{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Finite element model from labels\n",
    "================================\n",
    "\n",
    "The main problems in *shamo* depend on a finite element model to serve as a support for the computation.\n",
    "Generating such a model can be achieved in a few lines of code.\n",
    "\n",
    "To start, we import the :py:class:`~shamo.FEM` class and initialize a model with a name and a parent directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shamo import FEM\n",
    "\n",
    "model = FEM(\"fem_from_labels_sensors-circle\", \"../../derivatives\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Mesh generation\n",
    "---------------\n",
    "\n",
    "Several methods can be used ot generate the mesh:\n",
    "\n",
    "* :py:func:`~shamo.FEM.mesh_from_array`\n",
    "* :py:func:`~shamo.FEM.mesh_from_nii`\n",
    "* :py:func:`~shamo.FEM.mesh_from_masks`\n",
    "* :py:func:`~shamo.FEM.mesh_from_niis`\n",
    "\n",
    "All those methods skip the step of generating surfaces and allow us to directly produce a high definition mesh from the segmented image.\n",
    "Here, we start from a labelled volume created from the well known `Suzanne model from Blender <https://www.dummies.com/web-design-development/meet-suzanne-the-blender-monkey/>`_.\n",
    "\n",
    ".. note::\n",
    "   Several parameters can be used to refine the mesh. Make sure to tweak them to obtain the mesh you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "data_path = Path(\"../../rawdata/suzanne\")\n",
    "model.mesh_from_nii(\n",
    "    data_path / \"suzanne_labels.nii\", \n",
    "    [\"wm\", \"gm\", \"scalp\"], \n",
    "    max_facet_distance=0.25, \n",
    "    max_cell_circumradius=5.0, \n",
    "    min_facet_angle=20,\n",
    ")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "After this first step, a mesh built of triangles and tetrahedra and annotated with the name of the tissues is saved to the directory of the object.\n",
    "\n",
    "Sensors additions\n",
    "-----------------\n",
    "\n",
    "Sensors can be added to the mesh with the following methods:\n",
    "\n",
    "* :py:func:`~shamo.FEM.add_point_sensor` and the partials :py:func:`~shamo.FEM.add_point_sensor_on` and :py:func:`~shamo.FEM.add_point_sensor_in`\n",
    "* :py:func:`~shamo.FEM.add_point_sensors` and the partials :py:func:`~shamo.FEM.add_point_sensors_on` and :py:func:`~shamo.FEM.add_point_sensors_in`\n",
    "* :py:func:`~shamo.FEM.add_point_sensors_from_tsv` and the partials :py:func:`~shamo.FEM.add_point_sensors_from_tsv_on` and :py:func:`~shamo.FEM.add_point_sensors_from_tsv_in`\n",
    "\n",
    "In the case of Suzanne, the electrodes are defined in a TSV file with the format:\n",
    "\n",
    ".. list-table:: Electrodes TSV file\n",
    "   :widths: 25 25 25 25\n",
    "   :header-rows: 1\n",
    "\n",
    "   * - Name\n",
    "     - X\n",
    "     - Y\n",
    "     - Z\n",
    "   * - NZ\n",
    "     - 1.65\n",
    "     - -42.78\n",
    "     - -7.54\n",
    "   * - ...\n",
    "     - ...\n",
    "     - ...\n",
    "     - ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "model.add_circle_sensors_from_tsv_on(data_path / \"suzanne_sensors.tsv\", \"scalp\", 0.005)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "shamo",
   "language": "python",
   "name": "shamo"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}