{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f2aeb62",
   "metadata": {},
   "source": [
    "# Cloud vertices, What is it ?\n",
    "\n",
    "## Basics \n",
    "\n",
    "Cloud vertices is a synonym for point cloud. It is a group of points in a 3D space.\n",
    "\n",
    "It can be initialized from:\n",
    "\n",
    "- a list of vertices\n",
    "- a numpy array (shape (N, 3))\n",
    "- an Excel file\n",
    "- a CSV file (with/without header)\n",
    "- a Shape file (point type)\n",
    "- a DXF file (MTEXT or INSERT)\n",
    "\n",
    "Each point can have associated values.\n",
    "\n",
    "## Storage\n",
    "\n",
    "All vertices are stored in a dictionnary. The main key is the index of the point in the list. \n",
    "\n",
    "Each entry is a dictionnary with at least the following key : `vertex` (the `wolf_vertex` instance).\n",
    "\n",
    "Other keys can be added to store additional information.\n",
    "\n",
    "## KD-tree\n",
    "\n",
    "A KD-tree is a data structure that allows for efficient nearest neighbor searches in a multi-dimensional space. It is particularly useful for spatial queries, such as finding the closest points in a point cloud.\n",
    "\n",
    "Each instance of `cloud_vertices` has a KD-tree associated with it. The KD-tree is built using the vertices of the point cloud, and it allows for fast nearest neighbor searches.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72cdbc36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import _add_path # this line can be deleted if wolfhece is installed as a package (-> debug mode only)\n",
    "\n",
    "from wolfhece import __version__\n",
    "assert __version__ > '2.2.8', f'Bad version of wolfhece: {__version__} <= 2.2.8'\n",
    "\n",
    "from wolfhece.PyVertex import cloud_vertices, wolfvertex\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d31aa5",
   "metadata": {},
   "source": [
    "## Create a new cloud_vertices instance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ef57f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "vertices = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]])\n",
    "\n",
    "cloud = cloud_vertices()\n",
    "\n",
    "cloud.init_from_nparray(vertices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc2cf58f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "False\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "# number of vertices\n",
    "print(cloud.nbvertices)\n",
    "\n",
    "# Header information\n",
    "print(cloud.has_values)\n",
    "print(cloud.header)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d6f4dc",
   "metadata": {},
   "source": [
    "## Get coordinates of the vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "856f63b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0.]\n",
      " [1. 1. 1.]\n",
      " [2. 2. 2.]\n",
      " [3. 3. 3.]\n",
      " [4. 4. 4.]]\n"
     ]
    }
   ],
   "source": [
    "xyz = cloud.xyz # -> np.ndarray with shape (n,3)\n",
    "\n",
    "print(xyz)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e71f649",
   "metadata": {},
   "source": [
    "## Add a value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55190f85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['vertex', 'test']\n"
     ]
    }
   ],
   "source": [
    "new_val = np.array([1, 2, 3, 4, 5])\n",
    "\n",
    "cloud.add_values_by_id_list('test', new_val)\n",
    "\n",
    "print(cloud.header)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555d0124",
   "metadata": {},
   "source": [
    "## Get coordinates but value as z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4713f371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 1.]\n",
      " [1. 1. 2.]\n",
      " [2. 2. 3.]\n",
      " [3. 3. 4.]\n",
      " [4. 4. 5.]]\n"
     ]
    }
   ],
   "source": [
    "xyz_val = cloud.get_xyz('test')\n",
    "\n",
    "print(xyz_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08be8e1",
   "metadata": {},
   "source": [
