.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/example_asr_calibration_sensitivity.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_example_asr_calibration_sensitivity.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_example_asr_calibration_sensitivity.py:


ASR calibration sensitivity
===========================

This tutorial-style example illustrates how ASR results depend on the
calibration segment used during fitting.

We fit ASR on three candidate calibration windows and compare the resulting
channel-wise RMS attenuation. This helps users choose a calibration strategy in
real datasets.

The main question is whether the cleaning result is stable across plausible
calibration windows, or whether one window yields much stronger attenuation.

Uses `meegkit.asr.ASR()`.

References
----------
.. [1] Mullen, T., Kothe, C., Chi, Y., Ojeda, A., Kerth, T., Makeig, S.,
   Jung, T. P., & Cauwenberghs, G. (2015). Real-time neuroimaging and
   cognitive monitoring using wearable dry EEG. IEEE Transactions on
   Biomedical Engineering, 62(11), 2553-2567.

.. GENERATED FROM PYTHON SOURCE LINES 24-35

.. code-block:: Python

    import os

    import matplotlib.pyplot as plt
    import numpy as np

    from meegkit.asr import ASR
    from meegkit.utils.matrix import sliding_window

    raw = np.load(os.path.join("..", "tests", "data", "eeg_raw.npy"))
    sfreq = 250








.. GENERATED FROM PYTHON SOURCE LINES 36-40

Define candidate calibration windows
-----------------------------------------------------------------------------
In practice, calibration quality strongly affects how aggressively ASR
suppresses artifacts. Here we compare three 20-second windows.

.. GENERATED FROM PYTHON SOURCE LINES 40-52

.. code-block:: Python

    windows_sec = {
        "early (0-20s)": (0, 20),
        "middle (10-30s)": (10, 30),
        "late (20-40s)": (20, 40),
    }

    X = sliding_window(raw, window=int(sfreq), step=int(sfreq))
    raw_win = X.reshape(raw.shape[0], -1)
    rms_before = np.sqrt(np.mean(raw_win ** 2, axis=1))

    ratios = {}








.. GENERATED FROM PYTHON SOURCE LINES 53-55

Fit and apply ASR for each candidate window
-----------------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 55-69

.. code-block:: Python

    for label, (start_s, stop_s) in windows_sec.items():
        train_idx = np.arange(start_s * sfreq, stop_s * sfreq, dtype=int)

        asr = ASR(method="euclid")
        asr.fit(raw[:, train_idx])

        Y = np.zeros_like(X)
        for i in range(X.shape[1]):
            Y[:, i, :] = asr.transform(X[:, i, :])

        clean = Y.reshape(raw.shape[0], -1)
        rms_after = np.sqrt(np.mean(clean ** 2, axis=1))
        ratios[label] = rms_after / np.maximum(rms_before, np.finfo(float).eps)








.. GENERATED FROM PYTHON SOURCE LINES 70-75

Visualize channel-wise attenuation by calibration choice
-----------------------------------------------------------------------------
What to look for:
- Ratios below 1 indicate attenuation.
- Large differences across windows suggest sensitivity to calibration period.

.. GENERATED FROM PYTHON SOURCE LINES 75-99

.. code-block:: Python

    fig, ax = plt.subplots(1, 1, figsize=(8, 4))
    channels = np.arange(raw.shape[0])

    for label, ratio in ratios.items():
        ax.plot(channels, ratio, "o-", lw=1.5, label=label)

    ax.axhline(1.0, color="k", ls=":", lw=1)
    ax.set_xticks(channels)
    ax.set_xlabel("Channel")
    ax.set_ylabel("RMS ratio (after / before)")
    ax.set_title("ASR sensitivity to calibration window")
    ax.grid(True, axis="y", ls=":", alpha=.4)
    ax.legend(fontsize="small")
    plt.tight_layout()

    for label, ratio in ratios.items():
        print(f"{label:>16}: median ratio = {np.median(ratio):.3f}")

    best_label = min(ratios, key=lambda key: np.median(ratios[key]))
    print(f"Most aggressive calibration in this example: {best_label}")
    print("Interpretation: large separation between curves means the ASR result")
    print("depends strongly on which segment is treated as clean calibration data.")

    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_example_asr_calibration_sensitivity_001.png
   :alt: ASR sensitivity to calibration window
   :srcset: /auto_examples/images/sphx_glr_example_asr_calibration_sensitivity_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

       early (0-20s): median ratio = 0.942
     middle (10-30s): median ratio = 0.924
       late (20-40s): median ratio = 0.943
    Most aggressive calibration in this example: middle (10-30s)
    Interpretation: large separation between curves means the ASR result
    depends strongly on which segment is treated as clean calibration data.





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

   **Total running time of the script:** (0 minutes 1.308 seconds)


.. _sphx_glr_download_auto_examples_example_asr_calibration_sensitivity.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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