Weights should be reset to 1/N after resampling.

2017-07-03 13:07:28 -07:00
1 parent ba61f3a96c
commit 7c5aa45019
@@ -17,9 +17,7 @@
  {
   "cell_type": "code",
   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "data": {
@@ -349,15 +347,13 @@
  {
   "cell_type": "code",
   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6985f978>"
+       "<matplotlib.figure.Figure at 0x1958a845668>"
      ]
     },
     "metadata": {},
@@ -421,15 +417,13 @@
  {
   "cell_type": "code",
   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6994ff60>"
+       "<matplotlib.figure.Figure at 0x1958d562c88>"
      ]
     },
     "metadata": {},
@@ -506,9 +500,7 @@
  {
   "cell_type": "code",
   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
@@ -522,7 +514,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c69a8c470>"
+       "<matplotlib.figure.Figure at 0x1958d49d630>"
      ]
     },
     "metadata": {},
@@ -568,15 +560,13 @@
  {
   "cell_type": "code",
   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAEWCAYAAAAn0eDVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6a32bba8>"
+       "<matplotlib.figure.Figure at 0x1958d59aba8>"
      ]
     },
     "metadata": {},
@@ -602,15 +592,13 @@
  {
   "cell_type": "code",
   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6a3acda0>"
+       "<matplotlib.figure.Figure at 0x1958d6675c0>"
      ]
     },
     "metadata": {},
@@ -688,9 +676,7 @@
  {
   "cell_type": "code",
   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "data": {
@@ -797,7 +783,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "In the literature this part of the algorithm is called *Sequential Importance Sampling*, or SIS. The equation for the weights is called the *importance density*. I will give these theoretical underpinnings in a following section. For now I hope that this makes intuitive sense. If we weight the particles according to how how they match the measurements they are probably a good sample for the probability distribution of the system after incorporating the measurements. Theory proves this is so. The weights are the *likelihood* in Bayes theorem. Different problems will need to tackle this step in slightly different ways but this is the general idea."
+    "In the literature this part of the algorithm is called *Sequential Importance Sampling*, or SIS. The equation for the weights is called the *importance density*. I will give these theoretical underpinnings in a following section. For now I hope that this makes intuitive sense. If we weight the particles according to how well they match the measurements they are probably a good sample for the probability distribution of the system after incorporating the measurements. Theory proves this is so. The weights are the *likelihood* in Bayes theorem. Different problems will need to tackle this step in slightly different ways but this is the general idea."
   ]
  },
  {
@@ -842,9 +828,7 @@
  {
   "cell_type": "code",
   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "data": {
@@ -869,11 +853,9 @@
   "source": [
    "### Particle Resampling\n",
    "\n",
-    "The SIS algorithm suffers from the *degeneracy problem*. It starts with uniformly distributed particles with equal weights. There may only be a handful of particles near the robot. As the algorithm runs any particle that does not match the measurements will acquire an extremely low weight. Only the particles which are near the robot will have an appreciable weight. We could have 5,000 particles with only 3 contributing meaningfully to the state estimate! We say the filter has *degenerated*.\n",
+    "The SIS algorithm suffers from the *degeneracy problem*. It starts with uniformly distributed particles with equal weights. There may only be a handful of particles near the robot. As the algorithm runs any particle that does not match the measurements will acquire an extremely low weight. Only the particles which are near the robot will have an appreciable weight. We could have 5,000 particles with only 3 contributing meaningfully to the state estimate! We say the filter has *degenerated*.This problem is usually solved by some form of *resampling* of the particles. \n",
    "\n",
-    "This problem is usually solved by some form of *resampling* of the particles. Particles with very small weights do not meaningfully describe the probability distribution of the robot. \n",
-    "\n",
-    "The resampling algorithm discards particles with very low probability and replaces them with new particles with higher probability. It does that by duplicating particles with relatively high probability. The duplicates are slightly dispersed by the noise added in the predict step. This results in a set of points in which a large majority of the particles accurately represent the probability distribution.\n",
