# **SigmaEpsilon.Solid.Fourier** - Fourier solutions of some plate and beam bending problems in Python
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[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/sigmaepsilon.solid.fourier)
[](https://www.python.org)
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> **Note**
> Here and there, implementation of the performance critical parts of the library rely on the JIT-compilation capabilities of Numba. This means that the library performs well even for large scale problems, on the expense of a longer first call.
## What is sigmaepsilon.solid.fourier?
The `sigmaepsilon.solid.fourier` library offers semi-analytic solutions to some beam and plate bending problems, where the boundary conditions are a-priori satisfied by careful selection of the approximating functions. Although the calculations only cover a handful of boundary conditions, when they are applicable, they are significantly faster than let say a finite element solution. For this reason, it is very useful for a couple of things:
* experimentation
* verification
* concept validation
* education
* publication
### Highlights
* Semi-analytic, Navier solutions of beam and plate problems.
* Easy to use, high level interface to define various kinds of loads.
* Support for arbitrary loads using Monte-Carlo based coefficient determination.
* Industry-grade performance based on highly parallel, performant code.
* Tight integration with popular Python libraries like NumPy, SciPy, xarray, etc.
* A gallery of examples for plotting with Matplotlib for all types of problems.
* A collection of downloadable Jupyter Notebooks ready for execution covering all available functionality.
* Getting Started, User Guide and API Reference in the documentation.
* The library is intensively tested on CircleCI and has a high coverage level (read more about testing below).
## Documentation
The [documentation](https://sigmaepsilonsolidfourier.readthedocs.io/en/latest/) is built with [Sphinx](https://www.sphinx-doc.org/en/master/) using the [PyData Sphinx Theme](https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html) and hosted on [ReadTheDocs](https://readthedocs.org/). Check it out for the user guide, an ever growing set of examples, and API Reference.
## Installation
sigmaepsilon.solid.fourier can be installed from PyPI using `pip` on Python >= 3.10:
```console
>>> pip install sigmaepsilon.solid.fourier
```
or chechkout with the following command using GitHub CLI
```console
gh repo clone sigma-epsilon/sigmaepsilon.solid.fourier
```
and install from source by typing
```console
>>> pip install .
```
If you want to run the tests, you can install the package along with the necessary optional dependencies like this
```console
>>> pip install ".[test]"
```
If want to execute on the GPU, you need to manually install the necessary requirements. Numba is a direct dependency, so even in this case you have to care about having the prover version of the cuda toolkit installed. For this, you need to know the version of the cuda compute engine, which depends on the version of GPU card you are having.
### Development mode
If you are a developer and want to install the library in development mode, the suggested way is by using this command:
```console
>>> pip install "-e .[test, dev]"
```
### Checking your installation
You should be able to import sigmaepsilon.mesh from the Python prompt:
```console
$ python
Python 3.10.2 (tags/v3.10.2:3d8993a, May 3 2023, 11:48:03) [MSC v.1928 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import sigmaepsilon.solid.fourier
>>> sigmaepsilon.solid.fourier.__version__
'1.0.0'
```
## Testing and coverage
The following command runs all tests and creates a html report in a folder named `htmlcov` (the settings are governed by the `.coveragerc` file):
```console
python -m pytest --cov-report html --cov-config=.coveragerc --cov sigmaepsilon.solid.fourier
```
Open `htmlcov/index.html` to see the results.
## Changes and versioning
See the [changelog](CHANGELOG.md), for the most notable changes between releases.
The project adheres to [semantic versioning](https://semver.org/).
## How to contribute?
Contributions are currently expected in any the following ways:
* finding bugs
If you run into trouble when using the library and you think it is a bug, feel free to raise an issue.
* feedback
All kinds of ideas are welcome. For instance if you feel like something is still shady (after reading the user guide), we want to know. Be gentle though, the development of the library is financially not supported yet.
* feature requests
Tell us what you think is missing (with realistic expectations).
* examples
If you've done something with the library and you think that it would make for a good example, get in touch with the developers and we will happily inlude it in the documention.
* sharing is caring
If you like the library, share it with your friends or colleagues so they can like it too.
In all cases, read the [contributing guidelines](CONTRIBUTING.md) before you do anything.
