| Name | pyentrp JSON |
| Version |
0.9.0
JSON |
| download |
| home_page | |
| Summary | A Python library for computing entropy measures for time series analysis. |
| upload_time | 2023-12-20 13:32:23 |
| maintainer | |
| docs_url | None |
| author | Nikolay Donets |
| requires_python | >=3.9,<4.0 |
| license | Apache-2.0 |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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No Travis.
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| coveralls test coverage |
No coveralls.
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# pyEntropy (pyEntrp)
1. [Quick start](#quick-start)
2. [Usage](#usage)
3. [Contributors and participation](#contributors-and-participation)
pyEntropy is a lightweight library built on top of NumPy
that provides functions for computing various types of entropy for time series analysis.
The library currently supports the following types of entropy computation:
+ Shannon Entropy ```shannon_entropy```
+ Sample Entropy ```sample_entropy```
+ Multiscale Entropy ```multiscale_entropy```
+ Composite Multiscale Entropy ```composite_multiscale_entropy```
+ Permutation Entropy ```permutation_entropy```
+ Multiscale Permutation Entropy ```multiscale_permutation_entropy```
+ Weighted Permutation Entropy ```weighted_permutation_entropy```
## Quick start
Install [pyEntropy](https://github.com/nikdon/pyEntropy) using pip:
```
pip install pyentrp
```
Install [pyEntropy](https://github.com/nikdon/pyEntropy) using poetry:
```
poetry add pyentrp
```
## Usage
```python
from pyentrp import entropy as ent
import numpy as np
ts = [1, 4, 5, 1, 7, 3, 1, 2, 5, 8, 9, 7, 3, 7, 9, 5, 4, 3]
std_ts = np.std(ts)
sample_entropy = ent.sample_entropy(ts, 4, 0.2 * std_ts)
```
## Contributors and participation
[pyEntropy](https://github.com/nikdon/pyEntropy) is an open-source project, and contributions are highly encouraged.
If you would like to contribute, you can:
- [Fork the repository](https://github.com/nikdon/pyEntropy/fork) and submit pull requests with your improvements, bug
fixes, or new features.
- Report any issues or bugs you encounter on the [issue tracker](https://github.com/nikdon/pyEntropy/issues).
- Help improve the documentation by
submitting [documentation improvements or corrections](https://github.com/nikdon/pyEntropy/issues?q=is%3Aissue+is%3Aopen+label%3Adocumentation).
- Participate in discussions and share your ideas.
The following contributors have made significant contributions to pyEntropy:
* [Nikolay Donets](https://github.com/nikdon)
* [Jakob Dreyer](https://github.com/jakobdreyer)
* [Raphael Vallat](https://github.com/raphaelvallat)
* [Christopher Schölzel](https://github.com/CSchoel)
* [Sam Dotson](https://github.com/samgdotson)
Contributions are very welcome, documentation improvements/corrections, bug reports, even feature requests :)
If you find [pyEntropy](https://github.com/nikdon/pyEntropy) useful, please consider giving it a star.
Your support is greatly appreciated!
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"description": "# pyEntropy (pyEntrp)\n\n\n\n\n\n\n1. [Quick start](#quick-start)\n2. [Usage](#usage)\n3. [Contributors and participation](#contributors-and-participation)\n\npyEntropy is a lightweight library built on top of NumPy\nthat provides functions for computing various types of entropy for time series analysis.\n\nThe library currently supports the following types of entropy computation:\n\n+ Shannon Entropy ```shannon_entropy```\n+ Sample Entropy ```sample_entropy```\n+ Multiscale Entropy ```multiscale_entropy```\n+ Composite Multiscale Entropy ```composite_multiscale_entropy```\n+ Permutation Entropy ```permutation_entropy```\n+ Multiscale Permutation Entropy ```multiscale_permutation_entropy```\n+ Weighted Permutation Entropy ```weighted_permutation_entropy```\n\n## Quick start\n\nInstall [pyEntropy](https://github.com/nikdon/pyEntropy) using pip:\n\n```\npip install pyentrp\n```\n\nInstall [pyEntropy](https://github.com/nikdon/pyEntropy) using poetry:\n\n```\npoetry add pyentrp\n```\n\n## Usage\n\n```python\nfrom pyentrp import entropy as ent\nimport numpy as np\n\nts = [1, 4, 5, 1, 7, 3, 1, 2, 5, 8, 9, 7, 3, 7, 9, 5, 4, 3]\nstd_ts = np.std(ts)\nsample_entropy = ent.sample_entropy(ts, 4, 0.2 * std_ts)\n```\n\n## Contributors and participation\n\n[pyEntropy](https://github.com/nikdon/pyEntropy) is an open-source project, and contributions are highly encouraged.\nIf you would like to contribute, you can:\n\n- [Fork the repository](https://github.com/nikdon/pyEntropy/fork) and submit pull requests with your improvements, bug\n fixes, or new features.\n- Report any issues or bugs you encounter on the [issue tracker](https://github.com/nikdon/pyEntropy/issues).\n- Help improve the documentation by\n submitting [documentation improvements or corrections](https://github.com/nikdon/pyEntropy/issues?q=is%3Aissue+is%3Aopen+label%3Adocumentation).\n- Participate in discussions and share your ideas.\n\nThe following contributors have made significant contributions to pyEntropy:\n\n* [Nikolay Donets](https://github.com/nikdon)\n* [Jakob Dreyer](https://github.com/jakobdreyer)\n* [Raphael Vallat](https://github.com/raphaelvallat)\n* [Christopher Sch\u00f6lzel](https://github.com/CSchoel)\n* [Sam Dotson](https://github.com/samgdotson)\n\nContributions are very welcome, documentation improvements/corrections, bug reports, even feature requests :)\n\nIf you find [pyEntropy](https://github.com/nikdon/pyEntropy) useful, please consider giving it a star.\n\nYour support is greatly appreciated!\n",
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