| Name | findpeaks JSON |
| Version |
2.7.1
JSON |
| download |
| home_page | None |
| Summary | findpeaks is for the detection of peaks and valleys in a 1D vector and 2D array (image). |
| upload_time | 2025-07-27 13:21:40 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3 |
| license | MIT License
Copyright (c) 2020 Erdogan Taskesen
findpeaks - Python package
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
python
topology
mesh
sonar
mask
sar
topological data analysis
peak-detection
denoise images
peak analysis
speckle-noise removal
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
scipy
matplotlib
numpy
pandas
tqdm
requests
caerus
xarray
joblib
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# findpeaks
[](https://img.shields.io/pypi/pyversions/findpeaks)
[](https://pypi.org/project/findpeaks/)
[](https://erdogant.github.io/findpeaks/)
[](https://github.com/erdogant/findpeaks/)
[](https://pepy.tech/project/findpeaks)
[](https://pepy.tech/project/findpeaks)
[](https://github.com/erdogant/findpeaks/blob/master/LICENSE)
[](https://github.com/erdogant/findpeaks/network)
[](https://github.com/erdogant/findpeaks/issues)
[](http://www.repostatus.org/#active)
[](https://zenodo.org/badge/latestdoi/260400472)
[](https://erdogant.github.io/findpeaks/pages/html/Documentation.html#colab-notebook)
[](https://erdogant.github.io/findpeaks/pages/html/Documentation.html#medium-blog)
[](https://erdogant.github.io/findpeaks/pages/html/Documentation.html#)
<div>
<a href="https://erdogant.github.io/findpeaks/"><img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/logo.png" width="125" align="left" /></a>
findpeaks is a comprehensive Python library for robust detection and analysis of peaks and valleys in both 1D vectors and 2D arrays (images). The library provides multiple detection algorithms including topology-based persistent homology (most robust), mask-based local maximum filtering, and traditional peakdetect approaches. It can be used for time series analysis, signal processing, image analysis, and spatial data. ⭐️Star it if you like it⭐️
</div>
---
### Key Features
| Feature | Description |
|--------|-------------|
| [**Topology Detection**](https://erdogant.github.io/findpeaks/pages/html/Topology.html) | Mathematically grounded peak detection using persistent homology. |
| [**Peakdetect Method**](https://erdogant.github.io/findpeaks/pages/html/Peakdetect.html) | Traditional peak detection algorithm for noisy signals. |
| [**Mask Detection**](https://erdogant.github.io/findpeaks/pages/html/Mask.html) | Local maximum filtering for 2D image analysis. |
| [**Caerus Method**](https://erdogant.github.io/findpeaks/pages/html/Caerus.html) | Specialized algorithm for financial time series analysis. |
| [**Preprocessing**](https://erdogant.github.io/findpeaks/pages/html/Pre-processing.html) | Denoising, scaling, interpolation, and image preprocessing. |
| [**Visualization**](https://erdogant.github.io/findpeaks/pages/html/Plots.html) | Rich plotting capabilities including persistence diagrams and 3D mesh plots. |
---
### Resources and Links
- **Example Notebooks:** [Examples](https://erdogant.github.io/findpeaks/pages/html/Examples.html)
- **Blog Posts:** [Medium](https://erdogant.medium.com)
- **Documentation:** [Website](https://erdogant.github.io/findpeaks)
- **Bug Reports and Feature Requests:** [GitHub Issues](https://github.com/erdogant/findpeaks/issues)
---
### Background
* **Topology Method**: The most robust detection method based on persistent homology from topological data analysis. It quantifies peak significance through persistence scores and provides mathematically stable results even in noisy data.
* **Peakdetect Method**: Traditional algorithm that excels at finding local maxima and minima in noisy signals without requiring extensive preprocessing. Uses a lookahead approach to distinguish between true peaks and noise-induced fluctuations.
* **Mask Method**: Local maximum filtering approach specifically designed for 2D data (images). Employs 8-connected neighborhood analysis and background removal for spatial peak detection.
* **Preprocessing Pipeline**: Comprehensive preprocessing capabilities including interpolation, denoising (Lee, Frost, Kuan filters), scaling, and image resizing to improve detection accuracy.
