Reran chapters.

11-Extended-Kalman-Filters.ipynb

@@ -17,9 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -349,9 +347,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -441,7 +437,6 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -602,9 +597,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from math import sqrt\n",
@@ -627,9 +620,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def hx(x):\n",
@@ -650,9 +641,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from numpy.random import randn\n",
@@ -751,9 +740,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -839,7 +826,6 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -912,9 +898,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1014,7 +998,6 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1071,9 +1054,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1124,9 +1105,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1235,9 +1214,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1481,7 +1458,6 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [],
@@ -1559,7 +1535,6 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1606,9 +1581,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1649,7 +1622,6 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1689,7 +1661,6 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1729,7 +1700,6 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1793,9 +1763,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1831,9 +1799,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1883,9 +1849,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

12-Particle-Filters.ipynb

@@ -17,7 +17,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -347,13 +349,15 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnAAAAEOCAYAAAAJ0yqPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958a845668>"
+       "<matplotlib.figure.Figure at 0x248bd351ba8>"
       ]
      },
      "metadata": {},
@@ -417,13 +421,15 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAngAAAEQCAYAAAAj2AmQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958d562c88>"
+       "<matplotlib.figure.Figure at 0x248bdc881d0>"
       ]
      },
      "metadata": {},
@@ -500,7 +506,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -512,9 +520,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAEJCAYAAADRpICmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvX1wlOd9NnqtVoJds4gC+sA2VYRkmHFMCbWpEITj2pWT\nOQ1TChNqXohjMm/SCeOUsRzo5NV5M286HZ9R2+CYHFriM9PmDSlBgVcpWD3qyTHWG9ehSMjYoxCM\nOxh9WAUbSQjCamEXaT/OH6vr3t/923sh7RsQKPc1k/Hq2ee5Px9yX/v7uH6BTCaTgYeHh4eHh4eH\nx12BoqkegIeHh4eHh4eHRw6enHl4eHh4eHh43EXw5MzDw8PDw8PD4y6CJ2ceHh4eHh4eHncRPDnz\n8PDw8PDw8LiLUDzVA/j34urVq1M9BA8PDw8PDw+PXwnmzJmTd81bzjw8PDw8PDw87iJ4cubh4eHh\n4eHhcRfhnnNrSrhMgb+uOHnyJABgxYoVUzwSj5vB79O9Ab9Pdz/8Ht0b8Pvkxq1CtLzlzMPDw8PD\nw8PjLoInZx4eHh4eHh4edxE8OfPw8PDw8PDwuIvgyZmHh4eHh4eHx12E20LO3nzzTaxbtw4PPvgg\nAoEAvve9793ymZ///Of43d/9XYTDYTz44IP48z//c2QymdsxPA8PDw8PDw+Puxa3hZzFYjEsXboU\n3/72txEOh295fzQaxac+9SlUVlbirbfewre//W1885vfxLe+9a3bMTwPDw8PDw8Pj7sWt0VK4zOf\n+Qw+85nPAAC+8IUv3PL+H/zgB7h+/Tr27duHcDiMpUuX4l//9V/xrW99C1/96lcRCARuxzA9PDw8\nPDw8PO46BDK32XcYiUTw13/91zclac8++yxGR0fR3t5urr311luoq6tDX18fFi1aZK5LbZD333//\ntozZw+NOIrn1RQBA8b6vO79PvdQCAAju2Fzw+8ypXgSW1Ra8