.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/dss/plot_07_spectrogram_dss.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_dss_plot_07_spectrogram_dss.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_dss_plot_07_spectrogram_dss.py:


========================================
Time-Frequency DSS: Spectrogram Masking.
========================================

This example demonstrates DSS for extracting **transient oscillatory bursts**
using time-frequency (TF) domain constraints via spectrogram masking.

We cover **SpectrogramBias** (linear) and **SpectrogramDenoiser** (nonlinear)
for isolating activity that is sparse in the TF domain.

The examples move from synthetic spindle-like bursts to fixed-mask DSS,
adaptive spectrogram denoising, and a real MEG gamma-burst example.

Authors: Sina Esmaeili (sina.esmaeili@umontreal.ca)
         Hamza Abdelhedi (hamza.abdelhedi@umontreal.ca)

.. GENERATED FROM PYTHON SOURCE LINES 20-22

Imports
-------

.. GENERATED FROM PYTHON SOURCE LINES 22-40

.. code-block:: Python


    import matplotlib.pyplot as plt
    import mne
    import numpy as np
    from mne.datasets import somato
    from scipy import signal as sp_signal

    from mne_denoise.dss import DSS, IterativeDSS
    from mne_denoise.dss.denoisers import SpectrogramBias, SpectrogramDenoiser
    from mne_denoise.viz import (
        plot_channel_time_course_comparison,
        plot_component_spectrogram,
        plot_component_summary,
        plot_signal_overlay,
        plot_spectrogram_comparison,
        plot_time_frequency_mask,
    )








.. GENERATED FROM PYTHON SOURCE LINES 41-44

Part 0: Synthetic Transient Bursts (Sleep Spindles)
====================================================
Simulate 12 Hz spindle bursts embedded in noise

.. GENERATED FROM PYTHON SOURCE LINES 44-99

.. code-block:: Python


    print("--- Part 0: Synthetic Spindle Bursts ---")

    rng = np.random.default_rng(42)
    sfreq = 250  # Hz
    n_seconds = 10
    n_times = n_seconds * sfreq
    times = np.arange(n_times) / sfreq

    # Background noise (broadband)
    noise = rng.normal(0, 1.0, n_times)

    # Spindle bursts at specific times
    spindle_freq = 12.0  # Hz
    envelope = np.zeros_like(times)

    # Burst 1: 2-3 seconds
    mask1 = (times >= 2) & (times < 3)
    envelope[mask1] = np.hanning(mask1.sum())

    # Burst 2: 7-8 seconds
    mask2 = (times >= 7) & (times < 8)
    envelope[mask2] = np.hanning(mask2.sum())

    signal_spindle = envelope * np.sin(2 * np.pi * spindle_freq * times) * 3.0

    # Mixed data
    data_mixed = signal_spindle + noise

    # Visualize
    fig, axes = plt.subplots(3, 1, figsize=(14, 8), sharex=True)

    axes[0].plot(times, signal_spindle, "b", linewidth=1.5, label="Clean Spindle")
    axes[0].set_title("Ground Truth: 12 Hz Spindle Bursts")
    axes[0].set_ylabel("Amplitude")
    axes[0].legend()
    axes[0].grid(True, alpha=0.3)

    axes[1].plot(times, noise, "gray", alpha=0.7, label="Broadband Noise")
    axes[1].set_title("Noise")
    axes[1].set_ylabel("Amplitude")
    axes[1].legend()
    axes[1].grid(True, alpha=0.3)

    axes[2].plot(times, data_mixed, "r", alpha=0.7, label="Mixed (Signal + Noise)")
    axes[2].set_title("Observed Data")
    axes[2].set_xlabel("Time (s)")
    axes[2].set_ylabel("Amplitude")
    axes[2].legend()
    axes[2].grid(True, alpha=0.3)

    plt.tight_layout()
    plt.show()





.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_001.png
   :alt: Ground Truth: 12 Hz Spindle Bursts, Noise, Observed Data
   :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    --- Part 0: Synthetic Spindle Bursts ---




