Metadata-Version: 2.1
Name: sigmaepsilon
Version: 0.0.25b0
Summary: High-Performance Computational Mechanics in Python.
Home-page: https://github.com/dewloosh/sigmaepsilon
Author: Bence Balogh
Author-email: benceeok@gmail.com
License: UNKNOWN
Download-URL: https://github.com/dewloosh/sigmaepsilon/archive/refs/tags/0.0.25b.zip
Description: # **SigmaEpsilon** - High-Performance Computational Solid Mechanics in Python
        
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dewloosh/SigmaEpsilon/main?labpath=notebooksnotebooks%2Flpp.ipynb?urlpath=lab)
        [![CircleCI](https://circleci.com/gh/dewloosh/SigmaEpsilon.svg?style=shield)](https://circleci.com/gh/dewloosh/SigmaEpsilon) 
        [![Documentation Status](https://readthedocs.org/projects/sigmaepsilon/badge/?version=latest)](https://sigmaepsilon.readthedocs.io/en/latest/?badge=latest) 
        [![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        [![PyPI](https://badge.fury.io/py/sigmaepsilon.svg)](https://pypi.org/project/sigmaepsilon) 
        
        > **Warning**
        > This package is under active development and in an **alpha stage**. Come back later, or star the repo to make sure you donâ€™t miss the first stable release!
        
        ## Highlights
        
        Head over to the Quick Examples page in the docs to explore our gallery of examples showcasing what SigmaEpsilon can do! Want to test-drive SigmaEpsilon? All of the examples from the gallery are live on MyBinder for you to test drive without installing anything locally: Launch on Binder.
        
        ### Overview
        
        * A `solid` submodule to analyze and optimize solid structures of all kinds with the **Finite Element Method**. The implementations so far only cover linear behaviour, but with practically no limits on the complexity of the shape and topology of the domain under investigation.
        
        ## **Installation**
        This is optional, but we suggest you to create a dedicated virtual enviroment at all times to avoid conflicts with your other projects. Create a folder, open a command shell in that folder and use the following command
        
        ```console
        >>> python -m venv venv_name
        ```
        
        Once the enviroment is created, activate it via typing
        
        ```console
        >>> .\venv_name\Scripts\activate
        ```
        
        `sigmaepsilon` can be installed (either in a virtual enviroment or globally) from PyPI using `pip` on Python >= 3.6:
        
        ```console
        >>> pip install sigmaepsilon
        ```
        
        ## **Documentation**
        
        Refer to the [docs](https://sigmaepsilon.readthedocs.io/en/latest/) for further details on installation and usage.
        
        ## **Testing**
        
        To run all tests, open up a console in the root directory of the project and type the following
        
        ```console
        >>> python -m unittest
        ```
        
        ## **Dependencies**
        
        We use Numba's JIT compiler to speed up heavy computations, and it relies on the C++ redistributable package. It is likely already installed on your system, but if it is not, you can download it from Microsoft's website under "Other Tools, Frameworks, and Redistributables".
        
        must have 
          * `Numba`, `NumPy`, `SciPy`, `SymPy`, `awkward`
        
        strongly suggested
          * `PyVista`, `Plotly`, `matplotlib`, `sectionproperties`
        
        optional 
          * `networkx`
        
        ## **License**
        
        SigmaEpsilon is Copyright(C) 2022: Bence Balogh
        
        All rights reserved.
        
        This program is dual-licensed as follows:
        
        (1) You may use SigmaEpsilon as free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.
        
        In this case the program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License (http://www.gnu.org/licenses/gpl.txt) for more details.
        
        (2) You may use SigmaEpsilon as part of a commercial software. In this case a proper agreement must be reached with the Authors based on a proper licensing contract.
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6, <3.11
Description-Content-Type: text/markdown
