Metadata-Version: 2.4
Name: DeepPeak
Version: 0.0.7
Summary: A package for deep-learning peak detection.
Author-email: Martin Poinsinet de Sivry-Houle <martin.poinsinet.de.sivry@gmail.com>
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.
        
Project-URL: Homepage, https://github.com/MartinPdeS/DeepPeak
Project-URL: Documentation, https://martinpdes.github.io/DeepPeak/
Project-URL: Repository, https://github.com/MartinPdeS/DeepPeak.git
Keywords: refracive index,optics,microbeads,Mie scattering
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.10
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: tensorflow
Requires-Dist: scikit-learn
Requires-Dist: MPSPlots
Provides-Extra: testing
Requires-Dist: pytest>=0.6; extra == "testing"
Requires-Dist: pytest-cov>=2.0; extra == "testing"
Requires-Dist: pytest-json-report==1.5.0; extra == "testing"
Requires-Dist: coverage==7.10.7; extra == "testing"
Provides-Extra: documentation
Requires-Dist: numpydoc==1.9.0; extra == "documentation"
Requires-Dist: sphinx>=5.1.1; extra == "documentation"
Requires-Dist: sphinx-rtd-theme==3.0.2; extra == "documentation"
Requires-Dist: sphinx-gallery==0.19.0; extra == "documentation"
Requires-Dist: pydata-sphinx-theme==0.16.1; extra == "documentation"
Provides-Extra: dev
Requires-Dist: flake8==7.3.0; extra == "dev"
Dynamic: license-file

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   * - Meta
     - |python|
     - |docs|
     -
   * - Testing
     - |ci/cd|
     - |coverage|
     - |colab|
   * - PyPI
     - |PyPI|
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   * - Anaconda
     - |anaconda|
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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.

Contact
-------
For questions or contributions, contact `martin.poinsinet.de.sivry@gmail.com <mailto:martin.poinsinet.de.sivry@gmail.com>`_.

.. |python| image:: https://img.shields.io/pypi/pyversions/deeppeak.svg
    :alt: Python
    :target: https://www.python.org/
.. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg
    :alt: Google Colab
    :target: https://colab.research.google.com/github/MartinPdeS/DeepPeak/blob/master/notebook.ipynb
.. |docs| image:: https://github.com/martinpdes/deeppeak/actions/workflows/deploy_documentation.yml/badge.svg
    :target: https://martinpdes.github.io/DeepPeak/
    :alt: Documentation Status
.. |PyPI| image:: https://badge.fury.io/py/DeepPeak.svg
    :alt: PyPI version
    :target: https://badge.fury.io/py/DeepPeak
.. |PyPI_download| image:: https://img.shields.io/pypi/dm/DeepPeak?style=plastic&label=PyPI%20downloads&labelColor=hex&color=hex
    :alt: PyPI downloads
    :target: https://pypistats.org/packages/deeppeak
.. |coverage| image:: https://raw.githubusercontent.com/MartinPdeS/DeepPeak/python-coverage-comment-action-data/badge.svg
    :alt: Unittest coverage
    :target: https://htmlpreview.github.io/?https://github.com/MartinPdeS/DeepPeak/blob/python-coverage-comment-action-data/htmlcov/index.html
.. |ci/cd| image:: https://github.com/martinpdes/deeppeak/actions/workflows/deploy_coverage.yml/badge.svg
    :alt: Unittest Status
.. |anaconda| image:: https://anaconda.org/martinpdes/deeppeak/badges/version.svg
    :alt: Anaconda version
    :target: https://anaconda.org/martinpdes/deeppeak
.. |anaconda_download| image:: https://anaconda.org/martinpdes/deeppeak/badges/downloads.svg
    :alt: Anaconda downloads
    :target: https://anaconda.org/martinpdes/deeppeak
.. |anaconda_date| image:: https://anaconda.org/martinpdes/deeppeak/badges/latest_release_relative_date.svg
    :alt: Latest release date
    :target: https://anaconda.org/martinpdes/deeppeak
