diff --git a/animations/discrete_bayes_animations.ipynb b/animations/discrete_bayes_animations.ipynb
index ccb09ae..3002c9c 100644
--- a/animations/discrete_bayes_animations.ipynb
+++ b/animations/discrete_bayes_animations.ipynb
@@ -1,409 +1,481 @@
{
- "metadata": {
- "name": "",
- "signature": "sha256:8f58b707cf76c184d840e41abdb14f1e584995252c4df05a742336b9f07b9b0b"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
{
- "cells": [
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Discrete Bayes Animations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
{
- "cell_type": "heading",
- "level": 1,
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 1,
"metadata": {},
- "source": [
- "Discrete Bayes Animations"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "from __future__ import division, print_function\n",
- "import matplotlib.pyplot as plt\n",
- "import sys\n",
- "sys.path.insert(0,'..') # allow us to format the book\n",
- "sys.path.insert(0,'../code') \n",
- "import book_format\n",
- "book_format.load_style(directory='..')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "html": [
- "\n",
- "\n"
- ],
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 8,
- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 8
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This notebook creates the animations for the Discrete Bayesian filters chapter. It is not really intended to be a readable part of the book, but of course you are free to look at the source code, and even modify it. However, if you are interested in running your own animations, I'll point you to the examples subdirectory of the book, which contains a number of python scripts that you can run and modify from an IDE or the command line. This module saves the animations to GIF files, which is quite slow and not very interactive. "
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "from matplotlib import animation\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "from book_plots import bar_plot\n",
- "%matplotlib inline"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 9
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# the predict algorithm of the discrete bayesian filter\n",
- "def predict(pos, move, p_correct, p_under, p_over):\n",
- " n = len(pos)\n",
- " result = np.array(pos, dtype=float)\n",
- " for i in range(n):\n",
- " result[i] = \\\n",
- " pos[(i-move) % n] * p_correct + \\\n",
- " pos[(i-move-1) % n] * p_over + \\\n",
- " pos[(i-move+1) % n] * p_under \n",
- " return result\n",
- "\n",
- "\n",
- "def normalize(p):\n",
- " s = sum(p)\n",
