{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Compute source power spectral density (PSD) in a label\n\nReturns an STC file containing the PSD (in dB) of each of the sources\nwithin a label.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n\nimport mne\nfrom mne import io\nfrom mne.datasets import sample\nfrom mne.minimum_norm import compute_source_psd, read_inverse_operator\n\nprint(__doc__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Set parameters\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "data_path = sample.data_path()\nmeg_path = data_path / \"MEG\" / \"sample\"\nraw_fname = meg_path / \"sample_audvis_raw.fif\"\nfname_inv = meg_path / \"sample_audvis-meg-oct-6-meg-inv.fif\"\nfname_label = meg_path / \"labels\" / \"Aud-lh.label\"\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname, verbose=False)\nevents = mne.find_events(raw, stim_channel=\"STI 014\")\ninverse_operator = read_inverse_operator(fname_inv)\nraw.info[\"bads\"] = [\"MEG 2443\", \"EEG 053\"]\n\n# picks MEG gradiometers\npicks = mne.pick_types(\n    raw.info, meg=True, eeg=False, eog=True, stim=False, exclude=\"bads\"\n)\n\ntmin, tmax = 0, 120  # use the first 120s of data\nfmin, fmax = 4, 100  # look at frequencies between 4 and 100Hz\nn_fft = 2048  # the FFT size (n_fft). Ideally a power of 2\nlabel = mne.read_label(fname_label)\n\nstc = compute_source_psd(\n    raw,\n    inverse_operator,\n    lambda2=1.0 / 9.0,\n    method=\"dSPM\",\n    tmin=tmin,\n    tmax=tmax,\n    fmin=fmin,\n    fmax=fmax,\n    pick_ori=\"normal\",\n    n_fft=n_fft,\n    label=label,\n    dB=True,\n)\n\nstc.save(\"psd_dSPM\", overwrite=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "View PSD of sources in label\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plt.plot(stc.times, stc.data.T)\nplt.xlabel(\"Frequency (Hz)\")\nplt.ylabel(\"PSD (dB)\")\nplt.title(\"Source Power Spectrum (PSD)\")\nplt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}