examples/04_spatial_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import movekit as mkit\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial import Voronoi, voronoi_plot_2d, convex_hull_plot_2d, delaunay_plot_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Extracting all absolute features: 100%|██████████| 100.0/100 [00:01<00:00, 73.58it/s]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>animal_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>distance</th>\n",
       "      <th>direction</th>\n",
       "      <th>turning</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_acceleration</th>\n",
       "      <th>stopped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>312</td>\n",
       "      <td>405.29</td>\n",
       "      <td>417.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115306</td>\n",
       "      <td>0.005298</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>369.99</td>\n",
       "      <td>428.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012708</td>\n",
       "      <td>0.002823</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>607</td>\n",
       "      <td>390.33</td>\n",
       "      <td>405.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.045118</td>\n",
       "      <td>0.003392</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>811</td>\n",
       "      <td>445.15</td>\n",
       "      <td>411.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232790</td>\n",
       "      <td>0.026729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>905</td>\n",
       "      <td>366.06</td>\n",
       "      <td>451.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.067000</td>\n",
       "      <td>0.001639</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  animal_id       x       y  distance   direction  turning  \\\n",
       "0     1        312  405.29  417.76       0.0  (0.0, 0.0)      0.0   \n",
       "1     1        511  369.99  428.78       0.0  (0.0, 0.0)      0.0   \n",
       "2     1        607  390.33  405.89       0.0  (0.0, 0.0)      0.0   \n",
       "3     1        811  445.15  411.94       0.0  (0.0, 0.0)      0.0   \n",
       "4     1        905  366.06  451.76       0.0  (0.0, 0.0)      0.0   \n",
       "\n",
       "   average_speed  average_acceleration  stopped  \n",
       "0       0.115306              0.005298        1  \n",
       "1       0.012708              0.002823        1  \n",
       "2       0.045118              0.003392        1  \n",
       "3       0.232790              0.026729        1  \n",
       "4       0.067000              0.001639        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"datasets/fish-5-cleaned.csv\"\n",
    "data = mkit.read_data(path)\n",
    "data = mkit.extract_features(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatial Objects\n",
    "The function below produces three types of spatial objects: Voronoi-Diagrams, Convex Hulls and Delaunay Triangles. Optionally, one may obtain only group-specific outputs - one object per time-capture. These spatial objects may be used for a set of attributes, which are described below in detail.\n",
    "To get the full functionality of all attributes, please go to https://docs.scipy.org/doc/scipy-0.14.0/reference/tutorial/spatial.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating spatial objects: 100%|██████████| 1000/1000 [00:09<00:00, 102.57it/s]\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>convex_hull_object</th>\n",
       "      <th>voronoi_object</th>\n",
       "      <th>delaunay_object</th>\n",
       "      <th>convex_hull_volume</th>\n",
       "      <th>voronoi_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>&lt;scipy.spatial._qhull.ConvexHull object at 0x7...</td>\n",
       "      <td>&lt;scipy.spatial._qhull.Voronoi object at 0x7fa9...</td>\n",
       "      <td>&lt;scipy.spatial._qhull.Delaunay object at 0x7fa...</td>\n",
       "      <td>1519.44120</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>&lt;scipy.spatial._qhull.ConvexHull object at 0x7...</td>\n",
       "      <td>&lt;scipy.spatial._qhull.Voronoi object at 0x7fa9...</td>\n",
       "      <td>&lt;scipy.spatial._qhull.Delaunay object at 0x7fa...</td>\n",
       "      <td>1528.95525</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>&lt;scipy.spatial._qhull.ConvexHull object at 0x7...</td>\n",
       "      <td>&lt;scipy.spatial._qhull.Voronoi object at 0x7fa9...</td>\n",
       "      <td>&lt;scipy.spatial._qhull.Delaunay object at 0x7fa...</td>\n",
       "      <td>1539.23800</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>&lt;scipy.spatial._qhull.ConvexHull object at 0x7...</td>\n",
       "      <td>&lt;scipy.spatial._qhull.Voronoi object at 0x7fa9...</td>\n",
       "      <td>&lt;scipy.spatial._qhull.Delaunay object at 0x7fa...</td>\n",
       "      <td>1549.61380</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>&lt;scipy.spatial._qhull.ConvexHull object at 0x7...</td>\n",
       "      <td>&lt;scipy.spatial._qhull.Voronoi object at 0x7fa9...</td>\n",