.. GENERATED FROM PYTHON SOURCE LINES 100-103

Visualize Time-Frequency Representation
----------------------------------------
Compute spectrogram to see bursts in TF domain

.. GENERATED FROM PYTHON SOURCE LINES 103-119

.. code-block:: Python


    f, t, Sxx = sp_signal.spectrogram(data_mixed, fs=sfreq, nperseg=128, noverlap=96)

    plt.figure(figsize=(12, 5))
    plt.pcolormesh(t, f, 10 * np.log10(Sxx), shading="gouraud", cmap="viridis")
    plt.colorbar(label="Power (dB)")
    plt.ylabel("Frequency (Hz)")
    plt.xlabel("Time (s)")
    plt.title("Spectrogram: Spindle Bursts in Time-Frequency Domain")
    plt.ylim(0, 30)
    plt.axhline(spindle_freq, color="r", linestyle="--", alpha=0.7, label="12 Hz Spindle")
    plt.legend()
    plt.tight_layout()
    plt.show()





.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_002.png
   :alt: Spectrogram: Spindle Bursts in Time-Frequency Domain
   :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 120-123

Part 1: SpectrogramBias with Fixed Mask
========================================
Define a TF mask to isolate burst regions

.. GENERATED FROM PYTHON SOURCE LINES 123-216

.. code-block:: Python


    print("\n--- Part 1: Fixed TF Mask (SpectrogramBias) ---")

    # Create manually defined TF mask for DSS
    # Focus on 10-15 Hz range and burst time windows
    nperseg = 128
    noverlap = 96

    # Get TF grid dimensions
    _, t_grid, _ = sp_signal.spectrogram(
        data_mixed, fs=sfreq, nperseg=nperseg, noverlap=noverlap
    )
    n_freqs = nperseg // 2 + 1
    n_times_tf = len(t_grid)

    # Create mask: 1 where we expect spindles, 0 elsewhere
    mask_fixed = np.zeros((n_freqs, n_times_tf))

    # Define frequency band (10-15 Hz)
    freq_axis = np.fft.rfftfreq(nperseg, 1 / sfreq)
    freq_mask = (freq_axis >= 10) & (freq_axis <= 15)

    # Define time windows for bursts
    time_mask1 = (t_grid >= 2) & (t_grid < 3)
    time_mask2 = (t_grid >= 7) & (t_grid < 8)

    # Apply mask
    mask_fixed[freq_mask[:, None] & (time_mask1 | time_mask2)] = 1.0

    print(f"TF mask shape: {mask_fixed.shape}")
    print(
        f"Masked points: {mask_fixed.sum()} / {mask_fixed.size} "
        f"({100 * mask_fixed.sum() / mask_fixed.size:.1f}%)"
    )

    # Visualize mask
    plot_time_frequency_mask(
        mask_fixed,
        t_grid,
        freq_axis,
        title="SpectrogramBias Mask: 10-15 Hz Spindles",
        show=False,
    )
    plt.show()

    # Apply SPectrogramBias with DSS
    # For demonstration, create multi-channel data
    n_channels = 8
    data_multichan = np.tile(data_mixed, (n_channels, 1)) + rng.normal(
        0, 0.2, (n_channels, n_times)
    )

    # Create MNE Raw with montage for DSS
    ch_names = ["Fz", "Cz", "Pz", "F3", "F4", "C3", "C4", "Oz"]
    info = mne.create_info(ch_names, sfreq, "eeg")
    montage = mne.channels.make_standard_montage("standard_1020")
    info.set_montage(montage)
    raw_spindle = mne.io.RawArray(data_multichan, info)

    # Apply SpectrogramBias
    bias_tf = SpectrogramBias(mask=mask_fixed, nperseg=nperseg, noverlap=noverlap)
    dss_tf = DSS(n_components=3, bias=bias_tf)
    dss_tf.fit(raw_spindle)

    print(f"\nDSS Eigenvalues: {dss_tf.eigenvalues_[:3]}")

