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Cleanup Readme (#18)
* gitignore updated * user_guide files added * speed -> velocity * solution doc string fix * old user guide index file removed * API files added * doc string stress update * user guide and API references added * fix for get result info * fixes for generation with sphinx * rth files added in user guide * accesing_result .rst file update * misc_results file updated * _plot_chart removed (need to be discussed) * examples order changed * thermal analysis and mechanics analysis * extensions removed * suggested editorial improvements * suggested editorial improvements * suggested editorial improvements (API & index.rst) * cleaned up code docgen * touch log file to avoid tail on non-existing file * added readme and cleaned up example titles * fix setup.py Co-authored-by: lparadis <[email protected]> Co-authored-by: jazztekk <[email protected]>
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‎LICENSE‎

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The MIT License
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Copyright (c) 2020 ANSYS
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Copyright (c) 2021 ANSYS
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎README.md‎

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# DPF-Post
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ANSYS Data Processing Framework: post processing package.
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# DPF-Post - Ansys Data Post-Processing Framework
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## Disclaimer
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[![PyPI version](https://badge.fury.io/py/ansys-dpf-post.svg)](https://badge.fury.io/py/ansys-dpf-post)
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This API is currently a work in progress - things will break and change!
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[![Build Status](https://dev.azure.com/pyansys/pyansys/_apis/build/status/pyansys.DPF-Post?branchName=master)](https://dev.azure.com/pyansys/pyansys/_build/latest?definitionId=6&branchName=master)
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The Data Processing Framework (DPF) is designed to provide numerical
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simulation users/engineers with a toolbox for accessing and
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transforming simulation data. DPF can access data from solver result
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files as well as several neutral formats (csv, hdf5, vtk,
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etc.). Various operators are available allowing the manipulation and
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the transformation of this data.
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## Get Started
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The Python `ansys.dpf.post` package provides an simplified Python
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interface to DPF, thus enabling rapid post-processing, without ever
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leaving a Python environment.
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Clone or copy this directory and then install using:
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This module leverages the DPF-Core project's ``ansys.dpf.core``
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package and can be found by visiting [DPF-Core GitHub](https://github.com/pyansys/DPF-Core). Use ``ansys.dpf.core``
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for building more advanced and customized workflows using Ansys's DPF.
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Visit the [DPF-Post Documentation](https://postdocs.pyansys.com) for a
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detailed description of the package, or see the [Examples
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Gallery](https://postdocs.pyansys.com/examples/index.html) for more
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detailed examples.
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### Installation
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Install this repository with:
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```
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pip install ansys-dpf-post
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```
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You can also clone and install this repository with:
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```
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git clone https://github.com/pyansys/DPF-Post
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cd DPF-Post
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pip install . --user
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```
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See the example scripts in the examples folder for some basic example. More will be added later.
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### Running DPF-Post
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Provided you have ANSYS 2021R1 installed, a DPF server will start
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automatically once you start using DPF-Post. Should you wish to use
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DPF-Post without 2020R1, see the [DPF Docker](https://dpfdocs.pyansys.com/getting_started/docker.html) documentation.
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Opening and plotting a result file generated from Ansys workbench or
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MAPDL is as easy as:
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```python
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>>> from ansys.dpf import post
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>>> from ansys.dpf.post import examples
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>>> solution = post.load_solution(examples.multishells_rst)
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>>> stress = solution.stress()
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>>> stress.xx.plot_contour(show_edges=False)
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```
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![Example Stress Plot](https://github.com/pyansys/dpf-post/raw/master/docs/source/images/main_example.png)
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Or extract the raw data as a `numpy` array with:
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```python
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>>> stress.xx.get_data_at_field(0)
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array([-3.37871094e+10, -4.42471752e+10, -4.13249463e+10, ...,
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3.66408342e+10, 1.40736914e+11, 1.38633557e+11])
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```
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### Key Features
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**Computational Efficiency**
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The DPF-Post module is based on DPF Framework that been developed with
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a data framework that localizes the loading and post-processing within
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the DPF server, enabling rapid post-processing workflows as this is
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written in C and FORTRAN. At the same time, the DPF-Post Python
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module presents the result in Pythonic manner, allowing for the rapid
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development of simple or complex post-processing scripts.
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**Easy to use**
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The API of DPF-Post module has been developed in order to make easy
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post-processing steps easier by automating the use of DPF's chained
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operators. This allows for fast post-processing of potentially
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multi-gigabyte models in a short script. DPF-Post also details the
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usage of the operators used when computing the results so you can also
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build your own custom, low level scripts using the
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[DPF-Core](https://github.com/pyansys/DPF-Core) module.
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### License
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``DPF-Post`` is licensed under the MIT license. Please see the [LICENSE](https://github.com/pyansys/dpf-post/raw/master/LICENSE) for more details.

