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   "source": [
    "EEG leadfield computation - On regular grid\n",
    "===========================================\n",
    "\n",
    "The leadfield matrix can be computed on different source spaces in *shamo*. In this example, we compute it on a regular grid that can be processed as a NIFTI image.\n",
    "\n",
    "The first steps are the same as in the previous example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import sys\n",
    "\n",
    "logger = logging.getLogger(\"shamo\")\n",
    "handler = logging.StreamHandler(sys.stdout)\n",
    "handler.setFormatter(logging.Formatter(\"[{levelname}] {message}\", style=\"{\"))\n",
    "logger.addHandler(handler)\n",
    "logger.setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shamo import FEM\n",
    "\n",
    "model = FEM.load(\"../../derivatives/fem_from_labels/fem_from_labels.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shamo.eeg import ProbEEGLeadfield\n",
    "\n",
    "problem = ProbEEGLeadfield()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem.sigmas.set(\"scalp\", 0.4137)\n",
    "problem.sigmas.set(\"gm\", 0.4660)\n",
    "problem.sigmas.set(\"wm\", 0.2126)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem.reference.add(\"IZ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem.markers.adds([\"NZ\", \"LeftEar\", \"RightEar\"])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Grid definition\n",
    "---------------\n",
    "\n",
    "The grid is stored as a :py:class:`~shamo.core.problems.single.components.grid_sampler.CompGridSampler` instance. It provides the :py:func:`~shamo.core.problems.single.components.grid_sampler.CompGridSampler.set` method which requires an affine matrix and a shape. An additional mask can be set to reduce the region of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nibabel as nib\n",
    "from nilearn.image import crop_img\n",
    "\n",
    "img = nib.load(model.nii_path)\n",
    "mask = img.get_fdata() == 2\n",
    "mask_img = nib.Nifti1Image(mask.astype(int), img.affine)\n",
    "mask_img = crop_img(mask_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem.grid.set(\n",
    "    mask_img.affine, \n",
    "    mask_img.shape, \n",
    "    mask=mask_img.get_fdata().astype(bool)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = problem.solve(\"single_ssp-grid\", \"../../derivatives/eeg_leadfield\", model)"
   ]
  }
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