## Acknowledgements
**A lot of the packages mentioned on this document here and the introduction have a citable research paper. If you use them in your work through sigmaepsilon.mesh, take a moment to check out their documentations and cite their papers.**
Also, funding of these libraries is partly based on the size of the community they are able to support. If what you are doing strongly relies on these libraries, don't forget to press the :star: button to show your support.
## License
This package is licensed under the [MIT license](LICENSE.txt).
Raw data
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"description": "# **SigmaEpsilon.Solid.Fourier** - Fourier solutions of some plate and beam bending problems in Python\n\n\n\n[](https://dl.circleci.com/status-badge/redirect/gh/sigma-epsilon/sigmaepsilon.solid.fourier/tree/main)\n[](https://codecov.io/gh/sigma-epsilon/sigmaepsilon.solid.fourier)\n[](https://sigmaepsilonsolidfourier.readthedocs.io/en/latest/?badge=latest)\n[](https://opensource.org/licenses/MIT)\n[](https://pypi.org/project/sigmaepsilon.solid.fourier)\n[](https://www.python.org)\n[](https://github.com/psf/black)\n[](https://dependency-dash.repo-helper.uk/github/sigma-epsilon/sigmaepsilon.solid.fourier)\n\n> **Note**\n> Here and there, implementation of the performance critical parts of the library rely on the JIT-compilation capabilities of Numba. This means that the library performs well even for large scale problems, on the expense of a longer first call.\n\n## What is sigmaepsilon.solid.fourier?\n\nThe `sigmaepsilon.solid.fourier` library offers semi-analytic solutions to some beam and plate bending problems, where the boundary conditions are a-priori satisfied by careful selection of the approximating functions. Although the calculations only cover a handful of boundary conditions, when they are applicable, they are significantly faster than let say a finite element solution. For this reason, it is very useful for a couple of things:\n\n* experimentation\n* verification\n* concept validation\n* education\n* publication\n\n### Highlights\n\n* Semi-analytic, Navier solutions of beam and plate problems.\n* Easy to use, high level interface to define various kinds of loads.\n* Support for arbitrary loads using Monte-Carlo based coefficient determination.\n* Industry-grade performance based on highly parallel, performant code.\n* Tight integration with popular Python libraries like NumPy, SciPy, xarray, etc.\n* A gallery of examples for plotting with Matplotlib for all types of problems.\n* A collection of downloadable Jupyter Notebooks ready for execution covering all available functionality.\n* Getting Started, User Guide and API Reference in the documentation.\n* The library is intensively tested on CircleCI and has a high coverage level (read more about testing below).\n\n## Documentation\n\nThe [documentation](https://sigmaepsilonsolidfourier.readthedocs.io/en/latest/) is built with [Sphinx](https://www.sphinx-doc.org/en/master/) using the [PyData Sphinx Theme](https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html) and hosted on [ReadTheDocs](https://readthedocs.org/). Check it out for the user guide, an ever growing set of examples, and API Reference.\n\n## Installation\n\nsigmaepsilon.solid.fourier can be installed from PyPI using `pip` on Python >= 3.10:\n\n```console\n>>> pip install sigmaepsilon.solid.fourier\n```\n\nor chechkout with the following command using GitHub CLI\n\n```console\ngh repo clone sigma-epsilon/sigmaepsilon.solid.fourier\n```\n\nand install from source by typing\n\n```console\n>>> pip install .\n```\n\nIf you want to run the tests, you can install the package along with the necessary optional dependencies like this\n\n```console\n>>> pip install \".[test]\"\n```\n\nIf want to execute on the GPU, you need to manually install the necessary requirements. Numba is a direct dependency, so even in this case you have to care about having the prover version of the cuda toolkit installed. 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For instance if you feel like something is still shady (after reading the user guide), we want to know. Be gentle though, the development of the library is financially not supported yet.\n* feature requests\n Tell us what you think is missing (with realistic expectations).\n* examples\n If you've done something with the library and you think that it would make for a good example, get in touch with the developers and we will happily inlude it in the documention.\n* sharing is caring\n If you like the library, share it with your friends or colleagues so they can like it too.\n\nIn all cases, read the [contributing guidelines](CONTRIBUTING.md) before you do anything.\n\n## Acknowledgements\n\n**A lot of the packages mentioned on this document here and the introduction have a citable research paper. 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