       "      <td>&lt;scipy.spatial._qhull.Delaunay object at 0x7fa...</td>\n",
       "      <td>1560.33905</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time                                 convex_hull_object  \\\n",
       "0     1  <scipy.spatial._qhull.ConvexHull object at 0x7...   \n",
       "1     2  <scipy.spatial._qhull.ConvexHull object at 0x7...   \n",
       "2     3  <scipy.spatial._qhull.ConvexHull object at 0x7...   \n",
       "3     4  <scipy.spatial._qhull.ConvexHull object at 0x7...   \n",
       "4     5  <scipy.spatial._qhull.ConvexHull object at 0x7...   \n",
       "\n",
       "                                      voronoi_object  \\\n",
       "0  <scipy.spatial._qhull.Voronoi object at 0x7fa9...   \n",
       "1  <scipy.spatial._qhull.Voronoi object at 0x7fa9...   \n",
       "2  <scipy.spatial._qhull.Voronoi object at 0x7fa9...   \n",
       "3  <scipy.spatial._qhull.Voronoi object at 0x7fa9...   \n",
       "4  <scipy.spatial._qhull.Voronoi object at 0x7fa9...   \n",
       "\n",
       "                                     delaunay_object  convex_hull_volume  \\\n",
       "0  <scipy.spatial._qhull.Delaunay object at 0x7fa...          1519.44120   \n",
       "1  <scipy.spatial._qhull.Delaunay object at 0x7fa...          1528.95525   \n",
       "2  <scipy.spatial._qhull.Delaunay object at 0x7fa...          1539.23800   \n",
       "3  <scipy.spatial._qhull.Delaunay object at 0x7fa...          1549.61380   \n",
       "4  <scipy.spatial._qhull.Delaunay object at 0x7fa...          1560.33905   \n",
       "\n",
       "   voronoi_volume  \n",
       "0             inf  \n",
       "1             inf  \n",
       "2             inf  \n",
       "3             inf  \n",
       "4             inf  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spatial_obj = mkit.get_spatial_objects(data, group_output = True)\n",
    "spatial_obj.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Producing a voronoi diagram\n",
    "Each timestep gets a voronoi object as well as the area of the voronoi - shape. Infinity, if respective animal is outmost in swarm.\n",
    "\n",
    "Voronoi object contains the following attributes:\n",
    "\n",
    "- `points` Coordinates of input points.\n",
    "\n",
    "- `vertices` Coordinates of the Voronoi vertices.\n",
    "\n",
    "- `ridge_points` Indices of the points between which each Voronoi ridge lies.\n",
    "\n",
    "- `ridge_vertices` Indices of the Voronoi vertices forming each Voronoi ridge.\n",
    "\n",
    "- `regions` Indices of the Voronoi vertices forming each Voronoi region. -1 indicates vertex outside the Voronoi diagram.\n",
    "\n",
    "- `point_region` Index of the Voronoi region for each input point. If qhull option “Qc” was not specified, the list will contain -1 for points that are not associated with a Voronoi region.\n",
    "\n",
    "- `furthest_site` True if this was a furthest site triangulation and False if not.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Producing a convex hull\n",
    "Similar to voronoi-diagram, also the convex hull is of interest for movement-related data. Additionally, a set of attributes may be attached to the convex hull objects. Examples are:\n",
    "\n",
    "- `points` (array of ints) Coordinates of input points\n",
    "\n",
    "- `vertices` (array of ints ) Point indices forming the veritces of the convex hull\n",
    "\n",
    "- `area` Area of the convex hull.\n",
    "\n",
    "- `volume` Volume of the convex hull."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Producing Delaunay-triangles\n",
    "Examples for attributes of interest consist of:\n",
    "\n",
    "- `transform`\tAffine transform from x to the barycentric coordinates c.\n",
    "- `vertex_to_simplex`\tLookup array, from a vertex, to some simplex which it is a part of.\n",
    "- `convex_hull`\tVertices of facets forming the convex hull of the point set.\n",
    "- `vertex_neighbor_vertices`\tNeighboring vertices of vertices.\n",
    "- `points`\t(ndarray of double, shape (npoints, ndim)) Coordinates of input points.\n",
    "- `simplices`\t(ndarray of ints, shape (nsimplex, ndim+1)) Indices of the points forming the simplices in the triangulation. For 2-D, the points are oriented counterclockwise.\n",
    "- `neighbors`\t(ndarray of ints, shape (nsimplex, ndim+1)) Indices of neighbor simplices for each simplex. The kth neighbor is opposite to the kth vertex. For simplices at the boundary, -1 denotes no neighbor. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting all spatial objects at the 3rd timestep. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot all scipy - spatial objects of a given timestamp\n",
    "voronoi_plot_2d(spatial_obj.voronoi_object[2])\n",
    "convex_hull_plot_2d(spatial_obj.convex_hull_object[2])\n",
    "delaunay_plot_2d(spatial_obj.delaunay_object[2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "g",
   "language": "python",
   "name": "g"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}