.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/descriptors_example.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_descriptors_example.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_descriptors_example.py:


Minimal descriptors example with explicit NumPy inputs.

.. GENERATED FROM PYTHON SOURCE LINES 4-71




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Descriptor matrix shape: (12, 14)
    First five names: ['band_abs_delta_chgrp-all', 'band_abs_theta_chgrp-all', 'band_abs_alpha_chgrp-all', 'band_abs_beta_chgrp-all', 'band_abs_gamma_chgrp-all']
    Failure count: 0
    Grouped descriptor shape: (2, 2, 14)
    Grouped dims: ('obs', 'stat', 'feature')
    Grouped stats: ['mean', 'std']






|

.. code-block:: Python


    from __future__ import annotations

    import numpy as np

    from coco_pipe.descriptors import DescriptorPipeline
    from coco_pipe.io import DataContainer


    def main() -> None:
        rng = np.random.default_rng(42)
        X = rng.normal(size=(12, 3, 256))
        t = np.linspace(0, 1, 256, endpoint=False)
        X[:, 0, :] += np.sin(2 * np.pi * 10 * t)
        X[:, 1, :] += np.sin(2 * np.pi * 6 * t)
        ids = np.asarray([f"obs-{idx:02d}" for idx in range(12)])

        config = {
            "families": {
                "bands": {
                    "enabled": True,
                    "outputs": ["absolute_power", "corrected_absolute_power"],
                },
                "parametric": {
                    "enabled": True,
                    "outputs": ["aperiodic"],
                },
                "complexity": {
                    "enabled": True,
                    "measures": ["sample_entropy", "hjorth_mobility"],
                },
            },
        }

        channels = ["Fz", "Cz", "Pz"]
        pipe = DescriptorPipeline(config)
        result = pipe.extract(
            X=X,
            ids=ids,
            sfreq=256.0,
            channel_names=channels,
        )

        # Apply pooling after extraction
        result = pipe.pool_channels(result, {"all": channels})

        print("Descriptor matrix shape:", result["X"].shape)
        print("First five names:", result["descriptor_names"][:5])
        print("Failure count:", len(result["failures"]))

        container = DataContainer(
            X=result["X"],
            dims=("obs", "feature"),
            coords={"feature": result["descriptor_names"]},
        )
        grouped = container.aggregate(
            by=["sub-01"] * 6 + ["sub-02"] * 6,
            stats=["mean", "std"],
        )

        print("Grouped descriptor shape:", grouped.X.shape)
        print("Grouped dims:", grouped.dims)
        print("Grouped stats:", grouped.coords["stat"].tolist())


    if __name__ == "__main__":
        main()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 4.582 seconds)


.. _sphx_glr_download_auto_examples_descriptors_example.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: descriptors_example.ipynb <descriptors_example.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: descriptors_example.py <descriptors_example.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: descriptors_example.zip <descriptors_example.zip>`


.. only:: html

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

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