    # Visualize DSS components
    plot_component_summary(
        dss_tf,
        data=raw_spindle,
        info=raw_spindle.info,
        picks=np.arange(len(raw_spindle.ch_names)),
        n_components=2,
        show=False,
    )
    plt.gcf().suptitle("SpectrogramBias: DSS Components")
    plt.show()

    # Extract component
    sources_tf = dss_tf.transform(raw_spindle)
    comp0_tf = sources_tf[0]

    # Compare with ground truth
    if np.corrcoef(comp0_tf, signal_spindle)[0, 1] < 0:
        comp0_tf *= -1

    corr_tf = np.corrcoef(comp0_tf, signal_spindle)[0, 1]
    print(f"Correlation with ground truth: {corr_tf:.3f}")

    # Spectrogram comparison
    raw_orig = mne.io.RawArray(data_mixed[np.newaxis, :], mne.create_info(1, sfreq, "eeg"))
    raw_comp = mne.io.RawArray(comp0_tf[np.newaxis, :], mne.create_info(1, sfreq, "eeg"))




.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_003.png
         :alt: SpectrogramBias Mask: 10-15 Hz Spindles
         :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_004.png
         :alt: SpectrogramBias: DSS Components, Comp 0 Pattern, Comp 0 Time Course, PSD, Comp 1 Pattern, Comp 1 Time Course, PSD
         :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_004.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none


    --- Part 1: Fixed TF Mask (SpectrogramBias) ---
    TF mask shape: (65, 75)
    Masked points: 32.0 / 4875 (0.7%)
    Creating RawArray with float64 data, n_channels=8, n_times=2500
        Range : 0 ... 2499 =      0.000 ...     9.996 secs
    Ready.

    DSS Eigenvalues: [0.18872036 0.01285285 0.00731457]
    Correlation with ground truth: 0.512
    Creating RawArray with float64 data, n_channels=1, n_times=2500
        Range : 0 ... 2499 =      0.000 ...     9.996 secs
    Ready.
    Creating RawArray with float64 data, n_channels=1, n_times=2500
        Range : 0 ... 2499 =      0.000 ...     9.996 secs
    Ready.




.. GENERATED FROM PYTHON SOURCE LINES 217-231

.. code-block:: Python

    plot_spectrogram_comparison(
        raw_orig,
        raw_comp,
        picks=[0],
        times=raw_orig.times,
        fmin=5,
        fmax=20,
        show=False,
    )
    plt.gcf().suptitle("Spectrogram Comparison: Original vs Extracted Spindles")
    plt.show()

    # Plot comparison




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_005.png
   :alt: Spectrogram Comparison: Original vs Extracted Spindles, Before, After, Before - After
   :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_005.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 232-243

.. code-block:: Python

    plot_signal_overlay(
        signal_spindle,
        comp0_tf,
        times=times,
        title="Spindle Reconstruction: Ground Truth vs SpectrogramBias Component",
        scale_after=True,
        show=False,
    )
    plt.show()





.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_006.png
   :alt: Spindle Reconstruction: Ground Truth vs SpectrogramBias Component
   :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_006.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 244-247

Part 2: SpectrogramDenoiser + IterativeDSS (Adaptive)
======================================================
Automatically find TF regions with high energy

.. GENERATED FROM PYTHON SOURCE LINES 247-308

.. code-block:: Python


    print("\n--- Part 2: Adaptive TF Masking (SpectrogramDenoiser) ---")

    # SpectrogramDenoiser keeps only top percentile of TF energy
    spec_denoiser = SpectrogramDenoiser(
        threshold_percentile=90,  # Keep top 10%
        nperseg=128,
        noverlap=96,
    )