+    "Particles with very small weights do not meaningfully describe the probability distribution of the robot. The resampling algorithm discards particles with very low probability and replaces them with new particles with higher probability. It does that by duplicating particles with relatively high probability. The duplicates are slightly dispersed by the noise added in the predict step. This results in a set of points in which a large majority of the particles accurately represent the probability distribution.\n",
    "\n",
    "There are many resampling algorithms.  For now let's look at one of the simplest, *simple random resampling*, also called *multinomial resampling*. It samples from the current particle set $N$ times, making a new set of particles from the sample. The probability of selecting any given particle should be proportional to its weight.\n",
    "\n",
@@ -896,8 +878,7 @@
    "\n",
    "    # resample according to indexes\n",
    "    particles[:] = particles[indexes]\n",
-    "    weights[:] = weights[indexes]\n",
-    "    weights /= np.sum(weights) # normalize"
+    "    weights.fill(1.0 / N)"
   ]
  },
  {
@@ -952,7 +933,7 @@
    "def resample_from_index(particles, weights, indexes):\n",
    "    particles[:] = particles[indexes]\n",
    "    weights[:] = weights[indexes]\n",
-    "    weights /= np.sum(weights)   "
+    "    weights.fill (1.0 / len(weights))"
   ]
  },
  {
@@ -966,7 +947,6 @@
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
-    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
@@ -975,14 +955,14 @@
     "output_type": "stream",
     "text": [
      "final position error, variance:\n",
-      "\t [ 17.9  18.1] [ 0.00462  0.00431]\n"
+      "\t [ -0.106  0.106] [ 0.0086  0.00757]\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6a425390>"
+       "<matplotlib.figure.Figure at 0x1958e7090f0>"
      ]
     },
     "metadata": {},
@@ -1055,7 +1035,7 @@
    "    plt.legend([p1, p2], ['Actual', 'PF'], loc=4, numpoints=1)\n",
    "    plt.xlim(*xlim)\n",
    "    plt.ylim(*ylim)\n",
-    "    print('final position error, variance:\\n\\t', mu, var)\n",
+    "    print('final position error, variance:\\n\\t', mu - np.array([iters, iters]), var)\n",
    "    plt.show()\n",
    "\n",
    "from numpy.random import seed\n",
@@ -1097,23 +1077,21 @@
  {
   "cell_type": "code",
   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final position error, variance:\n",
-      "\t [ 7.99  7.98] [ 0.00271  0.00286]\n"
+      "\t [ -0.0187  -0.00526] [ 0.00515  0.0056]\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6a3cd9b0>"
+       "<matplotlib.figure.Figure at 0x1958e6f5710>"
      ]
     },
     "metadata": {},
@@ -1155,7 +1133,6 @@
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
-    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
@@ -1164,14 +1141,14 @@
     "output_type": "stream",
     "text": [
      "final position error, variance:\n",
-      "\t [ 7.83  8.09] [ 0.00292  0.00284]\n"
+      "\t [ -0.17  0.0845] [ 0.00548  0.00501]\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6b4f6898>"
+       "<matplotlib.figure.Figure at 0x1958e880668>"
      ]
     },
     "metadata": {},
@@ -1207,23 +1184,21 @@
  {
   "cell_type": "code",
   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final position error, variance:\n",
-      "\t [ 15.3  -13.5] [ 47.1  47.0]\n"
+      "\t [ -2.69  -31.5] [ 47.1  47.0]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAAEWCAYAAADbxMsfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6a3cd4a8>"
+       "<matplotlib.figure.Figure at 0x1958f850630>"
      ]
     },
     "metadata": {},
@@ -1247,23 +1222,21 @@
  {
   "cell_type": "code",
   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final position error, variance:\n",
-      "\t [ 18.1  17.9] [ 0.00396  0.00465]\n"
+      "\t [ 0.0353  -0.0766] [ 0.00741  0.00933]\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAEWCAYAAADB+CuRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6c701080>"
+       "<matplotlib.figure.Figure at 0x1958e701668>"
      ]
     },
     "metadata": {},
@@ -1368,7 +1341,7 @@
    "def resample_from_index(particles, weights, indexes):\n",
    "    particles[:] = particles[indexes]\n",
    "    weights[:] = weights[indexes]\n",
-    "    weights /= np.sum(weights)"
+    "    weights.fill(1.0 / len(weights))"
   ]
  },
  {
@@ -1384,7 +1357,6 @@
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
-    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
@@ -1397,9 +1369,9 @@
    },
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAAA2CAYAAACYw97kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncbr/CePRAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAABLCAYAAAAvf8D7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGgAAgGHEGgAAgGH/B77nceToDeobAAAAAElFTkSuQmCC\n",
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c69a35320>"
+       "<matplotlib.figure.Figure at 0x1958d5d48d0>"
      ]
     },
     "metadata": {},
@@ -1424,9 +1396,7 @@
  {
   "cell_type": "code",
   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
    "def multinomal_resample(weights):\n",
@@ -1446,15 +1416,14 @@
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
-    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c69a89668>"
