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add import_result_file.rst tutorial
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‎doc/source/user_guide/tutorials/import_data/import_result_file.rst‎

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.. _ref_tutorials_import_result_file:
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=========================
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Import result file in DPF
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=========================
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.. |Model| replace:: :class:`Model <ansys.dpf.core.model.Model>`
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.. |DataSources| replace:: :class:`DataSources <ansys.dpf.core.data_sources.DataSources>`
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.. |Examples| replace:: :mod:`Examples<ansys.dpf.core.examples>`
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.. |set_result_file_path| replace:: :func:`set_result_file_path() <ansys.dpf.core.data_sources.DataSources.set_result_file_path>`
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.. |add_file_path| replace:: :func:`add_file_path() <ansys.dpf.core.data_sources.DataSources.add_file_path>`
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This tutorial shows how to import a result file in DPF.
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You have two approaches to import a result file in DPF:
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- Using the |DataSources| object
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- Using the |Model| object
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.. note::
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The |Model| extracts a large amount of information by default (results, mesh and analysis data).
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If using this helper takes a long time for processing the code, mind using a |DataSources| object
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and instantiating operators directly with it. Check the ":ref:`get_mesh_mesh_provider`" for more
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information on how to get a mesh from a result file.
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Define the result file path
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---------------------------
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Both approaches need a file path to be defined. Here we will download result files available in
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our |Examples| package.
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.. tab-set::
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.. tab-item:: MAPDL
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.. jupyter-execute::
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# Import the ``ansys.dpf.core`` module, including examples files and the operators subpackage
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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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from ansys.dpf.core import operators as ops
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# Define the .rst result file
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result_file_path_11 = examples.find_static_rst()
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# Define the modal superposition harmonic analysis (.mode, .rfrq and .rst) result files
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result_file_path_12 = examples.download_msup_files_to_dict()
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print("1:", "\n",result_file_path_11, "\n")
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print("2:", "\n",result_file_path_12, "\n")
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.. tab-item:: LSDYNA
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.. jupyter-execute::
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# Import the ``ansys.dpf.core`` module, including examples files and the operators subpackage
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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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from ansys.dpf.core import operators as ops
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# Define the .d3plot result file
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result_file_path_21 = examples.download_d3plot_beam()
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# Define the .binout result file
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result_file_path_22 = examples.download_binout_matsum()
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print("1:", "\n",result_file_path_21, "\n")
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print("2:", "\n",result_file_path_22, "\n")
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.. tab-item:: Fluent
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.. jupyter-execute::
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# Import the ``ansys.dpf.core`` module, including examples files and the operators subpackage
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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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from ansys.dpf.core import operators as ops
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# Define the project .flprj result file
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result_file_path_31 = examples.download_fluent_axial_comp()["flprj"]
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# Define the CFF .cas.h5/.dat.h5 result files
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result_file_path_32 = examples.download_fluent_axial_comp()
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print("1:", "\n",result_file_path_31, "\n")
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print("2:", "\n",result_file_path_32, "\n")
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.. tab-item:: CFX
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.. jupyter-execute::
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# Import the ``ansys.dpf.core`` module, including examples files and the operators subpackage
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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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from ansys.dpf.core import operators as ops
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# Define the project .res result file
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result_file_path_41 = examples.download_cfx_mixing_elbow()
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# Define the CFF .cas.cff/.dat.cff result files
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result_file_path_42 = examples.download_cfx_heating_coil()
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print("1:", "\n",result_file_path_41, "\n")
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print("2:", "\n",result_file_path_42, "\n")
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Use a |DataSources|
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-------------------
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The |DataSources| object manages paths to their files. Use this object to declare data
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inputs for DPF operators and define their locations.
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.. tab-set::
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.. tab-item:: MAPDL
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**a) `.rst` result file**
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You create the |DataSources| object by defining the the path to the main result file.
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.. jupyter-execute::
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# Create the DataSources object
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my_data_sources_11 = dpf.DataSources(result_path=result_file_path_11)
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**b) `.mode`, `.rfrq` and `.rst` result files**
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In the modal superposition, modal coefficients are multiplied by mode shapes (of a previous modal analysis)
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to analyse a structure under given boundary conditions in a range of frequencies. Doing this expansion “on demand”
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in DPF instead of in the solver reduces the size of the result files.
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The expansion is recursive in DPF: first the modal response is read. Then, “upstream” mode shapes are found in
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the data sources, where they are read and expanded.
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To create a recursive workflow you have to add the upstream data to the main |DataSources| object. Upstream refers
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to a source that provides data to a particular process.
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.. jupyter-execute::
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# Create the DataSources object
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my_data_sources_12 = dpf.DataSources()
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# Define the main result data
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my_data_sources_12.set_result_file_path(filepath=result_file_path_12["rfrq"], key='rfrq')
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# Create the upstream DataSources object with the main upstream data
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up_stream_ds_12 = dpf.DataSources(result_path=result_file_path_12["mode"])
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# Add the additional upstream data to the upstream DataSources object
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up_stream_ds_12.add_file_path(filepath=result_file_path_12["rst"])
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# Add the upstream DataSources to the main DataSources object
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my_data_sources_12.add_upstream(upstream_data_sources=up_stream_ds_12)
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.. tab-item:: LSDYNA
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**a) `.d3plot` result file**
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This LS-DYNA d3plot file contains several individual results, each at different times.
