IRC_Exercise_7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def table_zeros(N):\n",
    "    \"\"\"\n",
    "    Table of zeros\n",
    "    Gives a np.array of zeros of\n",
    "    N x N dimensions \n",
    "    \"\"\"\n",
    "    if not isinstance(N, int):\n",
    "        print(\"Non-integer input given to function table_zeros; N = \" + str(N))\n",
    "        return 0\n",
    "    \n",
    "    table = np.zeros((N, N))\n",
    "    return table\n",
    "\n",
    "def create_table(N, arr):\n",
    "    \"\"\"\n",
    "    Creates a table with not-null entries\n",
    "    in the positions of arr\n",
    "    \"\"\"\n",
    "    if not isinstance(N, int):\n",
    "        print(\"Non-integer input given to function create_table; N = \" + str(N))\n",
    "        return 0\n",
    "    \n",
    "    table = table_zeros(N)\n",
    "    for i in range(len(arr)):\n",
    "        table[arr[i, 0], arr[i, 1]] = 1\n",
    "    return table \n",
    "\n",
    "def pad_with(vector, pad_width, iaxis, kwargs):\n",
    "    \"\"\"\n",
    "    Sorrounds a square matrix with null rows\n",
    "    and coloumns\n",
    "    \"\"\"\n",
    "    pad_value = kwargs.get('padder', 10)\n",
    "    vector[:pad_width[0]] = pad_value\n",
    "    vector[-pad_width[1]:] = pad_value\n",
    "    pass\n",
    "\n",
    "def sorround_zeros(T):\n",
    "    \"\"\"\n",
    "    Sorround the square table matrix with a \n",
    "    row and a column of zeros. \n",
    "    \"\"\"\n",
    "    ans = np.pad(T, 1, pad_with, padder = 0)\n",
    "    return ans    \n",
    "\n",
    "def sum_neighbours(T, i, j):\n",
    "    \"\"\"\n",
    "    Calculate the sum of the neighbours of the \n",
    "    (i,j) element of the table T matrix. \n",
    "    \"\"\"\n",
    "    T_pad = sorround_zeros(T)\n",
    "    i += 1\n",
    "    j += 1\n",
    "    above_sum = T_pad[i-1, j-1] + T_pad[i-1, j] + T_pad[i-1, j+1]\n",
    "    middle_sum = T_pad[i, j-1] + T_pad[i, j+1]\n",
    "    below_sum = T_pad[i+1, j-1] + T_pad[i+1, j] + T_pad[i+1, j+1]\n",
    "    return above_sum + middle_sum + below_sum\n",
    "\n",
    "def evolve_matrix(T):\n",
    "    \"\"\"\n",
    "    Evolves the table T matrix\n",
    "    one step in time. \n",
    "    \"\"\"\n",
    "    T_new = np.copy(T)\n",
    "    for i in range(T.shape[0]):\n",
    "        for j in range(T.shape[1]):\n",
    "            if T[i, j] == 1:\n",
    "                if sum_neighbours(T, i, j) > 3:\n",
    "                    T_new[i, j] = 0\n",
    "                elif sum_neighbours(T, i, j) < 2:\n",
    "                    T_new[i, j] = 0\n",
    "            elif T[i, j] == 0 and sum_neighbours(T, i, j) == 3:\n",
    "                T_new[i, j] = 1\n",
    "    return T_new\n",
    "\n",
    "def evolve_n_steps(T, steps):\n",
    "    \"\"\"\n",
    "    Evolves the table T matrix\n",
    "    n steps in time. \n",
    "    \"\"\"\n",
    "    if steps == 0:\n",
    "        return T\n",
    "    if steps > 0:\n",
    "        T_new = evolve_matrix(T)\n",
    "        return evolve_n_steps(T_new, steps - 1)\n",
    "    \n",
    "def list_of_frames(T, steps):\n",
    "    \"\"\"\n",
    "    Creates a list of tables starting\n",
    "    from the initial one T and making it \n",
    "    evolve a given number of steps.\n",
    "    \"\"\"\n",
    "    if steps == 0:\n",
    "        return np.array([T])\n",
    "    if steps > 0:\n",
    "        T_new = evolve_matrix(T)\n",
    "        ans = np.concatenate(( np.array([T]), list_of_frames(T_new, steps - 1) ))\n",
    "    return ans\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAD7CAYAAABZjGkWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIMIGAhE2EIiwgUD/BV/Z8ahZeXHiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e0694d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates the \"blinker\" oscillator\n",
    "T = create_table(5, np.array([[1, 2], [2, 2], [3,2]]))\n",
    "T_new = evolve_matrix(T)\n",
    "frames = list_of_frames(T, 50)\n",
    "\n",
    "# Create figure to save the video \n",
    "fig = plt.figure()\n",
    "i = 0\n",
    "im = plt.imshow(frames[0], cmap = 'Greys', interpolation='nearest', animated=True)\n",
    "\n",
    "# Update the figure \n",
    "def updatefig(*args):\n",
    "    global i\n",
    "    if (i < len(frames) - 1):\n",
    "        i += 1\n",
    "    else:\n",
    "        i=0\n",
    "    im.set_array(frames[i])\n",
    "    return im,\n",
    "\n",
    "# Creates the animation\n",
    "ani = animation.FuncAnimation(fig, updatefig,  blit=True, interval = 100)\n",
    "plt.show()\n",
    "\n",
    "# Save the video \n",
    "ani.save(\"movie_blinker.mp4\")\n",
    "\n",
    "\n",
    "# In case you want to use Google's Colab \n",
    "# use the next two lines. \n",
