semvec/pypi-packages huggingface dataset:
opticut 0.0.2
Solves the one dimensional cutting stock problem.
Requires Python: >=3.6

# opticut
1D cutting stock problem solver

It can be called as a python package, through CLI and a web based GUI.
This Package Needs a MIP(Mixed Integer Programming) solver as a dependency.

you can check the full documentation [here](https://cutting-stock-solver.readthedocs.io/en/latest/)


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## Installation
Windows:

Install one of the following solvers and add the executable file path to the environmental variables.

```sh
edit autoexec.bat
```

## Usage example

A few motivating and useful examples of how your product can be used. Spice this up with code blocks and potentially more screenshots.

_For more examples and usage, please refer to the [Wiki][wiki]._

## Development setup

Describe how to install all development dependencies and how to run an automated test-suite of some kind. Potentially do this for multiple platforms.

```sh
make install
npm test
```

## Release History

* 0.2.1
    * CHANGE: Update docs (module code remains unchanged)

## Meta

Sina Hajikazemi – sina.hkazemi@gmail.com

Distributed under the MIT license. See ``LICENSE`` for more information.

[https://github.com/sinahkazemi/](https://github.com/sinahkazemi/)

## Contributing

1. Fork it (<https://github.com/yourname/yourproject/fork>)
2. Create your feature branch (`git checkout -b feature/fooBar`)
3. Commit your changes (`git commit -am 'Add some fooBar'`)
4. Push to the branch (`git push origin feature/fooBar`)
5. Create a new Pull Request

<!-- Markdown link & img dfn's -->
[npm-image]: https://img.shields.io/npm/v/datadog-metrics.svg?style=flat-square
[npm-url]: https://npmjs.org/package/datadog-metrics
[npm-downloads]: https://img.shields.io/npm/dm/datadog-metrics.svg?style=flat-square
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[travis-url]: https://travis-ci.org/dbader/node-datadog-metrics
[wiki]: https://github.com/yourname/yourproject/wiki