+       "<matplotlib.figure.Figure at 0x1958d6c56d8>"
      ]
     },
     "metadata": {},
@@ -1532,15 +1501,13 @@
  {
   "cell_type": "code",
   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6a32e780>"
+       "<matplotlib.figure.Figure at 0x1958d63bb70>"
      ]
     },
     "metadata": {},
@@ -1577,15 +1544,13 @@
  {
   "cell_type": "code",
   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6a2857f0>"
+       "<matplotlib.figure.Figure at 0x1958e8b97b8>"
      ]
     },
     "metadata": {},
@@ -1652,15 +1617,13 @@
  {
   "cell_type": "code",
   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6b4ba128>"
+       "<matplotlib.figure.Figure at 0x1958e7a34e0>"
      ]
     },
     "metadata": {},
@@ -1731,15 +1694,14 @@
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
-    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6a421438>"
+       "<matplotlib.figure.Figure at 0x1958d60d0b8>"
      ]
     },
     "metadata": {},
@@ -1747,9 +1709,9 @@
    },
    {
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6a2c5c50>"
+       "<matplotlib.figure.Figure at 0x1958fa0c080>"
      ]
     },
     "metadata": {},
@@ -1757,9 +1719,9 @@
    },
    {
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6a3d4080>"
+       "<matplotlib.figure.Figure at 0x1958e76d6d8>"
      ]
     },
     "metadata": {},
@@ -1767,9 +1729,9 @@
    },
    {
     "data": {
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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x21c6b46bfd0>"
+       "<matplotlib.figure.Figure at 0x1958e94a780>"
      ]
     },
     "metadata": {},
@@ -1848,9 +1810,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.3"
  }
 },
 "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
@@ -2,7 +2,7 @@
 "cells": [
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
@@ -12,33 +12,72 @@
      "text/html": [
       "<style>\n",
       "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
-       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Lora');\n",
+       "\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Open+Sans');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Karla');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Poppins');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Roboto');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Lato');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Domine');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Chivo');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Cardo');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Arvo');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Ubuntu');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Fontin');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Raleway');\n",
+       "//@import url('http://fonts.googleapis.com/css?family=Merriweather');\n",
+       "\n",
       "\n",
       ".CodeMirror pre {\n",
       "    font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
       "}\n",
       "    div.cell{\n",
-       "        width: 900px;\n",
+       "        //width: 950px;\n",
       "        margin-left: 0% !important;\n",
       "        margin-right: auto;\n",
       "    }\n",
+       "    div.text_cell_render{\n",
+       "        font-family: 'Lora';\n",
+       "        //font-family: 'Open Sans';\n",
+       "        //font-family: 'Karla',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Roboto',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Lato',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Domine',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Chivo',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Cardo',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Arvo',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Poppins',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Ubuntu',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Fontin',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Raleway',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Merriweather',verdana,arial,sans-serif;\n",
+       "        //font-family: 'Crimson Text', verdana,arial,sans-serif;\n",
+       "        //font-family: verdana,arial,sans-serif;\n",
+       "        //font-family: arial,sans-serif;\n",
+       "        line-height: 125%;\n",
+       "        font-size: 130%;\n",
+       "        text-align: justify;\n",
+       "        text-justify:inter-word;\n",
+       "    }\n",
       "    div.text_cell code {\n",
       "        background: transparent;\n",
       "        color: #000000;\n",
-       "        font-weight: 600;\n",
-       "        font-size: 11pt;\n",
-       "        font-style: bold;\n",
+       "        font-weight: 400;\n",
+       "        font-size: 12pt;\n",
+       "        //font-style: bold;\n",
       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
       "   }\n",
       "    h1 {\n",
       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
       "\t}\n",
-       "\t\n",
+       "\n",
