.
 
         mode : int
@@ -161,7 +161,7 @@ class LinearOperator(Operator):
         Returns
         -------
         Field
-            The processed Field living on the Operator's target or domain,
+            The processed Field defined on the Operator's target or domain,
             depending on mode.
         """
         raise NotImplementedError
@@ -183,12 +183,12 @@ class LinearOperator(Operator):
         Parameters
         ----------
         x : Field
-            The input Field, living on the Operator's domain.
+            The input Field, defined on the Operator's domain.
 
         Returns
         -------
         Field
-            The processed Field living on the Operator's target domain.
+            The processed Field defined on the Operator's target domain.
         """
         return self.apply(x, self.TIMES)
 
@@ -198,12 +198,12 @@ class LinearOperator(Operator):
         Parameters
         ----------
         x : Field
-            The input Field, living on the Operator's target domain
+            The input Field, defined on the Operator's target domain
 
         Returns
         -------
         Field
-            The processed Field living on the Operator's domain.
+            The processed Field defined on the Operator's domain.
         """
         return self.apply(x, self.INVERSE_TIMES)
 
@@ -213,12 +213,12 @@ class LinearOperator(Operator):
         Parameters
         ----------
         x : Field
-            The input Field, living on the Operator's target domain
+            The input Field, defined on the Operator's target domain
 
         Returns
         -------
         Field
-            The processed Field living on the Operator's domain.
+            The processed Field defined on the Operator's domain.
         """
         return self.apply(x, self.ADJOINT_TIMES)
 
@@ -228,12 +228,12 @@ class LinearOperator(Operator):
         Parameters
         ----------
         x : Field
-            The input Field, living on the Operator's domain.
+            The input Field, defined on the Operator's domain.
 
         Returns
         -------
         Field
-            The processed Field living on the Operator's target domain.
+            The processed Field defined on the Operator's target domain.
 
         Notes
         -----

nifty5/operators/mask_operator.py

@@ -24,7 +24,7 @@ from .linear_operator import LinearOperator
 
 
 # MR FIXME: this needs a redesign to avoid most _global_data() calls
-# Possible approach: keep everything living on `domain` distributed and only
+# Possible approach: keep everything defined on `domain` distributed and only
 # collect the unstructured Fields.
 class MaskOperator(LinearOperator):
     def __init__(self, mask):

nifty5/operators/operator.py

@@ -20,7 +20,7 @@ from ..utilities import NiftyMetaBase, indent
 
 
 class Operator(NiftyMetaBase()):
-    """Transforms values living on one domain into values living on another
+    """Transforms values defined on one domain into values defined on another
     domain, and can also provide the Jacobian.
     """
 

nifty5/plot.py

@@ -276,7 +276,7 @@ class Plot(object):
         f: Field, or list of Field objects
             If `f` is a single Field, it must be defined on a single `RGSpace`,
             `PowerSpace`, `HPSpace`, `GLSpace`.
-            If it is a list, all list members must be Fields living over the
+            If it is a list, all list members must be Fields defined over the
             same one-dimensional `RGSpace` or `PowerSpace`.
         title: string
             title of the plot

nifty5/sugar.py

@@ -52,7 +52,7 @@ def PS_field(pspace, func):
 
     Returns
     -------
-    Field : a field living on (pspace,) containing the computed function values
+    Field : a field defined on (pspace,) containing the computed function values
     """
     if not isinstance(pspace, PowerSpace):
         raise TypeError
@@ -167,7 +167,7 @@ def power_analyze(field, spaces=None, binbounds=None,
 
 
 def _create_power_field(domain, power_spectrum):
-    if not callable(power_spectrum):  # we have a Field living on a PowerSpace
+    if not callable(power_spectrum):  # we have a Field defined on a PowerSpace
         if not isinstance(power_spectrum, Field):
             raise TypeError("Field object expected")
         if len(power_spectrum.domain) != 1: