{
  "nbformat_minor": 0, 
  "nbformat": 4, 
  "cells": [
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "%matplotlib inline"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "\n# Display sensitivity maps for EEG and MEG sensors\n\n\nSensitivity maps can be produced from forward operators that\nindicate how well different sensor types will be able to detect\nneural currents from different regions of the brain.\n\nTo get started with forward modeling see ref:`tut_forward`.\n\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Author: Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne.datasets import sample\nimport matplotlib.pyplot as plt\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nraw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'\nfwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\n\nsubjects_dir = data_path + '/subjects'\n\n# Read the forward solutions with surface orientation\nfwd = mne.read_forward_solution(fwd_fname, surf_ori=True)\nleadfield = fwd['sol']['data']\nprint(\"Leadfield size : %d x %d\" % leadfield.shape)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Compute sensitivity maps\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "grad_map = mne.sensitivity_map(fwd, ch_type='grad', mode='fixed')\nmag_map = mne.sensitivity_map(fwd, ch_type='mag', mode='fixed')\neeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed')"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Show gain matrix a.k.a. leadfield matrix with sensitivity map\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "picks_meg = mne.pick_types(fwd['info'], meg=True, eeg=False)\npicks_eeg = mne.pick_types(fwd['info'], meg=False, eeg=True)\n\nfig, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)\nfig.suptitle('Lead field matrix (500 dipoles only)', fontsize=14)\nfor ax, picks, ch_type in zip(axes, [picks_meg, picks_eeg], ['meg', 'eeg']):\n    im = ax.imshow(leadfield[picks, :500], origin='lower', aspect='auto',\n                   cmap='RdBu_r')\n    ax.set_title(ch_type.upper())\n    ax.set_xlabel('sources')\n    ax.set_ylabel('sensors')\n    plt.colorbar(im, ax=ax, cmap='RdBu_r')\nplt.show()\n\nplt.figure()\nplt.hist([grad_map.data.ravel(), mag_map.data.ravel(), eeg_map.data.ravel()],\n         bins=20, label=['Gradiometers', 'Magnetometers', 'EEG'],\n         color=['c', 'b', 'k'])\nplt.legend()\nplt.title('Normal orientation sensitivity')\nplt.xlabel('sensitivity')\nplt.ylabel('count')\nplt.show()\n\ngrad_map.plot(time_label='Gradiometer sensitivity', subjects_dir=subjects_dir,\n              clim=dict(lims=[0, 50, 100]))"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }
  ], 
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    "kernelspec": {
      "display_name": "Python 2", 
      "name": "python2", 
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