Metadata-Version: 2.1
Name: sigmaepsilon
Version: 0.0.1rc0
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/0.0.1c.zip
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
Requires-Dist: setuptools
Requires-Dist: wheel
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: numba
Requires-Dist: sympy
Requires-Dist: awkward
Requires-Dist: sectionproperties
Requires-Dist: linkeddeepdict
Requires-Dist: dewloosh.core
Requires-Dist: neumann (>=0.0.1)
Requires-Dist: polymesh (>=0.0.4)

# **SigmaEpsilon** - High-Performance Computational Mechanics in Python

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> **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**

must have 
  * `Numba`, `NumPy`, `SciPy`, `SymPy`, `awkward`

stringly suggested
  * `PyVista`, `Plotly`, `matplotlib`, `sectionproperties`

optional 
  * `networkx`

## **License**

This package is licensed under the MIT license.

