# **SigmaEpsilon** - High-Performance Computational Solid Mechanics in Python
[](https://mybinder.org/v2/gh/dewloosh/SigmaEpsilon/main?labpath=notebooksnotebooks%2Flpp.ipynb?urlpath=lab)
[](https://circleci.com/gh/dewloosh/SigmaEpsilon)
[](https://sigmaepsilon.readthedocs.io/en/latest/?badge=latest)
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/sigmaepsilon)
[](https://github.com/psf/black)
> **Warning**
> This package is under active development and in an **alpha stage**. Come back later, or star the repo to make sure you don’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 at http://www.gnu.org/licenses/gpl.txt or in the LICENSE file of this repository 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.
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"description": "# **SigmaEpsilon** - High-Performance Computational Solid Mechanics in Python\n\n[](https://mybinder.org/v2/gh/dewloosh/SigmaEpsilon/main?labpath=notebooksnotebooks%2Flpp.ipynb?urlpath=lab)\n[](https://circleci.com/gh/dewloosh/SigmaEpsilon) \n[](https://sigmaepsilon.readthedocs.io/en/latest/?badge=latest) \n[](https://opensource.org/licenses/MIT)\n[](https://pypi.org/project/sigmaepsilon)\n[](https://github.com/psf/black)\n\n> **Warning**\n> This package is under active development and in an **alpha stage**. Come back later, or star the repo to make sure you don\u2019t miss the first stable release!\n\n## Highlights\n\nHead 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.\n\n### Overview\n\n* 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.\n\n## Installation\n\nThis 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\n\n```console\n>>> python -m venv venv_name\n```\n\nOnce the enviroment is created, activate it via typing\n\n```console\n>>> .\\venv_name\\Scripts\\activate\n```\n\n`sigmaepsilon` can be installed (either in a virtual enviroment or globally) from PyPI using `pip` on Python >= 3.6:\n\n```console\n>>> pip install sigmaepsilon\n```\n\n## **Documentation**\n\nRefer to the [docs](https://sigmaepsilon.readthedocs.io/en/latest/) for further details on installation and usage.\n\n## **Testing**\n\nTo run all tests, open up a console in the root directory of the project and type the following\n\n```console\n>>> python -m unittest\n```\n\n## **Dependencies**\n\nWe 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\".\n\nmust have\n\n* `Numba`, `NumPy`, `SciPy`, `SymPy`, `awkward`\n\nstrongly suggested\n\n* `PyVista`, `Plotly`, `matplotlib`, `sectionproperties`\n\noptional\n\n* `networkx`\n\n## **License**\n\nSigmaEpsilon is Copyright(C) 2022: Bence Balogh\n\nAll rights reserved.\n\nThis program is dual-licensed as follows:\n\n(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.\n\nIn 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 at http://www.gnu.org/licenses/gpl.txt or in the LICENSE file of this repository for more details.\n\n(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.\n",
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