    # Use with IterativeDSS on original spindle data
    idss_spec = IterativeDSS(denoiser=spec_denoiser, n_components=2, max_iter=3)

    idss_spec.fit(raw_spindle)
    print("IterativeDSS with SpectrogramDenoiser converged")

    sources_idss = idss_spec.transform(raw_spindle)
    comp0_idss = sources_idss[0]

    if np.corrcoef(comp0_idss, signal_spindle)[0, 1] < 0:
        comp0_idss *= -1

    corr_idss = np.corrcoef(comp0_idss, signal_spindle)[0, 1]
    print(f"Correlation with ground truth: {corr_idss:.3f}")

    # Compare both methods
    fig, axes = plt.subplots(3, 1, figsize=(14, 9), sharex=True)

    axes[0].plot(times, signal_spindle, "k", linewidth=2, label="Ground Truth")
    axes[0].set_title("Ground Truth: Spindle Bursts")
    axes[0].set_ylabel("Amplitude")
    axes[0].legend()
    axes[0].grid(True, alpha=0.3)

    axes[1].plot(
        times,
        comp0_tf * (np.std(signal_spindle) / np.std(comp0_tf)),
        "b",
        label=f"Fixed Mask (r={corr_tf:.3f})",
    )
    axes[1].set_title("SpectrogramBias: Fixed TF Mask")
    axes[1].set_ylabel("Amplitude")
    axes[1].legend()
    axes[1].grid(True, alpha=0.3)

    axes[2].plot(
        times,
        comp0_idss * (np.std(signal_spindle) / np.std(comp0_idss)),
        "r",
        label=f"Adaptive Mask (r={corr_idss:.3f})",
    )
    axes[2].set_title("SpectrogramDenoiser: Adaptive TF Masking")
    axes[2].set_xlabel("Time (s)")
    axes[2].set_ylabel("Amplitude")
    axes[2].legend()
    axes[2].grid(True, alpha=0.3)

    plt.tight_layout()
    plt.show()





.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_007.png
   :alt: Ground Truth: Spindle Bursts, SpectrogramBias: Fixed TF Mask, SpectrogramDenoiser: Adaptive TF Masking
   :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_007.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    --- Part 2: Adaptive TF Masking (SpectrogramDenoiser) ---
    IterativeDSS with SpectrogramDenoiser converged
    Correlation with ground truth: 0.111




.. GENERATED FROM PYTHON SOURCE LINES 309-312

Part 3: Real MEG Gamma Bursts (Somato Dataset)
===============================================
Extract transient gamma oscillations

.. GENERATED FROM PYTHON SOURCE LINES 312-363

.. code-block:: Python


    print("\n--- Part 3: Real MEG Data (Gamma Bursts) ---")

    # Download somato dataset (will skip if already downloaded)
    data_path = somato.data_path(verbose=True)
    raw_path = data_path / "sub-01" / "meg" / "sub-01_task-somato_meg.fif"

    raw_somato = mne.io.read_raw_fif(raw_path, preload=True, verbose=False)
    raw_somato.pick_types(meg="grad", eeg=False, eog=False, stim=False, exclude="bads")
    # Use broader band and keep more data
    raw_somato.filter(1, 100, fir_design="firwin", verbose=False)  # Broad band first
    raw_somato.crop(0, 60)  # 60 seconds for more data

    print(f"MEG Data: {len(raw_somato.ch_names)} channels, {raw_somato.times[-1]:.1f}s")

    # Apply SpectrogramDenoiser
    spec_denoiser_meg = SpectrogramDenoiser(
        threshold_percentile=95,  # Top 5% of TF energy
        nperseg=256,
    )

    idss_meg = IterativeDSS(denoiser=spec_denoiser_meg, n_components=3, max_iter=3)

    idss_meg.fit(raw_somato)
    print("\nIterativeDSS on MEG data converged")

    # Visualize (skip topomaps - use time series)
    sources_meg = idss_meg.transform(raw_somato)

    # Create Raw for visualization
    comp_raw_meg = mne.io.RawArray(
        sources_meg[:1], mne.create_info(1, raw_somato.info["sfreq"], "misc")
    )
    raw_single_meg = raw_somato.copy().pick([0])