       "    div.input_area {\n",
       "        background: #F6F6F9;\n",
-       "        border: 1px solid #586e75;      \n",
+       "        border: 1px solid #586e75;\n",
       "    }\n",
       "\n",
       "    .text_cell_render h1 {\n",
@@ -51,7 +90,7 @@
       "        display: block;\n",
       "        white-space: wrap;\n",
       "        text-align: left;\n",
-       "    } \n",
+       "    }\n",
       "    h2 {\n",
       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
       "        text-align: left;\n",
@@ -67,7 +106,7 @@
       "        display: block;\n",
       "        white-space: wrap;\n",
       "        text-align: left;\n",
-       "    } \n",
+       "    }\n",
       "    h3 {\n",
       "        font-family: 'Open sans',verdana,arial,sans-serif;\n",
       "    }\n",
@@ -108,14 +147,6 @@
       "        display: block;\n",
       "        white-space: nowrap;\n",
       "    }\n",
-       "    div.text_cell_render{\n",
-       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
-       "        line-height: 150%;\n",
-       "        font-size: 110%;\n",
-       "        font-weight: 400;\n",
-       "        text-align:justify;\n",
-       "        text-justify:inter-word;\n",
-       "    }\n",
       "    div.output_subarea.output_text.output_pyout {\n",
       "        overflow-x: auto;\n",
       "        overflow-y: scroll;\n",
@@ -142,42 +173,38 @@
       "        margin: 2em;\n",
       "    }\n",
       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
+       "        padding-left: 0.5em;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.5em;\n",
       "    }\n",
       "    ul li li{\n",
-       "        padding-left: 0.2em; \n",
-       "        margin-bottom: 0.2em; \n",
-       "        margin-top: 0.2em; \n",
+       "        padding-left: 0.2em;\n",
+       "        margin-bottom: 0.2em;\n",
+       "        margin-top: 0.2em;\n",
       "    }\n",
       "    ol{\n",
       "        margin: 2em;\n",
       "    }\n",
       "    ol li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.5em; \n",
+       "        padding-left: 0.5em;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.5em;\n",
       "    }\n",
       "    ul li{\n",
-       "        padding-left: 0.5em; \n",
-       "        margin-bottom: 0.5em; \n",
-       "        margin-top: 0.2em; \n",
+       "        padding-left: 0.5em;\n",
+       "        margin-bottom: 0.5em;\n",
+       "        margin-top: 0.2em;\n",
       "    }\n",
       "    a:link{\n",
-       "       font-weight: bold;\n",
       "       color:#447adb;\n",
       "    }\n",
       "    a:visited{\n",
-       "       font-weight: bold;\n",
       "       color: #1d3b84;\n",
       "    }\n",
       "    a:hover{\n",
-       "       font-weight: bold;\n",
       "       color: #1d3b84;\n",
       "    }\n",
       "    a:focus{\n",
-       "       font-weight: bold;\n",
       "       color:#447adb;\n",
       "    }\n",
       "    a:active{\n",
@@ -185,10 +212,10 @@
       "       color:#447adb;\n",
       "    }\n",
       "    .rendered_html :link {\n",
-       "       text-decoration: underline; \n",
+       "       text-decoration: underline;\n",
       "    }\n",
       "    .rendered_html :hover {\n",
-       "       text-decoration: none; \n",
+       "       text-decoration: none;\n",
       "    }\n",
       "    .rendered_html :visited {\n",
       "      text-decoration: none;\n",
@@ -201,7 +228,7 @@
       "    }\n",
       "    .warning{\n",
       "        color: rgb( 240, 20, 20 )\n",
-       "    } \n",
+       "    }\n",
       "    hr {\n",
       "      color: #f3f3f3;\n",
       "      background-color: #f3f3f3;\n",
@@ -248,7 +275,7 @@
       "<IPython.core.display.HTML object>"
      ]
     },
-     "execution_count": 5,
+     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -258,7 +285,7 @@
    "%matplotlib inline\n",
    "import sys\n",
    "sys.path.insert(0,'..') # allow us to format the book\n",
-    "sys.path.insert(0,'../code') \n",
+    "sys.path.insert(0,'../kf_book') \n",
    "# use same formattibng as rest of book so that the plots are\n",
    "# consistant with that look and feel.\n",
    "import book_format\n",
@@ -267,7 +294,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
@@ -338,7 +365,7 @@
    "            w[i] = self.weights[index]\n",
    "\n",
    "        self.particles = p\n",
-    "        self.weights = w / np.sum(w)\n",
+    "        self.weights.fill(1.0 / self.N)\n",
    "\n",
    "\n",
    "    def estimate(self):\n",
@@ -352,7 +379,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
@@ -360,9 +387,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARIAAAETCAYAAAD6a4mGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8a1a908>"
+       "<matplotlib.figure.Figure at 0x208af46e3c8>"
      ]
     },
     "metadata": {},
@@ -439,7 +466,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.5.3"
  }
 },
 "nbformat": 4,
@@ -57,4 +57,10 @@ def animate(filename, func, frames, interval, fig=None, figsize=(6.5, 6.5)):