---
### Installation
##### Install findpeaks from PyPI
```bash
pip install findpeaks
```
##### Install from Github source
```bash
pip install git+https://github.com/erdogant/findpeaks
```
##### Import Library
```python
import findpeaks
print(findpeaks.__version__)
# Import library
from findpeaks import findpeaks
```
---
### Quick Start
```python
# Import library
from findpeaks import findpeaks
# Initialize with topology method (most robust)
fp = findpeaks(method='topology')
# Example data
X = fp.import_example('1dpeaks')
# Peak detection
results = fp.fit(X)
# Plot results
fp.plot()
# Plot persistence diagram
fp.plot_persistence()
```
---
### Examples
#### 1D Signal Analysis
* [Find peaks in low sampled dataset](https://erdogant.github.io/findpeaks/pages/html/Examples.html#find-peaks-in-low-sampled-dataset)
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Examples.html#find-peaks-in-low-sampled-dataset">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig1_raw.png" width="400" />
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig1_interpol.png" width="400" />
</a>
</p>
* [Comparison of peak detection methods](https://erdogant.github.io/findpeaks/pages/html/Examples.html#comparison-methods-1)
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Examples.html#comparison-methods-1">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig2_peakdetect_int.png" width="400" />
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig2_topology_int.png" width="400" />
</a>
</p>
* [Find peaks in high sampled dataset](https://erdogant.github.io/findpeaks/pages/html/Examples.html#find-peaks-in-high-sampled-dataset)
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Examples.html#find-peaks-in-high-sampled-dataset">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig3.png" width="600" />
</a>
</p>
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Examples.html#find-peaks-in-high-sampled-dataset">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig3_persistence_limit.png" width="600" />
</a>
</p>
#### 2D Image Analysis
* [Find peaks in an image (2D-array)](https://erdogant.github.io/findpeaks/pages/html/Examples.html#d-array-image)
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Examples.html#d-array-image">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/2dpeaks_raw.png" width="115" />
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/2dpeaks_mask.png" width="500" />
</a>
</p>
* [3D mesh visualization](https://erdogant.github.io/findpeaks/pages/html/Plots.html#d-mesh)
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Plots.html#d-mesh">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/2dpeaks_mesh1.png" width="400" />
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/2dpeaks_mesh2.png" width="400" />
</a>
</p>
#### Financial Time Series
* [Bitcoin price analysis](https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#bitcoin)
* [Facebook stock analysis](https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#facebook-stocks)
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#facebook-stocks">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig_facebook_minperc5.png" width="600" />
</a>
</p>
#### SAR/SONAR Image Processing
* [Peak detection in SAR/SONAR images](https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#sonar)
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#sonar">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/sonar_plot.png" width="600" />
</a>
</p>
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#sonar">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/sonar_mesh1.png" width="300" />
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/sonar_mesh2.png" width="300" />
</a>
</p>
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#sonar">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/sonar_mesh3.png" width="300" />
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/sonar_mesh4.png" width="300" />
</a>
</p>
#### Image Denoising
* [Denoising with Lee, Kuan, Fastnl, Bilateral, Frost, Mean, Median filters](https://erdogant.github.io/findpeaks/pages/html/Denoise.html#)
<p align="left">
<a href="https://erdogant.github.io/findpeaks/pages/html/Denoise.html#">
<img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/noise_distr_examples.png" width="600" />
</a>
</p>
<hr>
### References
* https://github.com/erdogant/findpeaks
* https://github.com/Anaxilaus/peakdetect
* https://www.sthu.org/blog/13-perstopology-peakdetection/index.html
### Contributors
Special thanks to the contributors!
<p align="left">
<a href="https://github.com/erdogant/findpeaks/graphs/contributors">
<img src="https://contrib.rocks/image?repo=erdogant/findpeaks" />
</a>
</p>
### Maintainer
* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
* Contributions are welcome.
* Yes! This library is entirely **free** but it runs on coffee! :) Feel free to support with a <a href="https://erdogant.github.io/donate/?currency=USD&amount=5">Coffee</a>.