    "\n",
    "# from google.colab import files\n",
    "# files.download(\"movie_test.mp4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creates the \"pulsar\" oscillator\n",
    "pulsar_indices = np.array([ \n",
    "                        [2, 4], [2, 5], [2, 6], [2, 10], [2, 11], [2, 12],\n",
    "                        [4, 2], [4, 7], [4, 9], [4, 14],\n",
    "                        [5, 2], [5, 7], [5, 9], [5, 14],\n",
    "                        [6, 2], [6, 7], [6, 9], [6, 14],\n",
    "                        [7, 4], [7, 5], [7, 6], [7, 10], [7, 11], [7, 12],\n",
    "                        [9, 4], [9, 5], [9, 6], [9, 10], [9, 11], [9, 12],\n",
    "                        [10, 2], [10, 7], [10, 9], [10, 14],\n",
    "                        [11, 2], [11, 7], [11, 9], [11, 14],\n",
    "                        [12, 2], [12, 7], [12, 9], [12, 14],\n",
    "                        [14, 4], [14, 5], [14, 6], [14, 10], [14, 11], [14, 12],\n",
    "                          ])\n",
    "\n",
    "T_pulsar = create_table(17, pulsar_indices)\n",
    "T_pulsar_new = evolve_matrix(T_pulsar)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAAD7CAYAAABOrvnfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e07c6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frames_pulsar = list_of_frames(T_pulsar, 50)\n",
    "\n",
    "# Create figure to save the video \n",
    "fig = plt.figure()\n",
    "i = 0\n",
    "im = plt.imshow(frames_pulsar[0], cmap = 'Greys', interpolation='nearest', animated=True)\n",
    "\n",
    "# Update figure \n",
    "def updatefig(*args):\n",
    "    global i\n",
    "    if (i < len(frames_pulsar) - 1):\n",
    "        i += 1\n",
    "    else:\n",
    "        i=0\n",
    "    im.set_array(frames_pulsar[i])\n",
    "    return im,\n",
    "\n",
    "# Create the animation\n",
    "ani = animation.FuncAnimation(fig, updatefig,  blit=True, interval = 200)\n",
    "plt.show()\n",
    "\n",
    "# Save the video \n",
    "ani.save(\"movie_pulsar.mp4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creates the \"penta-decathlon\" oscillator\n",
    "d = 3\n",
    "pentad_indices = np.array([ [5, 4+d], [5, 5+d], [5, 6+d],\n",
    "                           [6, 4+d], [6, 6+d],\n",
    "                           [7, 4+d], [7, 5+d], [7, 6+d],\n",
    "                           [8, 4+d], [8, 5+d], [8, 6+d],\n",
    "                           [9, 4+d], [9, 5+d], [9, 6+d],\n",
    "                           [10, 4+d], [10, 5+d], [10, 6+d],\n",
    "                           [11, 4+d], [11, 6+d],\n",
    "                           [12, 4+d], [12, 5+d], [12, 6+d],\n",
    "                          ])\n",
    "\n",
    "T_pentad = create_table(18, pentad_indices)\n",
    "T_pentad_new = evolve_matrix(T_pentad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "frames_pentad = list_of_frames(T_pentad, 50)\n",
    "\n",
    "# Create the figure \n",
    "fig = plt.figure()\n",
    "i = 0\n",
    "im = plt.imshow(frames_pentad[0], cmap = 'Greys', interpolation='nearest', animated=True)\n",
    "\n",
    "# Update the figure\n",
    "def updatefig(*args):\n",
    "    global i\n",
    "    if (i < len(frames_pentad) - 1):\n",
    "        i += 1\n",
    "    else:\n",
    "        i=0\n",
    "    im.set_array(frames_pentad[i])\n",
    "    return im,\n",
    "\n",
    "# Create the animation\n",
    "ani = animation.FuncAnimation(fig, updatefig,  blit=True, interval = 200)\n",
    "plt.show()\n",
    "\n",
    "# Save the figure \n",
    "ani.save(\"movie_pentadecathlon.mp4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creates the \"Glider\" \n",
    "glider_indices = np.array([ [1, 1], [2, 2], [2, 3],\n",
    "                           [3, 1], [3, 2]\n",
    "                          ])\n",
    "\n",
    "T_glider = create_table(25, glider_indices)\n",
    "T_glider_new = evolve_matrix(T_glider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "frames_glider = list_of_frames(T_glider, 50)\n",
    "\n",
    "# Create the figure\n",
    "fig = plt.figure()\n",
    "i = 0\n",
    "im = plt.imshow(frames_glider[0], cmap = 'Greys', interpolation='nearest', animated=True)\n",
    "\n",
    "# Update the figure\n",
    "def updatefig(*args):\n",
    "    global i\n",
    "    if (i < len(frames_glider) - 1):\n",
    "        i += 1\n",
    "    else:\n",
    "        i=0\n",
    "    im.set_array(frames_glider[i])\n",
    "    return im,\n",
    "\n",
    "# Create animation\n",
    "ani = animation.FuncAnimation(fig, updatefig,  blit=True, interval = 200)\n",
    "plt.show()\n",
    "\n",
    "# Save the video \n",
    "ani.save(\"movie_glider.mp4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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