`Scoping`#

class ansys.dpf.core.scoping.Scoping(scoping: ansys.grpc.dpf.scoping_pb2.Scoping | ctypes.c_void_p = None, server: ansys.dpf.core.server_types.AnyServerType = None, ids: IdVectorType = None, location: ansys.dpf.core.common.locations = None)#

Represents a scoping, which is a subset of a model support.

Parameters:

scoping – gRPC message or pointer for an existing scoping on the server.
server – Server with channel connected to the remote or local instance. The default is None, in which case an attempt is made to use the global server.
ids – List of entity IDs.
location – Location for this scoping. Defines the type of entities the IDs correspond to. For example, if location is locations.nodal, then the scoping is a list of node IDs.

Examples

Create a scoping for mesh entities.

>>> from ansys.dpf import core as dpf
>>> # 1. using the mesh_scoping_factory
>>> from ansys.dpf.core import mesh_scoping_factory
>>> # a. scoping with elemental location that targets the elements with id 2, 7 and 11
>>> my_elemental_scoping = mesh_scoping_factory.elemental_scoping([2, 7, 11])
>>> # b. scoping with nodal location that targets the elements with id 4 to 6
>>> my_nodal_scoping = mesh_scoping_factory.nodal_scoping(range(4, 7))
>>> #2. using the Scoping class directly
>>> # a. scoping with elemental location that targets the elements with id 2, 7 and 11
>>> my_elemental_scoping = dpf.Scoping(location=dpf.locations.elemental, ids=[2, 7, 11])
>>> # b. scoping with nodal  location that targets the elements with id 4 to 6
>>> my_nodal_scoping = dpf.Scoping(ids=range(4, 7))
>>> # 3. create a time_freq scoping that targets the second load step
>>> from ansys.dpf.core import time_freq_scoping_factory
>>> # a. using the time_freq_scoping_factory
>>> my_load_step_scoping = time_freq_scoping_factory.scoping_by_load_step(2)
>>> # b. using the Scoping class directly
>>> my_load_step_scoping = dpf.Scoping(location=dpf.locations.time_freq_step, ids=[2])

Overview#

Methods

`get_ids`	Return the list of entity IDs in the scoping as a list or as a numpy.ndarray.
`set_id`	Set the ID of the entity at index in the scoping.
`id`	Retrieve the entity ID at a given index.
`index`	Retrieve the index for a given entity ID.
`deep_copy`	Create a deep copy of the scoping’s data on a given server.
`as_local_scoping`	Create a deep copy of the scoping that can be accessed and modified locally.
`plot`	Plot the entities of the mesh corresponding to the scoping.

Properties

`ids`	Retrieve the entity IDs in the scoping.
`location`	Location of the scoping as a string.
`size`	Length of the list of IDs.

Special methods

`__len__`	Return the number of scoping ids.
`__del__`	Clean up resources associated with the instance.
`__iter__`	Return an iterator over the scoping ids.
`__getitem__`	Retrieve the ID at a requested index.
`__setitem__`	Set the ID at a given index.
`__str__`	Describe the entity.

Import detail#

from ansys.dpf.core.scoping import Scoping

Property detail#

property Scoping.ids: ansys.dpf.gate.dpf_array.DPFArray | list[int]#

Retrieve the entity IDs in the scoping.

Returns:: List of IDs in the scoping. By default, a mutable DPFArray is returned. To change the return type to a list for the complete python session, see ansys.dpf.core.settings.get_runtime_client_config() and ansys.dpf.core.runtime_config.RuntimeClientConfig.return_arrays(). To get the list of IDs from a scoping as a Python list without changing a default configuration, use ansys.dpf.core.scoping.Scoping.get_ids() instead.
Return type:: ids

property Scoping.location: str#

Location of the scoping as a string.

This defines the type of entity the IDs correspond to (such as node ID, element ID, face ID, and so on).

Returns:: The location of the scoping. One of the values of locations.
Return type:: location

property Scoping.size: int#

Length of the list of IDs.