- " for i in range (len(p)):\n",
- " p[i] = p[i] / s\n",
- " \n",
- "# the update algorithm of the discrete bayesian filter\n",
- "def update(pos, measure, p_hit, p_miss):\n",
- " q = np.array(pos, dtype=float)\n",
- " for i in range(len(hallway)):\n",
- " if hallway[i] == measure:\n",
- " q[i] = pos[i] * p_hit\n",
- " else:\n",
- " q[i] = pos[i] * p_miss\n",
- " normalize(q)\n",
- " return q"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 10
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pos = [1.0,0,0,0,0,0,0,0,0,0]\n",
- "def bar_animate(nframe):\n",
- " global pos\n",
- " bar_plot(pos)\n",
- " pos = predict(pos, 1, .8, .1, .1)\n",
- "\n",
- "\n",
- "fig = plt.figure(figsize=(6.5, 2.5))\n",
- "anim = animation.FuncAnimation(fig, bar_animate,\n",
- " frames=100, interval=75)\n",
- "anim.save('02_no_info.gif', writer='imagemagick')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncJKkVrLgJEmtZMFJklrp/wGkNvPGWyX77wAAAABJRU5ErkJggg==\n",
- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 11
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "![](\"02_no_info.gif\")
"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pos = np.array([.1]*10)\n",
- "hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])\n",
- "\n",
- "def bar_animate(nframe):\n",
- " global pos\n",
- " if nframe == 0:\n",
- " return\n",
- "\n",
- " bar_plot(pos, ylim=(0,1.0))\n",
- " if nframe % 2 == 0:\n",
- " pos = predict(pos, 1, .8, .1, .1)\n",
- " else:\n",
- " x = (nframe/2) % len(hallway)\n",
- " z = hallway[x]\n",
- " pos = update(pos, z, .6, .2)\n",
- " \n",
- "\n",
- "fig = plt.figure(figsize=(6.5, 2.5))\n",
- "anim = animation.FuncAnimation(fig, bar_animate,\n",
- " frames=60, interval=75)\n",
- "anim.save('02_simulate.gif', writer='imagemagick')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAACsCAYAAAAJ+rmKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
- "text": [
- ""
- ]
- }
- ],
- "prompt_number": 14
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " ![](\"02_simulate.gif\")
"
- ]
+ "output_type": "execute_result"
}
],
- "metadata": {}
+ "source": [
+ "from __future__ import division, print_function\n",
+ "import matplotlib.pyplot as plt\n",
+ "import sys\n",
+ "sys.path.insert(0,'..') # allow us to format the book\n",
+ "sys.path.insert(0,'../code') \n",
+ "import book_format\n",