x3UvcbNn/j1t/0efl/P7X+1Ptwfc\nen3/o+1L/DJjvtU+Avlj1Wtzq+cL9SvH9+9Zn1+mT70ewR2b/5fa/I/O08PjXsLixYvNZ5dm610h\nQnvx4kUsXLjQulZZWWm+k+TMw2O6gIdQ5sMR53fpN95BYN4ci0jJgzBzqjf738tXgcR43vdA/gGX\nOdWLzOWrCIi/k1tfzF6bN8ciBbwXADB5nwYP/F+GALlImOt7jmviD/7UzN+MW42RayT7ZbtmDh+O\nAKEZ2PPKyzh2/E3MnTsPjzy8FACwfdsLAIA9r7yc9/e7750GADzy8FJzHQBOn/k5AGDpx38Lp8/8\nHPMu/AJz08XoLb6ON1+5iO3bXsibi9xD13qY8X44AqTSSG590cxbP/vLEDZNwjOXryLzxjt5Y7jp\nM2J/Ui+1OMlSoX3kO+0aa6F1kMiI902TSr4LRU886gmcx7TFXUHO/qPwisM5eBXmewNyn3rOfjN7\nsWYhEgMXUPzlb2L52wfR89gmJAcuIAAgFJqJ0rIyRI/3oHTfUaCsDABw6XAHMvEEisIhIBjEjJqF\nKF29HAAQPd4DABgfGkXx2fPmes2eJvT93krzGQD6tjcjerwH48EgcDWG4i9/M3t/WRmGL55AAMj2\ncTWGdDwBAAhVPwgASAxcQOBqDCtaXkJPaCYSAxeQXv9fULahwcy3puUl87nnsU0AgOUrVqCv7Cii\nofMYHxrFjMr51hh5b2J8Ijf/0HkkxieQOT+E4i9n1y09NIrA+ASKrsbMe7+mYSXWXonjY1XV+OCf\nj2JJYgZqa2pRuno5yiNDCBYHER27is7uf0FFeSVWrFiBnU2N6O0/h/q6VaadY8ffxLXr1zCjpASh\nUAiNX/sK6utWAQC6yq6ivm4Vvtj8Ev5HUyMWHuxCvKIM7z1Zg/LJvT3Q/+cYHh5GRUUFPh4Ko7Ss\nzJobAMzjfn35m0gMXEBROIRwzUKMD40CV2OYcfY8Sif3K/rlb6IYQOnq5bh0uAPpeALzysrM+1Cj\n/t33nP0mxq/GUDbZb89jm5AYuICZZ89b78b40ChClfOz1376M2TiCQSuxlC2oQHR0HnrnSrdd9TM\ngegrO4pLV3+GdDyBUPWDKF29HNHjPUgUBVAUDmHGZH+l+47i0k9/ZvY6erwHmHzXopPjAIAZHMvV\nGGZUzs9e/+nPrLlGQ+cRv55Auv040m+8g8qNn84bl8fdA382uXGrCgF3BTlbsGABhoaGrGv8e8GC\nBVMxJA+P2w5zSAHIJFOInxtE3/Zm832o+kEsf/sguuavQWYimfd8UThkDrPlbx80RCsxcMF8D+TI\nmguSpF063GEOyZo9TYYEAOLQHBo1BzaJFecyPjSKdDxhkcPo8R6Url6Omj1N5jnOkWPW4+CzbLdm\nTxP6tjeb9olfFKVwZk4SFRX34VBTo7n+f824gMDwhyieFQRmAVW/EQTODKC+bhXOnhrAmoaVGB4Z\nypGt7k4Mjwyhq7sTS5ZVo6K8EnPnzsONGzcAAMMjQ6gor8xbu52TfZ7fVI/zk9e6ujuxpmElMAvA\nIqC+rgZbmneb+7u6O/HfRu4DALzR/mqWvCGMTDKFVDSGRDyBii1rrT3T6zmjcj4SAxcQPd6D5W8f\nBCCIr1hTkmR+p/cMAFJj15CYJFa8nhi4gEuHO7IEbbJv7p1sj9fS8YR5P7lX+pno8R5r7yT474B7\nnhi4gEwyhdLJ8V863IFLhzvy3rXU1ZixGHt4TDfcFeRs1apV+NrXvoZEIoFQKHugHD16FA888ACq\nq6undnAeHrcJJFJF4RCCs2eZ6zzUSlcvzxKz8SQCM7L/VEm8aL3ifSRmQI6USYuUxKXDHdbBLjGj\ncr45YOUhLkkSUbahwbJYyMOT/fDAJfkz1hDkSIccx6XDHc616nqrE2VFKTy0ZS32RrI/3Lq6BzA4\nOICqWRnUowZd3Z2or1uF4ZEhxGIxhMJhi1SROJFs7WrebQgWAAwODgDIkrFVdZ/EK9/+O2z5zxuR\niMfNPXymq7sTAAzBkxgeyY7PRegGBwfwpZlAKBzG5y/OQjxxHe9s/T08t3o5hg+0591PgiwJmyTi\nPY9tcu6xJMZEJplCOp4wZEiSbCC3x90Ln0IqGrP2Qu4zSTKJXlE4hFTyGsaHRi1iJp+R743sm5+N\nxW2SmAWKg3nvgiSWMyrnI55KoeiJR73VzGNa4rZJafT09KCnpwfpdBqDg4Po6enB4OAgAKCpqQkN\nDTnXx5YtW3DffffhC1/4Ak6fPo1/+Id/wF/8xV/4TE2PaY+icAhlGxpQd/51Y6kY+n4bEgMXULOn\nyVgkMhNJJAYuIFT9oHEf8X88nEpXL8fytw+i7vzrqDv/ep6rsG97M2r2NBnLS/fCp9Dz2CZzvWxD\nQ55rVB7u8oCV7crPtOiwPRI4Wk609Yvtsu10PIF0PIGyDQ3oWzwPeyND2NnUiD+7fgp/vvi66aet\n/QiGR4ZQVVWN+rpV2DVpnerq7sTZUwPYsukZAFkyRNJVX7cKg4MDiMVipm9azSrKKxEKh1FVVQ0g\n69bc9vwXzb19/b041NriJGZd3Z3WNZIyWuNIAHc170ZVVbUhjX+/4Br+e1kUbe1H8OyZI6gfPYad\nyyfw7JkjZm9JfggSILnekgTLvZDXACBQHERROITo8R7Ezw1i+EA7Slcvt9zQQJZ0B0qK86ym3Cs5\nJo4xUBxEOp7A8IF2i1Rxb2k5HR8aNe+IhCRmwdmzEKp+0LwLMyrnm3dJ/nDxMWce0xm3xXJ28uRJ\nPPnkk+bvb3zjG/jGN76BrVu34nvf+x4++ugj9PbmgkjnzJmDo0eP4itf+QpWrFiBuXPnYseOHfjq\nV796O4bn4TGlSL3Ugr6yo5YLSoKHKACEF38M8XODQCaDonCo4DO0atGSUrOnqaBFgXFt+vAudD/b\nk+4xAKYNHu66Pd2GtKjxGpAjgn3bmxGdVYKx2Bh2njmC+idz5Edar4iK8krLciU/k6wdOLgf6XQa\nwyND2NW8G13dnRgcHMDwyJCxouln29qPmM/FwSBC4TDGomMYT0+grf2I0yIm4bKmEcc6TljWurb2\nIxZZHB4ZQiIex966yTCPT9+PXc0HzfrQ6knwPZGEV1o9aY2r2LLWcqNLS63LwiUtddoyJ/dRW+fi\n5wbNWNhW9HiPZdnljwvtAk0MXEBw9izUnX/duLEJ9