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The d3plot file does not contain information related to Units. In this case, as the
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simulation was run through Mechanical, a ``file.actunits`` file is produced. If this
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file is supplemented in the |DataSources|, the units will be correctly fetched for all
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results in the file as well as for the mesh.
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.. jupyter-execute::
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# Create the DataSources object
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my_data_sources_21 = dpf.DataSources()
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my_data_sources_21.set_result_file_path(filepath=result_file_path_21[0], key="d3plot")
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my_data_sources_21.add_file_path(filepath=result_file_path_21[3], key="actunits")
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**b) `.binout` result file**
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The extension key ``.binout`` is not specified in the result file. Thus, we use the
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|set_result_file_path| method to correctly implement the result file to the |DataSources| by giving
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explicitly the extension key as an argument.
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.. jupyter-execute::
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# Create the DataSources object
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my_data_sources_22 = dpf.DataSources()
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# Define the the path to the main result
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my_data_sources_22.set_result_file_path(filepath=result_file_path_22, key="binout")
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.. tab-item:: Fluent
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**a) `.flprj` result file**
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You create the |DataSources| object by defining the the path to the main result file.
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.. jupyter-execute::
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# Create the DataSources object
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my_data_sources_31 = dpf.DataSources(result_path=result_file_path_31)
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**b) `.cas.h5`, `.dat.h5` result files**
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Here we have a main and an additional result files. Thus, we use the
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|set_result_file_path| method, to correctly implement the result file to the |DataSources| by giving
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explicitly the first extension key as an argument, and the |add_file_path| method, to add the additional
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result file.
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.. jupyter-execute::
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# Create the DataSources object
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my_data_sources_32 = dpf.DataSources()
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# Define the path to the main result file
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my_data_sources_32.set_result_file_path(filepath=result_file_path_32['cas'][0], key="cas")
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# Add the additional result file to the DataSources
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my_data_sources_32.add_file_path(filepath=result_file_path_32['dat'][0], key="dat")
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.. tab-item:: CFX
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**a) `.res` result file**
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You create the |DataSources| object by defining the the path to the main result file.
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.. jupyter-execute::
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# Create the DataSources object
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my_data_sources_41 = dpf.DataSources(result_path=result_file_path_41)
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**b) `.cas.cff`, `.dat.cff` result files**
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Here we have a main and an additional result files. Thus, we use the
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|set_result_file_path| method, to correctly implement the result file to the |DataSources| by giving
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explicitly the first extension key as an argument, and the |add_file_path| method, to add the additional
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result file.
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.. jupyter-execute::
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# Create the DataSources object
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my_data_sources_42 = dpf.DataSources()
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# Define the path to the main result file
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my_data_sources_42.set_result_file_path(filepath=result_file_path_42["cas"], key="cas")
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# Add the additional result file to the DataSources
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my_data_sources_42.add_file_path(filepath=result_file_path_42["dat"], key="dat")
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Use a |Model|
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-------------
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The |Model| is a helper designed to give shortcuts to the user to access the analysis results
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metadata, by opening a DataSources or a Streams, and to instanciate results provider for it.
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To create a |Model| you can provide the result file path, in the case you are working with a single result
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file with an explicit extension key, or a |DataSources| as an argument.
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.. tab-set::
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.. tab-item:: MAPDL
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**a) `.rst` result file**
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.. jupyter-execute::
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# Create the model with the result file path
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my_model_11 = dpf.Model(data_sources=result_file_path_11)
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# Create the model with the DataSources
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my_model_12 = dpf.Model(data_sources=my_data_sources_11)
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**b) `.mode`, `.rfrq` and `.rst` result files**
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.. jupyter-execute::
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# Create the model with the DataSources
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my_model_13 = dpf.Model(data_sources=my_data_sources_12)
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.. tab-item:: LSDYNA
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**a) `.d3plot` result file**
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.. jupyter-execute::
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# Create the model with the DataSources
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my_model_21 = dpf.Model(data_sources=my_data_sources_21)
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**b) `.binout` result file**
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.. jupyter-execute::
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# Create the model with the DataSources
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my_model_22 = dpf.Model(data_sources=my_data_sources_22)
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.. tab-item:: Fluent
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**a) `.flprj` result file**
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.. jupyter-execute::
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# Create the model with the result file path
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my_model_31 = dpf.Model(data_sources=result_file_path_31)
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# Create the model with the DataSources
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my_model_32 = dpf.Model(data_sources=my_data_sources_31)
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**b) `.cas.h5`, `.dat.h5` result files**
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.. jupyter-execute::
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# Create the model with the DataSources
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my_model_33 = dpf.Model(data_sources=my_data_sources_32)
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.. tab-item:: CFX
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.. jupyter-execute::
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**a) `.res` result file**
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.. jupyter-execute::
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# Create the model with the result file path
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my_model_41 = dpf.Model(data_sources=result_file_path_41)
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# Create the model with the DataSources
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my_model_42 = dpf.Model(data_sources=my_data_sources_41)
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**b) `.cas.cff`, `.dat.cff` result files**
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.. jupyter-execute::
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# Create the model with the DataSources
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my_model_43 = dpf.Model(data_sources=my_data_sources_42)
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