Returns:: Size of the scoping.
Return type:: size

Method detail#

Scoping.get_ids(np_array: bool = False) → list[int] | ansys.dpf.gate.dpf_array.DPFArray#

Return the list of entity IDs in the scoping as a list or as a numpy.ndarray.

Parameters:: np_array – Whether to return the list of IDs as a numpy array.
Returns:: List of entity IDs in the scoping.
Return type:: ids

Scoping.set_id(index: int, scopingid: int)#

Set the ID of the entity at index in the scoping.

Parameters:

index – Index in the scoping.
scopingid – ID of the entity.

Scoping.id(index: int) → int#

Retrieve the entity ID at a given index.

Parameters:: index – Index of the entity in the scoping.
Returns:: ID of the entity at index in the scoping.
Return type:: entity_id

Scoping.index(id: int) → int#

Retrieve the index for a given entity ID.

Parameters:: id – Entity ID at the index in the scoping.
Returns:: Index in the scoping for the entity ID.
Return type:: index

Scoping.__len__()#

Return the number of scoping ids.

Returns:: The number of scoping ids.
Return type:: int

Scoping.__del__()#

Clean up resources associated with the instance.

This method calls the deleter function to release resources. If an exception occurs during deletion, a warning is issued.

Raises:: Warning – If an exception occurs while attempting to delete resources.

Scoping.__iter__()#: Return an iterator over the scoping ids.

Scoping.__getitem__(key)#: Retrieve the ID at a requested index.

Scoping.__setitem__(index, id)#: Set the ID at a given index.

Scoping.__str__()#

Describe the entity.

Returns:: description
Return type:: str

Scoping.deep_copy(server=None)#

Create a deep copy of the scoping’s data on a given server.

This method is useful for passiong data from one server instance to another.

Parameters:: server (ansys.dpf.core.server, optional) – Server with the channel connected to the remote or local instance. The default is None, in which case an attempt is made to use the global server.
Returns:: scoping_copy
Return type:: Scoping

Scoping.as_local_scoping()#

Create a deep copy of the scoping that can be accessed and modified locally.

This method allows you to access and modify the local copy of the scoping without sending a request to the server. It should be used in a with statement so that the local field is released and the data is sent to the server in one action. If it is not used in a with statement, Scoping.release_data() should be used to update the scoping.

Warning

If this as_local_scoping method is not used as a context manager in a with statement or if the method release_data() is not called, the data will not be updated.

Returns:: local_scoping
Return type:: Scoping

Examples

>>> from ansys.dpf import core as dpf
>>> num_entities = 3
>>> scoping_to_local = dpf.Scoping()
>>> with scoping_to_local.as_local_scoping() as scoping:
...     for i in range(0,num_entities):
...         scoping[i] = i+1

Scoping.plot(mesh, show_mesh: bool = False, **kwargs)#

Plot the entities of the mesh corresponding to the scoping.

Parameters:

mesh – Mesh to use to translate the scoping into mesh entities.
show_mesh – Whether to also show the mesh with low opacity.
**kwargs (optional) – Additional keyword arguments for the plotter. More information are available at pyvista.plot().

Returns:

Returns what the pyvista.show() method returns based on arguments.

Return type:

(cpos, image)

Examples

>>> from ansys.dpf import core as dpf
>>> from ansys.dpf.core import examples
>>> model = dpf.Model(examples.download_cfx_mixing_elbow())
>>> mesh = model.metadata.meshed_region
>>> node_scoping = dpf.Scoping(
...    location=dpf.locations.nodal,
...    ids=mesh.nodes.scoping.ids[0:100]
...)
>>> node_scoping.plot(mesh=mesh, color="red")
>>> element_scoping = dpf.Scoping(
...    location=dpf.locations.elemental,
...    ids=mesh.elements.scoping.ids[0:100]
...)
>>> element_scoping.plot(mesh=mesh, color="green")

Scoping#

Overview#

Import detail#

Property detail#

Method detail#

`Scoping`#