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Examples
========

Welcome to the DeepPeak examples gallery! This directory contains comprehensive examples demonstrating the key capabilities of the DeepPeak library for signal processing and peak detection using deep learning.


DeepPeak is a Python library designed for detecting and analyzing peaks in 1D signals using machine learning approaches. These examples showcase the library's main features:

- **Signal Generation**: Create synthetic datasets with controllable noise and peak characteristics
- **Machine Learning Classifiers**: Train neural networks to detect regions of interest
- **Peak Detection Algorithms**: Apply traditional and ML-enhanced peak detection methods
- **Visualization Tools**: Plot and analyze results with built-in visualization utilities



.. raw:: html

    <div class="sphx-glr-thumbnails">

.. thumbnail-parent-div-open

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the use of the NonMaximumSuppression class to detect Gaussian pulses in a one-dimensional signal. It generates a synthetic dataset of Gaussian pulses, applies the non-maximum suppression algorithm, and plots the results.">

.. only:: html

  .. image:: /gallery/images/thumb/sphx_glr_amplitude_retrieval_thumb.png
    :alt:

  :ref:`sphx_glr_gallery_amplitude_retrieval.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Non-Maximum Suppression for Gaussian Pulse Detection</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use DeepPeak&#x27;s DenseNet classifier to identify regions of interest (ROIs) in synthetic 1D signals containing Gaussian peaks.">

.. only:: html

  .. image:: /gallery/images/thumb/sphx_glr_classifier_autoencoder_thumb.png
    :alt:

  :ref:`sphx_glr_gallery_classifier_autoencoder.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">DenseNet Classifier: Detecting Regions of Interest in Synthetic Signals</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use DeepPeak&#x27;s DenseNet classifier to identify regions of interest (ROIs) in synthetic 1D signals containing Gaussian peaks.">

.. only:: html

  .. image:: /gallery/images/thumb/sphx_glr_classifier_dense_thumb.png
    :alt:

  :ref:`sphx_glr_gallery_classifier_dense.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">DenseNet Classifier: Detecting Regions of Interest in Synthetic Signals</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use DeepPeak&#x27;s DenseNet classifier to identify regions of interest (ROIs) in synthetic 1D signals containing Gaussian peaks.">

.. only:: html

  .. image:: /gallery/images/thumb/sphx_glr_classifier_wavenet_thumb.png
    :alt:

  :ref:`sphx_glr_gallery_classifier_wavenet.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">DenseNet Classifier: Detecting Regions of Interest in Synthetic Signals</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to:   1. Generate synthetic signals with up to 3 Gaussian pulses.   2. Compute a Region of Interest (ROI) mask based on pulse positions.   3. Visualize signals with peak positions, amplitudes, and the ROI mask.">

.. only:: html

  .. image:: /gallery/images/thumb/sphx_glr_data_generation_thumb.png
    :alt:

  :ref:`sphx_glr_gallery_data_generation.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Generating and Visualizing Signal Data</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the use of the NonMaximumSuppression class to detect Gaussian pulses in a one-dimensional signal. It generates a synthetic dataset of Gaussian pulses, applies the non-maximum suppression algorithm, and plots the results.">

.. only:: html

  .. image:: /gallery/images/thumb/sphx_glr_non_maximum_suppression_thumb.png
    :alt:

  :ref:`sphx_glr_gallery_non_maximum_suppression.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Non-Maximum Suppression for Gaussian Pulse Detection</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /gallery/amplitude_retrieval
   /gallery/classifier_autoencoder
   /gallery/classifier_dense
   /gallery/classifier_wavenet
   /gallery/data_generation
   /gallery/non_maximum_suppression



.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_