    anim = animation.FuncAnimation(fig, func, init_func=init_func,
        frames=frames, interval=interval)
-    anim.save(filename, writer='imagemagick')
+        
+    import os
+    basename = os.path.splitext(filename)[0]
+    anim.save(basename + '.mp4', writer='ffmpeg')
+    
+    os.system("ffmpeg -y -i {}.mp4 {}.gif".format(basename, basename))
+    os.remove(basename + '.mp4')
@@ -17,7 +17,6 @@ from __future__ import (absolute_import, division, print_function,
                        unicode_literals)


-from kf_book.book_plots import figsize, end_interactive
 from filterpy.monte_carlo import stratified_resample, residual_resample
 import matplotlib as mpl
 import matplotlib.pyplot as plt
@@ -88,7 +87,7 @@ class ParticleFilter(object):
            w[i] = self.weights[index]

        self.particles = p
-        self.weights = w / np.sum(w)
+        self.weights.fill(1.0 / self.N)


    def estimate(self):
@@ -311,29 +310,27 @@ def test_pf2():

 def plot_cumsum(a):

-    with figsize(y=2):
-        fig = plt.figure()
-        N = len(a)
+    fig = plt.figure()
+    N = len(a)

-        cmap = mpl.colors.ListedColormap([[0., .4, 1.],
-                                          [0., .8, 1.],
-                                          [1., .8, 0.],
-                                          [1., .4, 0.]]*(int(N/4) + 1))
-        cumsum = np.cumsum(np.asarray(a) / np.sum(a))
-        cumsum = np.insert(cumsum, 0, 0)
+    cmap = mpl.colors.ListedColormap([[0., .4, 1.],
+                                      [0., .8, 1.],
+                                      [1., .8, 0.],
+                                      [1., .4, 0.]]*(int(N/4) + 1))
+    cumsum = np.cumsum(np.asarray(a) / np.sum(a))
+    cumsum = np.insert(cumsum, 0, 0)

-        #fig = plt.figure(figsize=(6,3))
-        fig=plt.gcf()
-        ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
-        norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
-        bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
-                                         norm=norm,
-                                         drawedges=False,
-                                         spacing='proportional',
-                                         orientation='horizontal')
-        if N > 10:
-            bar.set_ticks([])
-        end_interactive(fig)
+    #fig = plt.figure(figsize=(6,3))
+    fig=plt.gcf()
+    ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
+    norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
+    bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
+                                     norm=norm,
+                                     drawedges=False,
+                                     spacing='proportional',
+                                     orientation='horizontal')
+    if N > 10:
+        bar.set_ticks([])


 def plot_stratified_resample(a):
@@ -346,24 +343,22 @@ def plot_stratified_resample(a):
    cumsum = np.cumsum(np.asarray(a) / np.sum(a))
    cumsum = np.insert(cumsum, 0, 0)

-    with figsize(y=2):
-        fig = plt.figure()
-        ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
-        norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
-        bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
-                                         norm=norm,
-                                         drawedges=False,
-                                         spacing='proportional',
-                                         orientation='horizontal')
-        xs = np.linspace(0., 1.-1./N, N)
-        ax.vlines(xs, 0, 1, lw=2)
+    fig = plt.figure()
+    ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
+    norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
+    bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
+                                     norm=norm,
+                                     drawedges=False,
+                                     spacing='proportional',
+                                     orientation='horizontal')
+    xs = np.linspace(0., 1.-1./N, N)
+    ax.vlines(xs, 0, 1, lw=2)

-        # make N subdivisions, and chose a random position within each one
-        b = (random(N) + range(N)) / N
-        plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
-        bar.set_ticks([])
-        plt.title('stratified resampling')
-        end_interactive(fig)
+    # make N subdivisions, and chose a random position within each one
+    b = (random(N) + range(N)) / N
+    plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
+    bar.set_ticks([])
+    plt.title('stratified resampling')


 def plot_systematic_resample(a):
@@ -376,24 +371,22 @@ def plot_systematic_resample(a):
    cumsum = np.cumsum(np.asarray(a) / np.sum(a))
    cumsum = np.insert(cumsum, 0, 0)