[](https://www.buymeacoffee.com/erdogant)
Raw data
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"keywords": "Python, topology, mesh, sonar, mask, sar, topological data analysis, peak-detection, denoise images, peak analysis, speckle-noise removal",
"author": null,
"author_email": "Erdogan Taskesen <erdogant@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/2f/51/8a6b6a1ff82816c7f58c92f5a5fdd140ae8b7e1ee91d9b9853906a4a9e1e/findpeaks-2.7.1.tar.gz",
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"description": "# findpeaks\r\n\r\n[](https://img.shields.io/pypi/pyversions/findpeaks)\r\n[](https://pypi.org/project/findpeaks/)\r\n[](https://erdogant.github.io/findpeaks/)\r\n[](https://github.com/erdogant/findpeaks/)\r\n[](https://pepy.tech/project/findpeaks)\r\n[](https://pepy.tech/project/findpeaks)\r\n[](https://github.com/erdogant/findpeaks/blob/master/LICENSE)\r\n[](https://github.com/erdogant/findpeaks/network)\r\n[](https://github.com/erdogant/findpeaks/issues)\r\n[](http://www.repostatus.org/#active)\r\n[](https://zenodo.org/badge/latestdoi/260400472)\r\n[](https://erdogant.github.io/findpeaks/pages/html/Documentation.html#colab-notebook)\r\n[](https://erdogant.github.io/findpeaks/pages/html/Documentation.html#medium-blog)\r\n[](https://erdogant.github.io/findpeaks/pages/html/Documentation.html#)\r\n\r\n<div>\r\n<a href=\"https://erdogant.github.io/findpeaks/\"><img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/logo.png\" width=\"125\" align=\"left\" /></a>\r\nfindpeaks is a comprehensive Python library for robust detection and analysis of peaks and valleys in both 1D vectors and 2D arrays (images). The library provides multiple detection algorithms including topology-based persistent homology (most robust), mask-based local maximum filtering, and traditional peakdetect approaches. It can be used for time series analysis, signal processing, image analysis, and spatial data. \u2b50\ufe0fStar it if you like it\u2b50\ufe0f\r\n</div>\r\n\r\n---\r\n\r\n### Key Features\r\n\r\n| Feature | Description |\r\n|--------|-------------|\r\n| [**Topology Detection**](https://erdogant.github.io/findpeaks/pages/html/Topology.html) | Mathematically grounded peak detection using persistent homology. |\r\n| [**Peakdetect Method**](https://erdogant.github.io/findpeaks/pages/html/Peakdetect.html) | Traditional peak detection algorithm for noisy signals. |\r\n| [**Mask Detection**](https://erdogant.github.io/findpeaks/pages/html/Mask.html) | Local maximum filtering for 2D image analysis. |\r\n| [**Caerus Method**](https://erdogant.github.io/findpeaks/pages/html/Caerus.html) | Specialized algorithm for financial time series analysis. |\r\n| [**Preprocessing**](https://erdogant.github.io/findpeaks/pages/html/Pre-processing.html) | Denoising, scaling, interpolation, and image preprocessing. |\r\n| [**Visualization**](https://erdogant.github.io/findpeaks/pages/html/Plots.html) | Rich plotting capabilities including persistence diagrams and 3D mesh plots. |\r\n\r\n---\r\n\r\n### Resources and Links\r\n- **Example Notebooks:** [Examples](https://erdogant.github.io/findpeaks/pages/html/Examples.html)\r\n- **Blog Posts:** [Medium](https://erdogant.medium.com)\r\n- **Documentation:** [Website](https://erdogant.github.io/findpeaks)\r\n- **Bug Reports and Feature Requests:** [GitHub Issues](https://github.com/erdogant/findpeaks/issues)\r\n\r\n---\r\n\r\n### Background\r\n\r\n* **Topology Method**: The most robust detection method based on persistent homology from topological data analysis. It quantifies peak significance through persistence scores and provides mathematically stable results even in noisy data.\r\n\r\n* **Peakdetect Method**: Traditional algorithm that excels at finding local maxima and minima in noisy signals without requiring extensive preprocessing. Uses a lookahead approach to distinguish between true peaks and noise-induced fluctuations.\r\n\r\n* **Mask Method**: Local maximum filtering approach specifically designed for 2D data (images). Employs 8-connected neighborhood analysis and background removal for spatial peak detection.\r\n\r\n* **Preprocessing Pipeline**: Comprehensive preprocessing capabilities including interpolation, denoising (Lee, Frost, Kuan filters), scaling, and image resizing to improve detection accuracy.\r\n\r\n---\r\n\r\n### Installation\r\n\r\n##### Install findpeaks from PyPI\r\n```bash\r\npip install findpeaks\r\n```\r\n\r\n##### Install from Github source\r\n```bash\r\npip install git+https://github.com/erdogant/findpeaks\r\n``` \r\n\r\n##### Import Library\r\n```python\r\nimport findpeaks\r\nprint(findpeaks.