| Name | DeepPeak JSON |
| Version |
0.0.3
JSON |
| download |
| home_page | None |
| Summary | A package for deep-learning peak detection. |
| upload_time | 2025-08-27 13:45:38 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT License
Copyright (c) 2020 Martin Poinsinet de Sivry-Houle
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 |
refracive index
optics
microbeads
mie scattering
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
DeepPeak
========
DeepPeak is a Python package for detecting and localizing peaks in 1D signals using deep learning. Designed for researchers and engineers, it simplifies the process of training and deploying neural networks for peak detection.
Key Features
------------
- **Deep Learning-based Peak Detection**: Leverages Keras and TensorFlow for state-of-the-art performance.
- **Gaussian Peak Handling**: Built-in support for detecting Gaussian-shaped peaks.
- **Custom Signal Support**: Easily adaptable to various types of 1D signals.
- **Easy-to-Use API**: Train and predict with minimal setup.
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