{
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    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "%matplotlib inline"
      ], 
      "outputs": [], 
      "metadata": {
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      }
    }, 
    {
      "source": [
        "\n# Sensitivity map of SSP projections\n\n\nThis example shows the sources that have a forward field\nsimilar to the first SSP vector correcting for ECG.\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n#\n# License: BSD (3-clause)\n\nimport 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'\nfname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\necg_fname = data_path + '/MEG/sample/sample_audvis_ecg-proj.fif'\n\nfwd = read_forward_solution(fname, surf_ori=True)\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')"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Show sensitivity map\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "plt.hist(ssp_ecg_map.data.ravel())\nplt.show()\n\nargs = dict(clim=dict(kind='value', lims=(0.2, 0.6, 1.)), smoothing_steps=7,\n            hemi='rh', subjects_dir=subjects_dir)\nssp_ecg_map.plot(subject='sample', time_label='ECG SSP sensitivity', **args)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }
  ], 
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