Metadata-Version: 2.1
Name: ansys-dpf-post
Version: 0.2.1
Summary: DPF-Post Python gRPC client
Home-page: https://github.com/pyansys/pydpf-post
Author: ANSYS
Author-email: ramdane.lagha@ansys.com
Maintainer: ANSYS
Maintainer-email: ramdane.lagha@ansys.com
License: MIT
Description: # DPF-Post - Ansys Data Post-Processing Framework
         
        [![PyPI version](https://badge.fury.io/py/ansys-dpf-post.svg)](https://badge.fury.io/py/ansys-dpf-post)
        
        [![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)
        
        The Data Processing Framework (DPF) is designed to provide numerical
        simulation users/engineers with a toolbox for accessing and
        transforming simulation data. DPF can access data from solver result
        files as well as several neutral formats (csv, hdf5, vtk,
        etc.). Various operators are available allowing the manipulation and
        the transformation of this data.
        
        The Python `ansys.dpf.post` package provides an simplified Python
        interface to DPF, thus enabling rapid post-processing, without ever
        leaving a Python environment. 
        
        This module leverages the DPF-Core project's ``ansys.dpf.core``
        package and can be found by visiting [DPF-Core GitHub](https://github.com/pyansys/DPF-Core).  Use ``ansys.dpf.core``
        for building more advanced and customized workflows using Ansys's DPF.
        
        Visit the [DPF-Post Documentation](https://postdocs.pyansys.com) for a
        detailed description of the package, or see the [Examples
        Gallery](https://postdocs.pyansys.com/examples/index.html) for more
        detailed examples.
        
        
        ### Installation
        
        Install this repository with:
        
        ```
        pip install ansys-dpf-post
        ```
        
        You can also clone and install this repository with:
        
        ```
        git clone https://github.com/pyansys/DPF-Post
        cd DPF-Post
        pip install . --user
        ```
        
        ### Running DPF-Post
        Provided you have ANSYS 2021R1 installed, a DPF server will start
        automatically once you start using DPF-Post.  Should you wish to use
        DPF-Post without 2020R1, see the [DPF Docker](https://dpfdocs.pyansys.com/getting_started/docker.html) documentation.
        
        Opening and plotting a result file generated from Ansys workbench or
        MAPDL is as easy as:
        
        ```python
        >>> from ansys.dpf import post
        >>> from ansys.dpf.post import examples
        >>> solution = post.load_solution(examples.multishells_rst)
        >>> stress = solution.stress()
        >>> stress.xx.plot_contour(show_edges=False)
        ```
        
        ![Example Stress Plot](https://github.com/pyansys/dpf-post/raw/master/docs/source/images/main_example.png)
        
        
        Or extract the raw data as a `numpy` array with:
        
        ```python
        >>> stress.xx.get_data_at_field(0)
        array([-3.37871094e+10, -4.42471752e+10, -4.13249463e+10, ...,
                3.66408342e+10,  1.40736914e+11,  1.38633557e+11])
        ```
        
        ### Key Features
        
        
        **Computational Efficiency**
        
        The DPF-Post module is based on DPF Framework that been developed with
        a data framework that localizes the loading and post-processing within
        the DPF server, enabling rapid post-processing workflows as this is
        written in C and FORTRAN.  At the same time, the DPF-Post Python
        module presents the result in Pythonic manner, allowing for the rapid
        development of simple or complex post-processing scripts.
        
        
        **Easy to use**
        
        The API of DPF-Post module has been developed in order to make easy
        post-processing steps easier by automating the use of DPF's chained
        operators.  This allows for fast post-processing of potentially
        multi-gigabyte models in a short script.  DPF-Post also details the
        usage of the operators used when computing the results so you can also
        build your own custom, low level scripts using the
        [DPF-Core](https://github.com/pyansys/DPF-Core) module.
        
        
        ### License
        
        ``DPF-Post`` is licensed under the MIT license.  Please see the [LICENSE](https://github.com/pyansys/dpf-post/raw/master/LICENSE) for more details.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6.*
Description-Content-Type: text/markdown