    "## Find the nearest vertex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "320212ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distance: 0.7071067811865476\n",
      "vertex: 0.0 0.0 0.0\n",
      "key: {'vertex': <wolfhece.PyVertex.wolfvertex object at 0x000002B1087DA4A0>, 'test': np.int64(1)}\n"
     ]
    }
   ],
   "source": [
    "nb = 1\n",
    "dist, vert, assoc_dict = cloud.find_nearest([[.5, .5, 0.]], nb= nb)\n",
    "\n",
    "# If the number of neighbors is 1, the output is a single list of vertices and distances\n",
    "# If the number of neighbors is greater than 1, the output is a list of lists of vertices and distances\n",
    "\n",
    "print('distance:', dist)\n",
    "print('vertex:', vert.x, vert.y, vert.z)\n",
    "print('key:', assoc_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69c747ec",
   "metadata": {},
   "source": [
    "## Find the nearest vertices\n",
    "\n",
    "nb = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cdb540cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of neighbors: 2\n",
      "distance: 0.7071067811865476\n",
      "vertex: 0.0 0.0 0.0\n",
      "key: {'vertex': <wolfhece.PyVertex.wolfvertex object at 0x000002B1087DA4A0>, 'test': np.int64(1)}\n",
      "distance: 1.224744871391589\n",
      "vertex: 1.0 1.0 1.0\n",
      "key: {'vertex': <wolfhece.PyVertex.wolfvertex object at 0x000002B128E67DF0>, 'test': np.int64(2)}\n"
     ]
    }
   ],
   "source": [
    "nb = 2\n",
    "dist, vert, assoc_dict = cloud.find_nearest(np.asarray([[.5, .5, 0.]]), nb= nb)\n",
    "\n",
    "# If the number of neighbors is 1, the output is a single list of vertices and distances\n",
    "# If the number of neighbors is greater than 1, the output is a list of lists of vertices and distances\n",
    "\n",
    "print('number of neighbors:', len(dist))\n",
    "\n",
    "for i in range(nb):\n",
    "    print('distance:', dist[i])\n",
    "    print('vertex:', vert[i].x, vert[i].y, vert[i].z)\n",
    "    print('key:', assoc_dict[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d819d9d1",
   "metadata": {},
   "source": [
    "## Find the nearest vertex for multiple points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6644100b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distance: 1.118033988749895\n",
      "vertex: 1.0 1.0 1.0\n",
      "key: {'vertex': <wolfhece.PyVertex.wolfvertex object at 0x000002B128E67DF0>, 'test': np.int64(2)}\n",
      "distance: 1.118033988749895\n",
      "vertex: 0.0 0.0 0.0\n",
      "key: {'vertex': <wolfhece.PyVertex.wolfvertex object at 0x000002B1087DA4A0>, 'test': np.int64(1)}\n",
      "distance: 1.118033988749895\n",
      "vertex: 1.0 1.0 1.0\n",
      "key: {'vertex': <wolfhece.PyVertex.wolfvertex object at 0x000002B128E67DF0>, 'test': np.int64(2)}\n",
      "distance: 1.8027756377319946\n",
      "vertex: 2.0 2.0 2.0\n",
      "key: {'vertex': <wolfhece.PyVertex.wolfvertex object at 0x000002B128E66350>, 'test': np.int64(3)}\n"
     ]
    }
   ],
   "source": [
    "nb = 2\n",
    "xyz1 = [.5, 1., 0.]\n",
    "xyz2 = [.5, 2., 1.]\n",
    "dist, vert, assoc_dict = cloud.find_nearest([xyz1, xyz2], nb= nb)\n",
    "\n",
    "# If the number of neighbors is 1, the output is a single list of vertices and distances\n",
    "# If the number of neighbors is greater than 1, the output is a list of lists of vertices and distances\n",
    "\n",
    "if nb ==1:\n",
    "    for i_vert in range(2):\n",
    "        print('distance:', dist[i_vert])\n",
    "        print('vertex:', vert[i_vert].x, vert[i_vert].y, vert[i_vert].z)\n",
    "        print('key:', assoc_dict[i_vert])\n",
    "else:\n",
    "    for i_vert in range(2):\n",
    "        for i in range(nb):\n",
    "            dist_list = dist[i_vert]\n",
    "            vert_list = vert[i_vert]\n",
    "            assoc_dict_list = assoc_dict[i_vert]\n",
    "\n",
    "            print('distance:', dist_list[i])\n",
    "            print('vertex:', vert_list[i].x, vert_list[i].y, vert_list[i].z)\n",
    "            print('key:', assoc_dict_list[i])"
   ]
  }
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