+ "book_format.load_style(directory='..')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This notebook creates the animations for the Discrete Bayesian filters chapter. It is not really intended to be a readable part of the book, but of course you are free to look at the source code, and even modify it. However, if you are interested in running your own animations, I'll point you to the examples subdirectory of the book, which contains a number of python scripts that you can run and modify from an IDE or the command line. This module saves the animations to GIF files, which is quite slow and not very interactive. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from matplotlib import animation\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from book_plots import bar_plot\n",
+ "%matplotlib inline\n",
+ "\n",
+ "# the predict algorithm of the discrete bayesian filter\n",
+ "def predict(pos, move, p_correct, p_under, p_over):\n",
+ " n = len(pos)\n",
+ " result = np.array(pos, dtype=float)\n",
+ " for i in range(n):\n",
+ " result[i] = \\\n",
+ " pos[(i-move) % n] * p_correct + \\\n",
+ " pos[(i-move-1) % n] * p_over + \\\n",
+ " pos[(i-move+1) % n] * p_under \n",
+ " return result\n",
+ "\n",
+ "\n",
+ "def normalize(p):\n",
+ " s = sum(p)\n",
+ " for i in range (len(p)):\n",
+ " p[i] = p[i] / s\n",
+ " \n",
+ "# the update algorithm of the discrete bayesian filter\n",
+ "def update(pos, measure, p_hit, p_miss):\n",
+ " q = np.array(pos, dtype=float)\n",
+ " for i in range(len(hallway)):\n",
+ " if hallway[i] == measure:\n",
+ " q[i] = pos[i] * p_hit\n",
+ " else:\n",
+ " q[i] = pos[i] * p_miss\n",
+ " normalize(q)\n",
+ " return q"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'imagemagick'"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import matplotlib\n",
+ "matplotlib.matplotlib_fname()\n",
+ "matplotlib.rcParams['animation.writer']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\"anim = animation.FuncAnimation(fig, bar_animate,\\n frames=100, interval=75)\\nanim.save('02_no_info.gif', writer='imagemagick')\""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAELCAYAAADkyZC4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAE49JREFUeJzt3X+QXXd93vH3YwkDBrE2UbFBFmMoW2onBALCBhx+JChB\nNq3dhkxcFYcBiuO2WGGameI2ndLMME1LAilDDIyCbTAFbBIDid2xbOqBGAiuYbFdYywR2cJEsgy2\nwSxCJkSKP/3jHonrrfan9qt7Vvt+zezsPed8z7mPVrv32fPrbqoKSZIW2zGjDiBJOjpZMJKkJiwY\nSVITFowkqQkLRpLUhAUjSWrCgpEkNWHBaFlJ8sQk70zy10keSfK9JF9JsmlozKVJPn8EM52U5ONJ\n7kyyL8n/nmbc05P8aZLJ7uPKJP/gSOWU5mvlqANIR9gHgVcBvw38X+ApwAuBtSPM9Hjge8B7gN8A\nVkwdkOQY4H8B+4H1DH45/ADw58CZRyypNA/xTn4tJ0keBv5TVX1gmuW/B7xjyuw3VtVHkzwZ+K/A\n64ATgG8C76yqz3TrngLsAH4TeDPwUuB+4Her6pNzzPcRYE1V/cqU+b8KXA88t6q2d/NOA+4Efqmq\nbprL9qUjyUNkWm7uB85KcsI0y/8Q+ATwZeCk7uNPkwS4Fngeg72Mn2WwN3RVkl+eso0/AC4Fnt9t\n6+