    # --- Time Course Comparison ---
    plot_channel_time_course_comparison(
        raw_single_meg,
        comp_raw_meg,
        picks=[0],
        times=raw_single_meg.times,
        start=0,
        stop=int(5 * raw_single_meg.info["sfreq"]),
        show=False,
    )
    plt.gcf().suptitle("Real MEG: Original vs TF-DSS Component 0 (Gamma Bursts)")
    plt.show()

    # --- Spectrogram Comparison (Pre/Post) ---
    # Compares broadband raw data vs the broadband component




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_008.png
   :alt: Real MEG: Original vs TF-DSS Component 0 (Gamma Bursts)
   :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_008.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    --- Part 3: Real MEG Data (Gamma Bursts) ---
    Using default location ~/mne_data for somato...
    Fetching 1 file for the somato dataset ...

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    Download complete in 18s (581.8 MB)
    NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
    MEG Data: 204 channels, 60.0s

    IterativeDSS on MEG data converged
    Creating RawArray with float64 data, n_channels=1, n_times=18019
        Range : 0 ... 18018 =      0.000 ...    59.999 secs
    Ready.




.. GENERATED FROM PYTHON SOURCE LINES 364-380

.. code-block:: Python

    plot_spectrogram_comparison(
        raw_single_meg,
        comp_raw_meg,
        picks=[0],
        times=raw_single_meg.times,
        fmin=1,
        fmax=100,
        show=False,
    )
    plt.gcf().suptitle("Spectrogram Comparison: Raw Data vs Extracted Component")
    plt.show()

    # --- Component Spectrogram (Single Component TFR) ---
    # Zoom into the component's frequency content
    freqs = np.arange(10, 50, 2)




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_009.png
   :alt: Spectrogram Comparison: Raw Data vs Extracted Component, Before, After, Before - After
   :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_009.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 381-395

.. code-block:: Python

    plot_component_spectrogram(
        sources_meg[0],
        sfreq=raw_somato.info["sfreq"],
        freqs=freqs,
        title="TF-DSS Component 0: Extracted Transient Oscillations",
        show=False,
    )
    plt.show()

    # --- Full Component Summary (with Topomaps!) ---
    # Now that we have info, we can show topomaps
    # Note: IterativeDSS stores patterns, so plot_component_summary can extract them.
    print("\nPlotting full component dashboard (including Topomaps)...")




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_010.png
   :alt: TF-DSS Component 0: Extracted Transient Oscillations
   :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_010.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    Plotting full component dashboard (including Topomaps)...




.. GENERATED FROM PYTHON SOURCE LINES 396-408

.. code-block:: Python

    plot_component_summary(
        idss_meg,
        data=raw_somato,
        info=raw_somato.info,
        picks=np.arange(len(raw_somato.ch_names)),
        n_components=1,
        show=False,
    )
    plt.gcf().suptitle("TF-DSS Component 0 Dashboard")
    plt.show()

    print("\nSuccessfully extracted transient gamma oscillations using TF masking!")



.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_011.png
   :alt: TF-DSS Component 0 Dashboard, Comp 0 Pattern, Comp 0 Time Course, PSD
   :srcset: /auto_examples/dss/images/sphx_glr_plot_07_spectrogram_dss_011.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    Successfully extracted transient gamma oscillations using TF masking!





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

   **Total running time of the script:** (0 minutes 30.214 seconds)


.. _sphx_glr_download_auto_examples_dss_plot_07_spectrogram_dss.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_07_spectrogram_dss.ipynb <plot_07_spectrogram_dss.ipynb>`

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

      :download:`Download Python source code: plot_07_spectrogram_dss.py <plot_07_spectrogram_dss.py>`

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

      :download:`Download zipped: plot_07_spectrogram_dss.zip <plot_07_spectrogram_dss.zip>`