02-working_with_BIDS_data.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with BIDS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import pathlib\n",
    "\n",
    "import mne\n",
    "import mne_bids\n",
    "\n",
    "matplotlib.use('Qt5Agg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the input data we wish to convert to BIDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_dir = mne.datasets.sample.data_path()\n",
    "sample_data_dir = pathlib.Path(sample_data_dir)\n",
    "\n",
    "raw_path = sample_data_dir / 'MEG' / 'sample' / 'sample_audvis_raw.fif'\n",
    "raw = mne.io.read_raw(raw_path)\n",
    "\n",
    "events = mne.find_events(raw)\n",
    "event_id = {\n",
    "    'Auditory/Left': 1,\n",
    "    'Auditory/Right': 2,\n",
    "    'Visual/Left': 3,\n",
    "    'Visual/Right': 4,\n",
    "    'Smiley': 5,\n",
    "    'Button': 32\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write the raw data to BIDS!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to specify the power line frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.info['line_freq'] = 60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, actually write the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_path = pathlib.Path('out_data/sample_BIDS')\n",
    "\n",
    "bids_path = mne_bids.BIDSPath(subject='01',\n",
    "                              session='01',\n",
    "                              task='audiovisual',\n",
    "                              run='01',\n",
    "                              root=out_path)\n",
    "\n",
    "mne_bids.write_raw_bids(raw, bids_path=bids_path, events_data=events,\n",
    "                        event_id=event_id, overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write MEGIN / Elekta / NeuroMag fine-calibration and crosstalk files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal_path = sample_data_dir / 'SSS' / 'sss_cal_mgh.dat'\n",
    "ct_path = sample_data_dir / 'SSS' / 'ct_sparse_mgh.fif'\n",
    "\n",
    "mne_bids.write_meg_calibration(cal_path, bids_path=bids_path)\n",
    "mne_bids.write_meg_crosstalk(ct_path, bids_path=bids_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>:\n",
    "     <ul>\n",
    "         <li>Write the same raw data again, but this time act as if it were from a different subject – subject \"02\".</li>\n",
    "    </ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print the created file & directory structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mne_bids.print_dir_tree(out_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate a data summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(mne_bids.make_report(out_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading BIDS data\n",
    "\n",
    "Let's try to read back the data we just stored according to the BIDS specification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_root = pathlib.Path('out_data/sample_BIDS')\n",
    "\n",
    "bids_path = mne_bids.BIDSPath(subject='01',\n",
    "                              session='01',\n",
    "                              task='audiovisual',\n",
    "                              run='01',\n",
    "                              datatype='meg',\n",
    "                              root=bids_root)\n",
    "\n",
    "raw = mne_bids.read_raw_bids(bids_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Events are stored as annotations – but we convert between the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.annotations[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "events, event_id = mne.events_from_annotations(raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mne.viz.plot_events(events, event_id=event_id, sfreq=raw.info['sfreq'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine the MEG fine-calibration and crosstalk files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_path.meg_calibration_fpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_path.meg_crosstalk_fpath"
   ]
  }
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