‎docs/source/index.rst‎

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This module leverages the DPF-Core module ``ansys.dpf.core`` module
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and can be found by visiting `DPF-Core GitHub
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<https://github.com/pyansys/DPF-Post>`_. Use ``ansys.dpf.core`` for
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<https://github.com/pyansys/DPF-Core>`_. Use ``ansys.dpf.core`` for
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building more advanced and customized workflows using Ansys's DPF.
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multi-gigabyte models in a short script. DPF-Post also details the
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usage of the operators used when computing the results so you can also
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build your own custom, low level scripts using the
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`DPF-Core <https://github.com/pyansys/DPF-Post>`_ module.
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`DPF-Core <https://github.com/pyansys/DPF-Core>`_ module.
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.. toctree::

‎examples/00-Overview/README.txt‎

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.. _ref_overview:
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DPF-Post overview
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DPF-Post Overview
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~~~~~~~~~~~~~~~~~
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Here are a quick overview of DPF-Post module with ``ansys-dpf-post``.
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Here are a quick overview of DPF-Post module with ``ansys-dpf-post``.

‎examples/01-Different-analysis-types/README.txt‎

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.. _ref_different_analysis:
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DPF-Post different analysis type
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Here are a series of analysis type focused examples with ``ansys-dpf-post`` module.
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DPF-Post Analysis Types
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~~~~~~~~~~~~~~~~~~~~~~~
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Here are a series of analysis type focused examples with
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``ansys-dpf-post`` module.

‎examples/README.txt‎

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.. _gallery:
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DPF-Post examples
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=================
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Here are a series of examples DPF-Post module with ``ansys-dpf-post``.
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========
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Examples
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========
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Here are a series of examples DPF-Post module with ``ansys-dpf-post``.

‎setup.py‎

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install_requires = ['pyvista>=0.24.0',
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'matplotlib',
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'ansys.dpf.core==0.2.0']
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'ansys.dpf.core>=0.2.0']
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# Get version from version info
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name='ansys-dpf-post',
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packages=['ansys.dpf.post', 'ansys.dpf.post.examples'],
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version=__version__,
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description='DPF Python gRPC client',
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# long_description=io_open(readme_file, encoding="utf-8").read(),
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maintainer='ANSYS',
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maintainer_email='[email protected]',
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description='DPF-Post Python gRPC client',
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url='https://github.com/pyansys/DPF-Post',
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long_description=open('README.md').read(),
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long_description_content_type='text/markdown',
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classifiers=[
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'Development Status :: 4 - Beta',
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'Intended Audience :: Science/Research',
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'Topic :: Scientific/Engineering :: Information Analysis',
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'Operating System :: Microsoft :: Windows',
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'Operating System :: POSIX',
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'Programming Language :: Python :: 3.5',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7',
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'Programming Language :: Python :: 3.8',
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],
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python_requires='>=3.5.*',
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python_requires='>=3.6.*',
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install_requires=install_requires,
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)

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