mne_example.ipynb

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   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "\n",
    "import mne\n",
    "import nibabel\n",
    "\n",
    "# TODO:\n",
    "# 1. FIX .clim setting breakage\n",
    "# 2. add mne_analyze_colormap as colormap\n",
    "# 3. add brain behind STC\n",
    "\n",
    "data_path = mne.datasets.sample.data_path()\n",
    "subjects_dir = data_path + '/subjects'\n",
    "subj_dir = subjects_dir + '/sample/surf/'\n",
    "stc = mne.read_source_estimate(data_path + '/MEG/sample/sample_audvis-meg-eeg')\n",
    "stc.crop(0.09, 0.09)\n",
    "stc = stc.morph('sample', grade=None, smooth=10, subjects_dir=subjects_dir,\n",
    "                subject_from='sample')\n",
    "\n",
    "azimuths = dict(lh=dict(lat=270, med=90), rh=dict(lat=90, med=270))\n",
    "hemis = ['lh']\n",
    "views = ['lat']\n",
    "hemi_data = dict(lh=stc.data[:len(stc.vertices[0]), 0],\n",
    "                 rh=stc.data[len(stc.vertices[0]):, 0])\n",
    "\n",
    "for hi, hemi in enumerate(hemis):\n",
    "    data = hemi_data[hemi]\n",
    "    data /= 23\n",
    "    surf = subj_dir + '%s.inflated' % hemi\n",
    "    curv = subj_dir + '%s.curv' % hemi\n",
    "    rr, tris = nibabel.freesurfer.read_geometry(surf)\n",
    "    tris = tris.astype(np.uint32)\n",
    "    assert len(data) == len(rr)\n",
    "    curv = nibabel.freesurfer.read_morph_data(curv)\n",
    "    curv = (curv > 0).astype(float)\n",
    "    curv = (curv - 0.5) / 3 + 0.5\n",
    "    curv = curv[:, np.newaxis] * [1, 1, 1]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rr.shape, tris.shape, curv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pythreejs import *\n",
    "import numpy as np\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vertices = rr\n",
    "faces = tris\n",
    "vertexcolors = curv\n",
    "geom = PlainBufferGeometry(vertices=vertices, faces=faces, colors=vertexcolors)\n",
    "mesh = Mesh(geometry=geom, material=PhongMaterial(shininess=0,vertexColors='VertexColors'))\n",
    "camera = PerspectiveCamera(position=[500, 500, 500], fov=20,\n",
    "                           children=[DirectionalLight(color='#ffffff',\n",
    "                                                      position=[-3, 5, 1],\n",
    "                                                      intensity=0.5)])\n",
    "scene = Scene(children=[mesh, AmbientLight(color='#dddddd')])\n",
    "\n",
    "renderer = Renderer(camera=camera, background='black', background_opacity=1,\n",
    "                    scene=scene, controls=[OrbitControls(controlling=camera)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(renderer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "norm = np.linalg.norm(vertices, axis=1)\n",
    "norm /= norm.max()\n",
    "def color(n):\n",
    "    newcolor=curv.copy()\n",
    "    newcolor[:,0] = np.abs(np.sin(norm*n))\n",
    "    newcolor[:,2] = np.abs(np.cos(norm*n))\n",
    "    geom.colors = newcolor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "color(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import FloatSlider, VBox\n",
    "x = FloatSlider(min=0, max=12)\n",
    "def slide(change):\n",
    "    color(x.value)\n",
    "x.observe(slide)\n",
    "display(VBox([x, renderer]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "camera.position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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