{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Sensitivity map of SSP projections\n\nThis example shows the sources that have a forward field\nsimilar to the first SSP vector correcting for ECG.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Author: 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\nfrom mne import read_forward_solution, read_proj, sensitivity_map\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nsubjects_dir = data_path / \"subjects\"\nmeg_path = data_path / \"MEG\" / \"sample\"\nfname = meg_path / \"sample_audvis-meg-eeg-oct-6-fwd.fif\"\necg_fname = meg_path / \"sample_audvis_ecg-proj.fif\"\n\nfwd = read_forward_solution(fname)\n\nprojs = read_proj(ecg_fname)\n# take only one projection per channel type\nprojs = projs[::2]\n\n# Compute sensitivity map\nssp_ecg_map = sensitivity_map(fwd, ch_type=\"grad\", projs=projs, mode=\"angle\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Show sensitivity map\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plt.hist(ssp_ecg_map.data.ravel())\nplt.show()\n\nargs = dict(\n    clim=dict(kind=\"value\", lims=(0.2, 0.6, 1.0)),\n    smoothing_steps=7,\n    hemi=\"rh\",\n    subjects_dir=subjects_dir,\n)\nssp_ecg_map.plot(subject=\"sample\", time_label=\"ECG SSP sensitivity\", **args)"
      ]
    }
  ],
  "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"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
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