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Add cheat sheet links in PyDPF-Core doc (#468)
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* Add cheat sheet links and standard format with PyDPF-Core doc

* Use same title for licensing info

* Apply suggestions from code review

Make corrections from Revathy's review

Co-authored-by: Revathy Venugopal <[email protected]>

* Fix spacing of list item in README.md

---------

Co-authored-by: Revathy Venugopal <[email protected]>
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73 changes: 48 additions & 25 deletions README.md
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[![pypi](https://badge.fury.io/py/ansys-dpf-post.svg?logo=python&logoColor=white)](https://pypi.org/project/ansys-dpf-post)
[![MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

The Ansys Data Processing Framework (DPF) is designed to provide numerical
simulation users and engineers with a toolbox for accessing and
transforming simulation data.
Ansys Data Processing Framework (DPF) provides numerical simulation
users and engineers with a toolbox for accessing and transforming simulation
data. With DPF, you can perform complex preprocessing or postprocessing of
large amounts of simulation data within a simulation workflow.

The Python `ansys-dpf-post` package provides a high-level, physics-oriented API for postprocessing.
Loading a simulation (defined by its result files) allows you to extract simulation metadata as well
as results and then apply postprocessing operations on it.
The Python `ansys-dpf-post` package provides a high-level, physics-oriented
API for postprocessing. Loading a simulation (defined by its results files)
allows you to extract simulation metadata and results and then apply
postprocessing operations on them.

The latest version of DPF supports Ansys solver result files for:
The latest version of DPF supports Ansys solver results files for:

- MAPDL (`.rst`, `.mode`, `.rfrq`, `.rdsp`)
- Mechanical APDL (`.rst`, `.mode`, `.rfrq`, `.rdsp`)
- LS-DYNA (`.d3plot`, `.binout`)
- Fluent (`.cas/dat.h5`, `.flprj`)
- CFX (`.cad/dat.cff`, `.flprj`)

See the `PyDPF-Core main page <https://dpf.docs.pyansys.com/version/stable/index.html>`_
for more information on compatibility.
For more information on file support, see the [main page](https://dpf.docs.pyansys.com/version/stable/index.html)
in the PyDPF-Core documentation.

This module leverages the PyDPF-Core project's ``ansys-dpf-core`` package, which is
PyDPF-Post leverages the PyDPF-Core project's ``ansys-dpf-core`` package, which is
available at [PyDPF-Core GitHub](https://github.com/ansys/pydpf-core).
Use the ``ansys-dpf-core`` package for building more advanced and customized workflows
using Ansys DPF.

## Documentation
## Documentation and issues

For comprehensive information on this package, see the [PyDPF-Post Documentation](https://post.docs.pyansys.com),
For detailed how-to information, see the [Examples](https://post.docs.pyansys.com/version/stable/examples/index.html)
in the PyDPF-Post documentation.
Documentation for the latest stable release of PyPDF-Post is hosted at
[PyDPF-Post documentation](https://post.docs.pyansys.com/version/stable/).

In the upper right corner of the documentation's title bar, there is an option for switching from
viewing the documentation for the latest stable release to viewing the documentation for the
development version or previously released versions.

You can also [view](https://cheatsheets.docs.pyansys.com/pydpf-post_cheat_sheet.png) or
[download](https://cheatsheets.docs.pyansys.com/pydpf-post_cheat_sheet.pdf) the
PyDPF-Post cheat sheet. This one-page reference provides syntax rules and commands
for using PyDPF-Post.

On the [PyDPF-Post Issues](https://github.com/ansys/pydpf-post/issues) page,
you can create issues to report bugs and request new features. On the
[PyDPF-Post Discussions](https://github.com/ansys/pydpf-post/discussions) page or the [Discussions](https://discuss.ansys.com/)
page on the Ansys Developer portal, you can post questions, share ideas, and get community feedback.

To reach the project support team, email [[email protected]](mailto:[email protected]).

## Installation

To install this package, use this command:
To install this package, run this command:

```
pip install ansys-dpf-post
```

You can also clone and install this package with this code:
You can also clone and install this package with these commands:

```
git clone https://github.com/ansys/pydpf-post
Expand All @@ -51,10 +68,11 @@ pip install . --user

## Brief demo

Provided you have Ansys 2023 R1 or later installed, a DPF server starts
automatically once you start using PyDPF-Post.
Provided you have Ansys 2023 R1 or later installed, a DPF server automatically starts
once you start using PyDPF-Post.

To load a simulation to extract and post-process results, use this code:
To load a simulation for a MAPDL result file to extract and
postprocess results, use this code:

```pycon
>>> from ansys.dpf import post
Expand Down Expand Up @@ -85,8 +103,8 @@ To load a simulation to extract and post-process results, use this code:
```
![Example Stress plot Crankshaft](https://github.com/ansys/pydpf-post/raw/master/docs/source/images/crankshaft_stress.png)

To run PyDPF-Post with Ansys versions 2021 R1 and 2022 R2, use this code to
start the legacy PyDPF-Post tools::
To run PyDPF-Post with Ansys 2021 R1 through 2022 R2, use this code to
start the legacy PyDPF-Post tools:

