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add new examples to expose fields containers capabilities (#123)
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‎ansys/dpf/core/mesh_scoping_factory.py‎

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"""
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from ansys.dpf.core import Scoping
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from ansys.dpf.core import errors as dpf_errors
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from ansys.dpf.core.common import locations
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@@ -26,8 +25,6 @@ def nodal_scoping(node_ids, server=None):
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-------
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scoping : ansys.dpf.core.Scoping
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"""
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if not isinstance(node_ids, list):
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raise dpf_errors.InvalidTypeError("list", "node_ids")
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scoping = Scoping(server=server, ids=node_ids, location=locations.nodal)
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return scoping
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@@ -48,8 +45,6 @@ def elemental_scoping(element_ids, server=None):
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-------
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scoping : ansys.dpf.core.Scoping
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"""
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if not isinstance(element_ids, list):
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raise dpf_errors.InvalidTypeError("list", "element_ids")
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scoping = Scoping(server=server, ids=element_ids, location=locations.elemental)
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return scoping
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‎examples/00-basic/06-use_result_helpers.py‎ renamed to ‎examples/00-basic/07-use_result_helpers.py‎

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‎examples/00-basic/08-results_over_time_subset.py‎

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"""
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.. _ref_results_over_time:
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Scope results over custom time domains
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The ``Result`` class, which are instances created by the ``Model``, give
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access to helpers for requesting results on specific mesh and time scopings.
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With these helpers, working on a temporal subset of the
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model is straightforward. In this example, different ways to choose the temporal subset to
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evaluate a result are exposed. This example can be extended to frequency subsets.
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Import necessary modules:
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"""
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from ansys.dpf import core as dpf
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from ansys.dpf.core import examples
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###############################################################################
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# Create a model object to establish a connection with an example result file:
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model = dpf.Model(examples.download_transient_result())
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print(model)
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###############################################################################
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# Request specific time sets
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~
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# If specific time sets are of interest, looking into the ``TimeFreqSupport``
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# and connect a given ``time_scoping`` accordingly to the cumulative indexes can be useful.
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print(model.metadata.time_freq_support)
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time_sets = [1, 3, 10]
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disp = model.results.displacement.on_time_scoping(time_sets).eval()
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print(disp)
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# Or using a scoping
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time_sets_scoping = dpf.time_freq_scoping_factory.scoping_by_sets([1, 3, 10])
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disp = model.results.displacement.on_time_scoping(time_sets_scoping).eval()
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print(disp)
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###############################################################################
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# Equivalent to:
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disp_op = model.results.displacement()
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disp_op.inputs.time_scoping(time_sets)
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disp = disp_op.outputs.fields_container()
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###############################################################################
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# Equivalent to:
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disp = model.results.displacement(time_scoping=time_sets_scoping).eval()
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###############################################################################
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# Request specific time steps
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# If specific time steps or load steps are of interest, looking into the
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# ``TimeFreqSupport`` and connect a given ``time_scoping`` located on steps can be done.
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time_steps_scoping = dpf.time_freq_scoping_factory.scoping_by_load_step([1])
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disp = model.results.displacement.on_time_scoping(time_steps_scoping).eval()
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print(disp)
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###############################################################################
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# Equivalent to:
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disp_op = model.results.displacement()
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disp_op.inputs.time_scoping(time_steps_scoping)
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disp = disp_op.outputs.fields_container()
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###############################################################################
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# Using helpers
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# ~~~~~~~~~~~~~
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# Evaluate at all times.
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disp = model.results.displacement.on_all_time_freqs().eval()
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###############################################################################
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# Evaluate at first and last times
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disp = model.results.displacement.on_first_time_freq().eval()
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print(disp)
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disp = model.results.displacement.on_last_time_freq().eval()
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print(disp)