NJXnCYuc8EdhwoF4CqugvYBfziYW5basJDZFpu3sLgRf/BJN8A/g9wXVX9BUBV7U3yt8C+qnrg\nwEpJXgW8BDixqn7Yzf5QkpcCm4DPDT3HpVV1Zff4P3cF9DvAGw4j99OB7xxi/ncYlKDUOxaMlpWq\n+nKSfwiczuAQ1iuAq5NsqapzZlj1xcCxwH2DnZmDjgX+esrYm6dM/xXw6sMKPpDZh0j9YcFo2amq\nv2dQAjcDf5Tk9cD/TPLyqvriNKsdA0wC6w6x7O9mecrFKIb7OXRJndQtk3rHczASbOs+P637/Hf8\n/1dyfRU4HnhiVe2Y8rFrytiXTpl+GfCNeeQ51JU3XwKeleQ5B2Z0J/lP7pZJveMejJaVJDcxOAfz\nNeBB4DnA7wMPAwfufdkB/Hr3Av4A8MOq+lySG4FPJ3k78HUGV5K9DPhxVV069DRvTrKte47zGZy7\neessuQ5cBPBUYFWS5zO4yvP2bv6NwK3Ax7p7do4B3g/cXFVfWNhXQ2rLy5S1rCS5GHgt8FwG98A8\nANwE/H5VbevGnAB8jMGVW0/hp5cpPwH4L8B5wBrg+8BtwB9U1V9OuUz5LQyKZTeDy6KvmiXXo0OT\nxeCwWlXViqExJwHvAzZ0Y64DNlXVQwv9ekgtzVowSS5n8AP5QFU9b5ox7wPOAh5h8MN422IHlfpu\nqGB+saq+PNo00ujN5RzMhxn8xnRISc4GnlNV48BvMbg3QJK0zM1aMN1VNQ/PMOQc4Ipu7C3A8UlO\nXJx40pLjMWepsxgn+dcAO4emdzG4suW7i7Btacmoqns5xPuIScvVYl1FNvU6/8f8Fjc5OelvdZJ0\nlBsbG3tMFyzGfTD38dh3oj25mydJWsYWo2CuoXuPpSQvAX5QVR4ek6RlbtZDZEmuBF4JrE6yk8F9\nAI8DqKrNVXVdkrOT3A3sBd400/bGxsbmFXBiYgKAdesO9Q4do9PHXH3MBP3M1cdM0M9cfcwE5pqP\nlpkmJyenXTZrwVTVxjmMuWiemSRJRznfi0yS1IQFI0lqwoKRJDVhwUiSmrBgJElNWDCSpCYsGElS\nExaMJKkJC0aS1IQFI0lqwoKRJDVhwUiSmrBgJElNWDCSpCYsGElSExaMJKkJC0aS1IQFI0lqwoKR\nJDVhwUiSmrBgJElNWDCSpCYsGElSExaMJKkJC0aS1IQFI0lqwoKRJDVhwUiSmrBgJElNWDCSpCYs\nGElSExaMJKkJC0aS1IQFI0lqwoKRJDVhwUiSmpi1YJJsSLItyfYkFx9i+ViSa5PcnuTOJG9sklSS\ntKTMWDBJVgCXABuA04CNSU6dMuytwJ1V9QLgVcB7kqxskFWStITMtgdzOnB3Vd1bVfuAq4Bzp4x5\nFHhK9/gpwPeqav/ixpQkLTWpqukXJr8OvKaqLuimzwfOqKpNQ2OeDFwLPBdYBfxGVW0Z3s7k5OTB\nJ9m+ffui/gMkSaMzPj5+8PHY2FiGl822BzN9+/zUBuDWqnoG8ALg/UlWzTekJOnoMtu5kvuAtUPT\na4FdU8a8EfhvAFV1T5JvMdibmTjUBtetWzevgBMTEwtar7U+5upjJuhnrj5mgn7m6mMmMNd8tMw0\nOTk57bLZ9mAmgPEkpyQ5FjgPuGbKmL8B1gMkOZFBuexYcFpJ0lFhxj2Yqtqf5CLgBmAFcFlVbU1y\nYbd8M/BO4CNJ7gACvL2qvt84tySp52a9nLg7Yb9lyrzNQ4/vB16z+NEkSUuZd/JLkpqwYCRJTVgw\nkqQmLBhJUhMWjCSpCQtGktSEBSNJasKCkSQ1YcFIkpqwYCRJTVgwkqQmLBhJUhMWjCSpCQtGktSE\nBSNJasKCkSQ1YcFIkpqwYCRJTVgwkqQmLBhJUhMWjCSpCQtGktSEBSNJasKCkSQ1YcFIkpqwYCRJ\nTVgwkqQmLBhJUhMWjCSpCQtGktSEBSNJasKCkSQ1YcFIkpqwYCRJTVgwkqQmZi2YJBuSbEuyPcnF\n04x5VZLbktyZ5C8XPaUkaclZOdPCJCuAS4D1wH3AV5NcU1Vbh8YcD7wfeE1V7UqyumVgSdLSMNse\nzOnA3VV1b1XtA64Czp0y5l8Cn6qqXQBV9dDix5QkLTWzFcwaYOfQ9K5u3rBx4KlJPp9kIslvLmZA\nSdLSlKqafmHyOmBDVV3QTZ8PnFFVm4bGXAK8EHg1cBxwM/Daqtp+YMzk5OTBJ9m+/eBsSdISNz4+\nfvDx2NhYhpfNeA6GwXmXtUPTaxnsxQzbCTxUVT8GfpzkC8DzAZtEkpax2QpmAhhPcgqwGzgP2Dhl\nzF8Al3QXBDweOAP4o+k2uG7dunkFnJiYWNB6rfUxVx8zQT9z9TET9DNXHzOBueajZabJyclpl81Y\nMFW1P8lFwA3ACuCyqtqa5MJu+eaq2pbkeuAO4FHgQ1V116KllyQtSbPtwVBVW4AtU+ZtnjL9buDd\nixtNkrSUeSe/JKkJC0aS1IQFI0lqwoKRJDVhwUiSmrBgJElNWDCSpCYsGElSExaMJKkJC0aS1IQF\nI0lqwoKRJDVhwUiSmrBgJElNWDCSpCYsGElSExaMJKkJC0aS1IQFI0lqwoKRJDVhwUiSmrBgJElN\nWDCSpCYsGElSExaMJKkJC0aS1IQFI0lqwoKRJDVhwUiSmrBgJElNWDCSpCYsGElSExaMJKkJC0aS\n1IQFI0lqwoKRJDUxa8Ek2ZBkW5LtSS6eYdyLk+xP8muLG1GStBTNWDBJVgCXABuA04CNSU6dZty7\ngOuBNMgpSVpiZtuDOR24u6rurap9wFXAuYcYtwm4GnhwkfNJkpao2QpmDbBzaHpXN++gJGsYlM4H\nu1m1aOkkSUtWqqbvgySvAzZU1QXd9PnAGVW1aWjMnwHvrqpbknwEuLaqPjW8ncnJyYNPsn379sX9\nF0iSRmZ8fPzg47GxscecIlk5y7r3AWuHptcy2IsZ9iLgqiQAq4GzkuyrqmsWGliStPTNtgezEvgm\n8GpgN/AVYGNVbZ1m/IcZ7MF8enj+8B7M2NjYvAJOTEwAsG7dunmt11ofc/UxE/QzVx8zQT9z9TET\nmGs+WmaanJw8+HheezBVtT/JRcANwArgsqramuTCbvnmRU8rSToqzHaIjKraAmyZMu+QxVJVb1qk\nXJKkJc47+SVJTVgwkqQmLBhJUhMWjCSpCQtGktSEBSNJasKCkSQ1YcFIkpqwYCRJTVgwkqQmLBhJ\nUhMWjCSpCQtGktSEBSNJasKCkSQ1Mevfg5Ek9cOOh/awe+/+ea+35wlPA+BL3354Qc/7jCet5Nmr\nV817PQtGkpaI3Xv3s+kLDx3GFn6yoLX++BWrefbq+a/nITJJUhMWjCSpCQtGktSEBSNJasKCkSQ1\nYcFIkpqwYCRJTVgwkqQmLBhJUhMWjCSpCQtGktSEBSNJasKCkSQ1YcFIkpqwYCRJTVgwkqQmLBhJ\nUhMWjCSpiSP+J5Pn+zehR/W3pCUtbzse2sPuvfvnvZ6vWT81p4JJsgF4L7ACuLSq3jVl+euBtwMB\n9gD/pqruONS2Fv73pI/s35KWtLzt3rv/MF6vwNesORwiS7ICuATYAJwGbExy6pRhO4BXVNXPA+8E\n/mSxg0qSlpa5nIM5Hbi7qu6tqn3AVcC5wwOq6uaqmuwmbwFOXtyYkqSlZi6HyNYAO4emdwFnzDD+\nXwHXHU6oxbRnzx4mJu5ptv2JiYlm216oPmaCfubqYyboZ64+ZoJ2uQ6cSznSZnrN6mOm8fHxadeb\nS8HUXEMk+SXgzcCZc11HknR0mkvB3AesHZpey2Av5jGS/DzwIWBDVS3s8okGVq1axbqfe+aib/fA\nb03r1q1b9G0vVB8zQT9z9TET9DNXHzNB+1yDq8AWdqL+cMz0mtXHTJOTk4ecD3M7BzMBjCc5Jcmx\nwHnANcMDkjwT+DRwflXdPdfQkqSj16x7MFW1P8lFwA0MLlO+rKq2JrmwW74ZeAdwAvDBJAD7qur0\ndrElSX03p/tgqmoLsGXKvM1Dj98CvGVxo0mSljLfKkaS1IQFI0lqwoKRJDVhwUiSmrBgJElNWDCS\npCYsGElSExaMJKkJC0aS1IQFI0lqwoKRJDVhwUiSmrBgJElNWDCSpCYsGElSExaMJKkJC0aS1IQF\nI0lqwoKRJDWxctQBRm3HQ3vYvXf/vNfb84SnAfClbz+8oOd9xpNW8uzVqxa0rnQ08Wfw6LXsC2b3\n3v1s+sJDh7GFnyxorT9+xWqevfownlY6SvgzePTyEJkkqQkLRpLUhAUjSWrCgpEkNWHBSJKasGAk\nSU1YMJKkJiwYSVITFowkqQkLRpLUhAUjSWrCgpEkNWHBSJKaWPbvptxHvn25WhjV9xX4vbVczVow\nSTYA7wVWAJdW1bsOMeZ9wFnAI8Abq+q2xQ66nPj25WphVN9X4PfWcjXjIbIkK4BLgA3AacDGJKdO\nGXM28JyqGgd+C/hgo6ySpCVktnMwpwN3V9W9VbUPuAo4d8qYc4ArAKrqFuD4JCcuelJJ0pIyW8Gs\nAXYOTe/q5s025uTDjyZJWspSVdMvTF4HbKiqC7rp84EzqmrT0Jhrgf9eVX/VTd8IvL2qbj0wZnJy\ncvonkSQdFcbGxjI8PdsezH3A2qHptQz2UGYac3I3T5K0jM1WMBPAeJJTkhwLnAdcM2XMNcAbAJK8\nBPhBVX130ZNKkpaUGS9Trqr9SS4CbmBwmfJlVbU1yYXd8s1VdV2Ss5PcDewF3jR1O1N3myRJR78Z\nz8FIkrRQvX6rmCQbkmxLsj3JxaPOA5Dk8iTfTfL1UWcZlmRtks8n+UaSO5P8dg8yPSHJLUlu7zL9\n3qgzDUuyIslt3YUqvZDk3iR3dLm+Muo8AEmOT3J1kq1J7uoOhY8603O7r9GBj8mefM//u+57/etJ\nPpHk8aPOBJDkbV2mO5O87Yg9b1/3YLqbPL8JrGdw0cBXgY1VtXXEuV4O/Aj4aFU9b5RZhiU5CTip\nqm5P8mTga8A/68HX67iqeiTJSuBLwNu6+6VGLsnvAC8CVlXVOaPOA5DkW8CLqur7o85yQJIrgJuq\n6vLu//FJVTU56lwHJDmGwWvE6VW1c7bxDXOsAb4InFpVP0nySeC6qrpiVJm6XD8HXAm8GNgHXA/8\n66q6p/Vz93kPZi43eR5xVfVFYOFvytRIVX2nqm7vHv8I2Ao8Y7SpoKoe6R4eCzwOeHSEcQ5KcjJw\nNnAp0LdzhL3Jk2QMeHlVXQ6D87J9KpfOeuCeUZbLkJXAcV0RH0c/rqj9x8AtVfW3VfX3wE3Arx2J\nJ+5zwczlJk8dQpJTgF8ARr6nkOSYJLcD3wU+W1VfHXWmzv8A/j09KbwhBdyYZCLJBaMOAzwLeDDJ\nh5PcmuRDSY4bdagp/gXwiVGHqKr7gPcAfwPsZnBF7Y2jTQXAncDLkzy1+797LUfoZvg+F0w/j931\nXHd47GoGh6J+NOo8VfVoVb2AwTf0GUl+dtSZkvwT4IHuTVl7s7fQObOqfoHBm8e+tTskO0orgRcC\nH6iqFzK4UvQ/jDbST3W3T/xT4M96kOUEBm+ddQqDowdPTvL6kYYCqmob8C7gs8AW4DaO0C9WfS6Y\nudzkqSFJHgd8CvhYVf35qPMM6w6rfJ7BG6eO2suAc7rzHVcCv5zkoyPOBEBV3d99fhD4DINDxaO0\nC9g1tOd5NYPC6YuzgK91X69RWw98q6q+V1X7gU8z+F4buaq6vKrWVdUrgR8wOL/dXJ8LZi43eaqT\nJMBlwF1V9d5R5wFIsjrJ8d3jJwK/wuDc0EhV1e9W1dqqehaDwyufq6o3jDpXkuOSrOoePwn4VWCk\nVytW1XeAnUn+UTdrPfCNEUaaaiODXxL64NvAS5I8sft5XA/cNeJMACR5Wvf5mcA/5wgdUuztHxyb\n7ibPEcciyZXAK4GfSbITeEdVfXjEsQDOBM4H7khy4O/x/Mequn6EmZ4OXNFdEXgM8Mmqum6EeabT\nl8OxJwKfGbw2sRL4eFV9drSRANgEfLz7Re8eDnEz9Sh0Jbwe6MO5KqrqK0muBm4F9nef/2S0qQ66\nOsnPMLiK7N9W1Q+PxJP29jJlSdLS1udDZJKkJcyCkSQ1YcFIkpqwYCRJTVgwkqQmLBhJUhMWjCSp\nCQtGktTE/wPYAenFmNOy1QAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAAGXCAYAAADBMEVQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from gif_animate import animate\n",
+ "\n",
+ "pos = [1.0,0,0,0,0,0,0,0,0,0]\n",
+ "def bar_animate(nframe):\n",
+ " global pos\n",
+ " bar_plot(pos)\n",
+ " plt.title('Step {}'.format(nframe + 1))\n",
+ " pos = predict(pos, 1, .8, .1, .1)\n",
+ "\n",
+ "for i in range(10):\n",
+ " bar_animate(i)\n",
+ "\n",
+ "fig = plt.figure(figsize=(6.5, 2.5))\n",
+ "animate('foo.gif', bar_animate, frames=100, interval=75);\n",
+ "'''anim = animation.FuncAnimation(fig, bar_animate,\n",
+ " frames=100, interval=75)\n",
+ "anim.save('02_no_info.gif', writer='imagemagick')'''"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "pos = np.array([.1]*10)\n",
+ "hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])\n",
+ "\n",
+ "def bar_animate(nframe):\n",
+ " global pos\n",
+ " if nframe == 0:\n",
+ " return\n",
+ "\n",
+ " bar_plot(pos, ylim=(0,1.0))\n",
+ " if nframe % 2 == 0:\n",
+ " pos = predict(pos, 1, .8, .1, .1)\n",
+ " else:\n",
+ " x = (nframe/2) % len(hallway)\n",
+ " z = hallway[x]\n",
+ " pos = update(pos, z, .6, .2)\n",
+ " \n",
+ "\n",
+ "fig = plt.figure(figsize=(6.5, 2.5))\n",
+ "anim = animation.FuncAnimation(fig, bar_animate,\n",
+ " frames=60, interval=75)\n",
+ "anim.save('02_simulate.gif', writer='imagemagick')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " "
+ ]
}
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}