| Name | ampdLib JSON |
| Version |
1.1.5
JSON |
| download |
| home_page | |
| Summary | Implementation of AMPD algorithm for peak detection in quasi-periodic signals |
| upload_time | 2023-12-08 15:17:07 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.6 |
| license | |
| keywords |
peak detection
signal processing
quasi-periodic signals
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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README.md rev. 10 Feb 2023 by Luca Cerina.
Copyright (c) 2023 Luca Cerina.
Distributed under the Apache 2.0 License in the accompanying file LICENSE.
# Automatic Multiscale-based Peak Detection (AMPD)
ampdLib implements automatic multiscale-based peak detection (AMPD) algorithm
as in An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and
Quasi-Periodic Signals, by Felix Scholkmann, Jens Boss and Martin Wolf,
Algorithms 2012, 5, 588-603.
### Python required dependencies
- Python >= 3.6
- Numpy
- Scipy for tests
### Installation
The library can be easily installed with setuptools support using `pip install .` or via PyPI with `pip install ampdlib`
### Usage
A simple example is:
```
peaks = ampdlib.ampd(input)
```
AMPD may require a lot of memory (N*(lsm_limit*N/2) bytes for a given length N and default lsm_limit). A solution is to divide the signal in windows with `ampd_fast` or `ampd_fast_sub` or determine a better lsm_limit for the minimum distance between peaks required by the use case with `get_optimal_size`.
### Tests
The tests folder contains an ECG signal with annotated peaks in matlab format.
#### Contribution
If you feel generous and want to show some extra appreciation:
[![Buy me a coffee][buymeacoffee-shield]][buymeacoffee]
[buymeacoffee]: https://www.buymeacoffee.com/u2Vb3kO
[buymeacoffee-shield]: https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png
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"description": "README.md rev. 10 Feb 2023 by Luca Cerina.\r\nCopyright (c) 2023 Luca Cerina.\r\nDistributed under the Apache 2.0 License in the accompanying file LICENSE.\r\n\r\n# Automatic Multiscale-based Peak Detection (AMPD)\r\n\r\nampdLib implements automatic multiscale-based peak detection (AMPD) algorithm\r\nas in An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and\r\nQuasi-Periodic Signals, by Felix Scholkmann, Jens Boss and Martin Wolf,\r\nAlgorithms 2012, 5, 588-603.\r\n\r\n### Python required dependencies\r\n- Python >= 3.6\r\n- Numpy\r\n- Scipy for tests\r\n\r\n### Installation\r\nThe library can be easily installed with setuptools support using `pip install .` or via PyPI with `pip install ampdlib`\r\n\r\n### Usage\r\nA simple example is:\r\n```\r\npeaks = ampdlib.ampd(input)\r\n```\r\n\r\nAMPD may require a lot of memory (N*(lsm_limit*N/2) bytes for a given length N and default lsm_limit). A solution is to divide the signal in windows with `ampd_fast` or `ampd_fast_sub` or determine a better lsm_limit for the minimum distance between peaks required by the use case with `get_optimal_size`. \r\n\r\n### Tests\r\nThe tests folder contains an ECG signal with annotated peaks in matlab format.\r\n\r\n#### Contribution\r\nIf you feel generous and want to show some extra appreciation:\r\n\r\n[![Buy me a coffee][buymeacoffee-shield]][buymeacoffee]\r\n\r\n[buymeacoffee]: https://www.buymeacoffee.com/u2Vb3kO\r\n[buymeacoffee-shield]: https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png\r\n",
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