__version__)\r\n\r\n# Import library\r\nfrom findpeaks import findpeaks\r\n```\r\n\r\n---\r\n\r\n### Quick Start\r\n\r\n```python\r\n# Import library\r\nfrom findpeaks import findpeaks\r\n\r\n# Initialize with topology method (most robust)\r\nfp = findpeaks(method='topology')\r\n\r\n# Example data\r\nX = fp.import_example('1dpeaks')\r\n\r\n# Peak detection\r\nresults = fp.fit(X)\r\n\r\n# Plot results\r\nfp.plot()\r\n\r\n# Plot persistence diagram\r\nfp.plot_persistence()\r\n```\r\n\r\n---\r\n\r\n### Examples\r\n\r\n#### 1D Signal Analysis\r\n* [Find peaks in low sampled dataset](https://erdogant.github.io/findpeaks/pages/html/Examples.html#find-peaks-in-low-sampled-dataset)\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Examples.html#find-peaks-in-low-sampled-dataset\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig1_raw.png\" width=\"400\" />\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig1_interpol.png\" width=\"400\" /> \r\n </a>\r\n</p>\r\n\r\n* [Comparison of peak detection methods](https://erdogant.github.io/findpeaks/pages/html/Examples.html#comparison-methods-1)\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Examples.html#comparison-methods-1\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig2_peakdetect_int.png\" width=\"400\" /> \r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig2_topology_int.png\" width=\"400\" /> \r\n </a>\r\n</p>\r\n\r\n* [Find peaks in high sampled dataset](https://erdogant.github.io/findpeaks/pages/html/Examples.html#find-peaks-in-high-sampled-dataset)\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Examples.html#find-peaks-in-high-sampled-dataset\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig3.png\" width=\"600\" />\r\n </a>\r\n</p>\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Examples.html#find-peaks-in-high-sampled-dataset\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig3_persistence_limit.png\" width=\"600\" />\r\n </a>\r\n</p>\r\n\r\n#### 2D Image Analysis\r\n* [Find peaks in an image (2D-array)](https://erdogant.github.io/findpeaks/pages/html/Examples.html#d-array-image)\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Examples.html#d-array-image\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/2dpeaks_raw.png\" width=\"115\" />\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/2dpeaks_mask.png\" width=\"500\" />\r\n </a>\r\n</p>\r\n\r\n* [3D mesh visualization](https://erdogant.github.io/findpeaks/pages/html/Plots.html#d-mesh)\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Plots.html#d-mesh\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/2dpeaks_mesh1.png\" width=\"400\" />\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/2dpeaks_mesh2.png\" width=\"400\" />\r\n </a>\r\n</p>\r\n\r\n#### Financial Time Series\r\n* [Bitcoin price analysis](https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#bitcoin)\r\n* [Facebook stock analysis](https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#facebook-stocks)\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#facebook-stocks\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/fig_facebook_minperc5.png\" width=\"600\" />\r\n </a>\r\n</p>\r\n\r\n#### SAR/SONAR Image Processing\r\n* [Peak detection in SAR/SONAR images](https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#sonar)\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#sonar\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/sonar_plot.png\" width=\"600\" />\r\n </a>\r\n</p>\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#sonar\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/sonar_mesh1.png\" width=\"300\" />\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/sonar_mesh2.png\" width=\"300\" />\r\n </a>\r\n</p>\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Use-cases.html#sonar\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/sonar_mesh3.png\" width=\"300\" />\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/sonar_mesh4.png\" width=\"300\" />\r\n </a>\r\n</p>\r\n\r\n#### Image Denoising\r\n* [Denoising with Lee, Kuan, Fastnl, Bilateral, Frost, Mean, Median filters](https://erdogant.github.io/findpeaks/pages/html/Denoise.html#)\r\n\r\n<p align=\"left\">\r\n <a href=\"https://erdogant.github.io/findpeaks/pages/html/Denoise.html#\">\r\n <img src=\"https://github.com/erdogant/findpeaks/blob/master/docs/figs/noise_distr_examples.png\" width=\"600\" />\r\n </a>\r\n</p>\r\n\r\n<hr>\r\n\r\n### References\r\n* https://github.com/erdogant/findpeaks\r\n* https://github.com/Anaxilaus/peakdetect\r\n* https://www.sthu.org/blog/13-perstopology-peakdetection/index.html\r\n\r\n### Contributors\r\nSpecial thanks to the contributors!\r\n\r\n<p align=\"left\">\r\n <a href=\"https://github.com/erdogant/findpeaks/graphs/contributors\">\r\n <img src=\"https://contrib.rocks/image?repo=erdogant/findpeaks\" />\r\n </a>\r\n</p>\r\n\r\n### Maintainer\r\n* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)\r\n* Contributions are welcome.\r\n* Yes! This library is entirely **free** but it runs on coffee! :) Feel free to support with a <a href=\"https://erdogant.github.io/donate/?currency=USD&amount=5\">Coffee</a>.\r\n\r\n[](https://www.buymeacoffee.com/erdogant)\r\n",
"bugtrack_url": null,
"license": "MIT License\r\n \r\n Copyright (c) 2020 Erdogan Taskesen\r\n findpeaks - Python package\r\n \r\n Permission is hereby granted, free of charge, to any person obtaining a copy\r\n of this software and associated documentation files (the \"Software\"), to deal\r\n in the Software without restriction, including without limitation the rights\r\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\r\n copies of the Software, and to permit persons to whom the Software is\r\n furnished to do so, subject to the following conditions:\r\n \r\n The above copyright notice and this permission notice shall be included in all\r\n copies or substantial portions of the Software.\r\n \r\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\r\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\r\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\r\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\r\n SOFTWARE.\r\n ",
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