‎examples/00-basic/09-results_over_space_subset.py‎

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"""
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.. _ref_results_over_space:
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Scope results over custom space domains
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The ``Result`` class, which are instances created by the ``Model``, give
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access to helpers for requesting results on specific mesh and time scopings.
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With these helpers, working on a spatial subset of the model is straightforward.
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In this example, different ways to choose the spatial subset to
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evaluate a result are exposed
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Import necessary modules:
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"""
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from ansys.dpf import core as dpf
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from ansys.dpf.core import examples
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###############################################################################
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# Create a model object to establish a connection with an example result file:
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model = dpf.Model(examples.download_all_kinds_of_complexity())
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print(model)
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###############################################################################
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# Choose specific nodes
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# ~~~~~~~~~~~~~~~~~~~~~
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# If some nodes or elements are specifically of interest, a nodal ``mesh_scoping``
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# can be connected.
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nodes_scoping = dpf.mesh_scoping_factory.nodal_scoping(range(400, 500))
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print(nodes_scoping)
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###############################################################################
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# or
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nodes_scoping = dpf.Scoping(ids=range(400, 500), location=dpf.locations.nodal)
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print(nodes_scoping)
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###############################################################################
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disp = model.results.displacement.on_mesh_scoping(nodes_scoping).eval()
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model.metadata.meshed_region.plot(disp)
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###############################################################################
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# Equivalent to:
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disp_op = model.results.displacement()
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disp_op.inputs.mesh_scoping(nodes_scoping)
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disp = disp_op.outputs.fields_container()
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###############################################################################
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# Equivalent to:
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disp = model.results.displacement(mesh_scoping=nodes_scoping).eval()
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###############################################################################
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# Choose specific elements
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# ~~~~~~~~~~~~~~~~~~~~~~~~
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# If some elements are specifically of interest, an elemental ``mesh_scoping``
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# can be connected.
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elements_scoping = dpf.mesh_scoping_factory.elemental_scoping(range(500, 5000))
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print(elements_scoping)
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# or
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elements_scoping = dpf.Scoping(ids=range(500, 5000), location=dpf.locations.elemental)
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print(elements_scoping)
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volume = model.results.elemental_volume.on_mesh_scoping(elements_scoping).eval()
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model.metadata.meshed_region.plot(volume)
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###############################################################################
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# Equivalent to:
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volume_op = model.results.elemental_volume()
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volume_op.inputs.mesh_scoping(elements_scoping)
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volume = volume_op.outputs.fields_container()
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###############################################################################
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# Equivalent to:
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volume = model.results.elemental_volume(mesh_scoping=elements_scoping).eval()
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###############################################################################
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# Choose specific named selections
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# Named selections (also known as components) can be selected to create
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# a spatial domain for a result. A ``mesh_scoping`` can be created with a
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# named selection.
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# To know the available named selections in the result file, use:
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print(model.metadata.available_named_selections)
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###############################################################################
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# Get the ``mesh_scoping`` of a named selection:
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mesh_scoping = model.metadata.named_selection('_CM82')
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print(mesh_scoping)
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###############################################################################
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# Connect this ``mesh_scoping`` to the result provider
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volume = model.results.elemental_volume(mesh_scoping=mesh_scoping).eval()
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model.metadata.meshed_region.plot(volume)
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###############################################################################
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# Equivalent to:
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volume = model.results.elemental_volume.on_named_selection('_CM82')
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###############################################################################
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# Equivalent to:
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ns_provider = dpf.operators.scoping.on_named_selection(
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requested_location=dpf.locations.elemental,
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named_selection_name='_CM82',
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data_sources=model,
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)
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volume = model.results.elemental_volume(mesh_scoping=ns_provider).eval()
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###############################################################################
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# Split results depending on spatial properties
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# For many applications, it can be useful to request results on different subsets
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# of the model. The ``ScopingsContainer`` entity contains different ``Scopings``
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# and can be connected to any result provider to get results split with the
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# same partition as the input ``ScopingsContainer``.
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# For example, some application require to get results split by body, by material,
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# by element types. It might also be necessary to get results by element shape types
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# (shell, solid, beam) to average data properly...
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# Customers might also require split by entirely custom spatial domains.
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###############################################################################
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# Split results by element shapes
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stress = model.results.stress.split_by_shape.on_location(dpf.locations.nodal).eval()
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print(stress)
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shell_stresses = stress.shell_fields()
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model.metadata.meshed_region.plot(shell_stresses[0])
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solid_stresses = stress.solid_fields()
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model.metadata.meshed_region.plot(solid_stresses[0])
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###############################################################################
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# Split results by bodies
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stress = model.results.stress.split_by_body.on_location(dpf.locations.nodal).eval()
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print(stress)
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for body_id in stress.get_mat_scoping().ids:
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field = stress.get_field_by_mat_id(body_id)
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if field.elementary_data_count > 0:
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model.metadata.meshed_region.plot(field)
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###############################################################################
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# Create a custom spatial split
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scopings_container = dpf.ScopingsContainer()
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scopings_container.add_label("custom_split")
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scopings_container.add_scoping(
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{"custom_split": 1},
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dpf.Scoping(ids=range(100, 500), location=dpf.locations.elemental),
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)
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scopings_container.add_scoping(
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{"custom_split": 2},
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dpf.Scoping(ids=range(500, 5000), location=dpf.locations.elemental),
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)
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###############################################################################
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elemental_stress = model.results.stress.on_location(dpf.locations.elemental)(
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mesh_scoping=scopings_container)\
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.eval()
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print(elemental_stress)
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for field in elemental_stress:
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model.metadata.meshed_region.plot(field)

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