{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import k3d\n",
    "import numpy as np\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "plot = k3d.plot()\n",
    "\n",
    "N = 10000\n",
    "vertices = np.random.normal(size=(N, 3)).astype(np.float32)\n",
    "\n",
    "nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(vertices)\n",
    "distances, indices = nbrs.kneighbors(vertices)\n",
    "\n",
    "obj = k3d.mesh(vertices, indices.astype(np.uint32), side='double')\n",
    "plot +=  obj\n",
    "\n",
    "plot.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "triangles_attribute = []\n",
    "\n",
    "for ind in indices:\n",
    "    v = vertices[ind]\n",
    "    triangles_attribute.append(np.linalg.norm(np.cross(v[2] - v[1], v[0] - v[1])) * 0.5)\n",
    "\n",
    "triangles_attribute = np.array(triangles_attribute, dtype=np.float32)\n",
    "\n",
    "obj.triangles_attribute = triangles_attribute\n",
    "obj.color_range = (np.max(triangles_attribute) / 8, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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