```pycon
>>> from ansys.dpf import post
Expand All @@ -97,7 +115,12 @@ start the legacy PyDPF-Post tools::
```
![Example Stress plot Crankshaft](https://github.com/ansys/pydpf-post/raw/master/docs/source/images/crankshaft_stress.png)

## License
## License and acknowledgements

PyDPF-Post is licensed under the MIT license. For more information, see the
[LICENSE](https://github.com/ansys/pydpf-post/raw/master/LICENSE) file.

``PyDPF-Post`` is licensed under the MIT license. For more information, see the
[LICENSE](https://github.com/ansys/pydpf-post/raw/master/LICENSE).
PyDPF-Post makes no commercial claim over Ansys whatsoever. This library
extends the functionality of Ansys DPF by adding a Python interface
to DPF without changing the core behavior or license of the original
software.
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PyDPF-Post
==========

The Ansys Data Processing Framework (DPF) is designed to provide
numerical simulation users and engineers with a toolbox for accessing
and transforming simulation data.
Ansys Data Processing Framework (DPF) provides numerical simulation
users and engineers with a toolbox for accessing and transforming simulation
data. With DPF, you can perform complex preprocessing or postprocessing of
large amounts of simulation data within a simulation workflow.

The Python `ansys-dpf-post` package provides a high-level, physics-oriented API
for postprocessing. Loading a simulation (defined by its result files) allows you
to extract simulation metadata as well as results and then apply postprocessing
operations on it.
The Python `ansys-dpf-post` package provides a high-level, physics-oriented
API for postprocessing. Loading a simulation (defined by its results files)
allows you to extract simulation metadata and results and then apply
postprocessing operations on them.

The latest version of DPF supports Ansys solver result files for:
The latest version of DPF supports Ansys solver results files for:

- MAPDL (``.rst``, ``.mode``, ``.rfrq``, ``.rdsp``)
- LS-DYNA (``.d3plot``, ``.binout``)
- Fluent (``.cas/dat.h5``, ``.flprj``)
- CFX (``.cad/dat.cff``, ``.flprj``)
- Mechanical APDL (`.rst`, `.mode`, `.rfrq`, `.rdsp`)
- LS-DYNA (`.d3plot`, `.binout`)
- Fluent (`.cas/dat.h5`, `.flprj`)
- CFX (`.cad/dat.cff`, `.flprj`)

See the `PyDPF-Core main page <https://dpf.docs.pyansys.com/version/stable/index.html>`_
for more information on file support.
For more information on file support, see the `main page <https://dpf.docs.pyansys.com/version/stable/index.html>`_
in the PDF-Core documentation.

This module leverages the PyDPF-Core project's ``ansys-dpf-core`` package, which is
PyDPF-Post leverages the PyDPF-Core project's ``ansys-dpf-core`` package, which is
available at `PyDPF-Core GitHub <https://github.com/ansys/pydpf-core>`_.
Use the ``ansys-dpf-core`` package for building more advanced and customized
workflows using DPF.


Brief demo
~~~~~~~~~~

Provided you have Ansys 2023 R1 installed, a DPF server starts
automatically once you start using PyDPF-Post.
Provided you have Ansys 2023 R1 or later installed, a DPF server automatically starts
once you start using PyDPF-Post.

To load a simulation for a MAPDL result file to extract and
postprocess results, use this code:
Expand Down Expand Up @@ -76,7 +76,7 @@ postprocess results, use this code:
.. figure:: ./images/crankshaft_stress.png
:width: 300pt

To run PyDPF-Post with Ansys versions 2021 R1 and 2022 R2, use this code to
To run PyDPF-Post with Ansys 2021 R1 through 2022 R2, use this code to
start the legacy PyDPF-Post tools:

.. code:: python
Expand All @@ -91,7 +91,7 @@ start the legacy PyDPF-Post tools:
:width: 300pt


For comprehensive how-to information, see :ref:`gallery`.
For comprehensive examples of how you use PyDPF-Post, see :ref:`gallery`.


Key features
Expand All @@ -105,14 +105,34 @@ because they are written in C and FORTRAN. Because PyDPF-Post presents results
in a Pythonic manner, you can rapidly develop simple or complex postprocessing
scripts.


**Easy to use**

The PyDPF-Post API automates the use of chained DPF operators to make
postprocessing easier. The PyDPF-Post documentation describes how you can
use operators to compute results. This allows you to build your own custom,
low-level scripts to enable fast postprocessing of potentially multi-gigabyte
models using `PyDPF-Core <https://github.com/ansys/pydpf-core>`_.
low-level scripts to enable fast postprocessing of potentially multi-gigabyte models
using `PyDPF-Core <https://github.com/ansys/pydpf-core>`_.

Documentation and issues
------------------------
Documentation for the latest stable release of PyDPF-Post is hosted at `PyDPF-Post documentation
<https://post.docs.pyansys.com/version/stable/>`_.

In the upper right corner of the documentation's title bar, there is an option for switching from
viewing the documentation for the latest stable release to viewing the documentation for the
development version or previously released versions.

You can also `view <https://cheatsheets.docs.pyansys.com/pydpf-post_cheat_sheet.png>`_ or
`download <https://cheatsheets.docs.pyansys.com/pydpf-post_cheat_sheet.pdf>`_ the
PyDPF-Post cheat sheet. This one-page reference provides syntax rules and commands
for using PyDPF-Post.

On the `PyDPF-Post Issues <https://github.com/ansys/pydpf-post/issues>`_ page,
you can create issues to report bugs and request new features. On the `PyDPF-Post Discussions
<https://github.com/ansys/pydpf-post/discussions>`_ page or the `Discussions <https://discuss.ansys.com/>`_
page on the Ansys Developer portal, you can post questions, share ideas, and get community feedback.

To reach the project support team, email `[email protected] <[email protected]>`_.


.. toctree::
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