-    with figsize(y=2):
-        fig = plt.figure()
-        ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
-        norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
-        bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
-                                         norm=norm,
-                                         drawedges=False,
-                                         spacing='proportional',
-                                         orientation='horizontal')
-        xs = np.linspace(0., 1.-1./N, N)
-        ax.vlines(xs, 0, 1, lw=2)
+    fig = plt.figure()
+    ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
+    norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
+    bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
+                                     norm=norm,
+                                     drawedges=False,
+                                     spacing='proportional',
+                                     orientation='horizontal')
+    xs = np.linspace(0., 1.-1./N, N)
+    ax.vlines(xs, 0, 1, lw=2)

-        # make N subdivisions, and chose a random position within each one
-        b = (random() + np.array(range(N))) / N
-        plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
-        bar.set_ticks([])
-        plt.title('systematic resampling')
-        end_interactive(fig)
+    # make N subdivisions, and chose a random position within each one
+    b = (random() + np.array(range(N))) / N
+    plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
+    bar.set_ticks([])
+    plt.title('systematic resampling')


 def plot_multinomial_resample(a):
@@ -406,22 +399,20 @@ def plot_multinomial_resample(a):
    cumsum = np.cumsum(np.asarray(a) / np.sum(a))
    cumsum = np.insert(cumsum, 0, 0)

-    with figsize(y=2):
-        fig = plt.figure()
-        ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
-        norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
-        bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
-                                         norm=norm,
-                                         drawedges=False,
-                                         spacing='proportional',
-                                         orientation='horizontal')
+    fig = plt.figure()
+    ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
+    norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
+    bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
+                                     norm=norm,
+                                     drawedges=False,
+                                     spacing='proportional',
+                                     orientation='horizontal')

-        # make N subdivisions, and chose a random position within each one
-        b = random(N)
-        plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
-        bar.set_ticks([])
-        plt.title('multinomial resampling')
-        end_interactive(fig)
+    # make N subdivisions, and chose a random position within each one
+    b = random(N)
+    plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
+    bar.set_ticks([])
+    plt.title('multinomial resampling')


 def plot_residual_resample(a):
@@ -436,34 +427,36 @@ def plot_residual_resample(a):
                                      [1., .8, 0.],
                                      [1., .4, 0.]]*(int(N/4) + 1))

-    with figsize(y=2):
-        fig = plt.figure()
-        ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
-        norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
-        bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
-                                         norm=norm,
-                                         drawedges=False,
-                                         spacing='proportional',
-                                         orientation='horizontal')
+    fig = plt.figure()
+    ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
+    norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
+    bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
+                                     norm=norm,
+                                     drawedges=False,
+                                     spacing='proportional',
+                                     orientation='horizontal')
+
+    indexes = residual_resample(a_norm)
+    bins = np.bincount(indexes)
+    for i in range(1, N):
+        n =  bins[i-1] # number particles in this sample
+        if n > 0:
+            b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
+            plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
+    bar.set_ticks([])
+    plt.title('residual resampling')

-        indexes = residual_resample(a_norm)
-        bins = np.bincount(indexes)
-        for i in range(1, N):
-            n =  bins[i-1] # number particles in this sample
-            if n > 0:
-                b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
-                plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
-        bar.set_ticks([])
-        plt.title('residual resampling')
-        end_interactive(fig)


 if __name__ == '__main__':
-    plot_residual_resample([.1, .2, .3, .4, .2, .3, .1])
+
+    show_two_pf_plots()
+
+    #plot_residual_resample([.1, .2, .3, .4, .2, .3, .1])

    #example()
    #show_two_pf_plots()

-    a = [.1, .2, .1, .6]
+    #a = [.1, .2, .1, .6]
    #plot_cumsum(a)
    #test_pf()
@@ -24,7 +24,7 @@ def merge_notebooks(outfile, filenames):
    for fname in filenames:
        with io.open(fname, 'r', encoding='utf-8') as f:
            nb = nbformat.read(f, nbformat.NO_CONVERT)
-            remove_formatting(nb)
+            #remove_formatting(nb)
            if not added_appendix and fname[0:8] == 'Appendix':
                remove_links_add_appendix(nb)
                added_appendix = True
@@ -1,7 +1,7 @@
 mkdir tmp
 copy ..\*.ipynb .\tmp
 copy ..\*.py .\tmp
-cp -r ..\code\ .\tmp\code\
+cp -r ..\kf_book\ .\tmp\kf_book\

 cd tmp