Metadata-Version: 2.1
Name: Intrst-algrms
Version: 0.4.4
Summary: https://intrst-algrms.readthedocs.io/en/latest/
Home-page: https://pypi.org/project/Intrst-algrms/
Author: Nikolai Vidov
Author-email: lastsal@mail.ru
License: MIT
Keywords: example documentation algorithms
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Other Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: Cython
Requires-Dist: iteration-utilities
Requires-Dist: more-itertools
Requires-Dist: memory-profiler
Requires-Dist: matplotlib

[![](https://travis-ci.org/Niccolum/intrst_algrms.svg?branch=master)](https://travis-ci.org/Niccolum/intrst_algrms)
[![](https://codecov.io/gh/Niccolum/intrst_algrms/branch/master/graph/badge.svg)](https://codecov.io/gh/Niccolum/intrst_algrms)
[![](https://img.shields.io/pypi/v/Intrst_algrms.svg?colorB=blue)](https://pypi.org/project/Intrst-algrms/)
[![](https://img.shields.io/pypi/pyversions/Intrst_algrms.svg)](https://pypi.org/project/Intrst-algrms/)

# Instruments and algorithms

Here is examples of realizations several algorithms, they performance, memory used and result plots.

## Getting Started

### Requirements

Python 3.6+ (because of type checking)

### Installing

```bash
python3 -m venv env
source env/bin/activate
```

##### For check and research you must do:

```bash
pip install -e git+https://github.com/Niccolum/intrst_algrms#egg=Intrst_algrms
cd env/src/intrst_algrms
```

###### Note:

*For check memory tests - install line_profiler manually.*
*This package not include to install because of problem with installation. Details [here](https://github.com/rkern/line_profiler#installation)*

After that you can run commands from results.md on each folders.

More detailed description and HOW-TO:

Results of research algorithms here:
* [Binary Tree (sorting)](https://github.com/Niccolum/intrst_algrms/blob/master/binary_tree/results.md)
* [Knapsack problem](https://github.com/Niccolum/intrst_algrms/blob/master/knapsack_problem/results.md)
* [Unpacking nested lists of indefinite depth](https://github.com/Niccolum/intrst_algrms/blob/master/unpacking_flatten_lists/results.md)

##### For use funcs in your own code you can install package from PYPI:

```bash
pip install Intrst_algrms
```

and import it from your code:
```python
from binary_tree.funcs import ...
from knapsack_problem.funcs import ...
from unpacking_flatten_lists.funcs import ...
```

More, about funcs, in [docs](https://intrst-algrms.readthedocs.io/en/latest/)

## Contributing

Please read [CONTRIBUTING.md](https://github.com/Niccolum/intrst_algrms/blob/master/CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.

## Dev

For developers info [here](https://github.com/Niccolum/intrst_algrms/blob/master/README-DEV.md).

## Authors

* **Nikolai Vidov** - *maintainer* - [Niccolum](https://github.com/Niccolum)

## License

This project is licensed under the MIT License - see the [LICENSE.md](https://github.com/Niccolum/intrst_algrms/blob/master/LICENSE.md) file for details