iOth5cnY5k8PKYjbgs5e+KJJ26q7u+qGPBb\nv/VbePPNN2/HcDw87mroA7d+9Jj57HI9yntljJj8Xh6MgO1m6l74FFJj16wDWsZ+8WCUFhMJHSMk\n+6nYstYaC+OFaEFjP6loDEP7XkWgpBg3AhlcSt/Av135AOc/V58lQKi0rFEkUTubGg150iSILsed\nTY2GnAFAUVERKsorsbOp0bSViMeNRY3WMrbR1d2JRCKBV779dyZhoK39CBLxuCGJ9XWrLKvbsY4T\nAGAI37q16602JTgvAEjE44hEIli3dj2WLKs2RO1Qa4sZF+Pg2IfcrzIRl8XrmsjQWiaD7qVLWrsc\ngZwVlt91L3zK6o8xifId0DFm+hr7ZruyX1pX5d9AllzSYiy/79veDGz9FDw8pivuipgzD49fJwR3\nbDbZda5DrlCQfCGQjMmsPEKSLOk6RDpjgsHpapQJBK54NR74POjlPZIM6DGn4wnLymKsPIFsRt+N\nohTejV/GKxhBpP2iISLSskWyQ0tTcTCIru5OQ1ikNQrIkaRIJJK3XhXllRYxI9FiO8MjQ0glU9jz\nysvo7T9nnuFYgCzB6uvvRTqdRiIet/pPxOMWAXONa93a9WhrP4JQOIyzpwbM3GipA3Lk81BrC4ZH\nsu5dRLJk70D9OqAE2DL5jsi1l7FkMoMSyLkoXQkH0rrJeDa+l3w/SALptibGh0YxfLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958d49d630>"
+       "<matplotlib.figure.Figure at 0x248bfe95860>"
       ]
      },
      "metadata": {},
@@ -560,13 +568,15 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAEJCAYAAAA6kzulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958d59aba8>"
+       "<matplotlib.figure.Figure at 0x248bff52240>"
       ]
      },
      "metadata": {},
@@ -592,13 +602,15 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAEHCAYAAABldKiqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958d6675c0>"
+       "<matplotlib.figure.Figure at 0x248c0087ba8>"
       ]
      },
      "metadata": {},
@@ -676,7 +688,9 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -828,7 +842,9 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -947,6 +963,7 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
+    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -960,9 +977,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958e7090f0>"
+       "<matplotlib.figure.Figure at 0x248c0107f98>"
       ]
      },
      "metadata": {},
@@ -1077,7 +1094,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1089,9 +1108,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAEOCAYAAABCe/liAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958e6f5710>"
+       "<matplotlib.figure.Figure at 0x248c00fa198>"
       ]
      },
      "metadata": {},
@@ -1133,6 +1152,7 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
+    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1146,9 +1166,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958e880668>"
+       "<matplotlib.figure.Figure at 0x248c00fa6a0>"
       ]
      },
      "metadata": {},
@@ -1184,7 +1204,9 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1196,9 +1218,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAEOCAYAAAANJvqyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958f850630>"
+       "<matplotlib.figure.Figure at 0x248c00e90b8>"
       ]
      },
      "metadata": {},
@@ -1222,7 +1244,9 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1234,9 +1258,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958e701668>"
+       "<matplotlib.figure.Figure at 0x248c0193f28>"
       ]
      },
      "metadata": {},
@@ -1357,6 +1381,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
+    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1369,9 +1394,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAABLCAYAAAAvf8D7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGgAAgGHEGgAAgGH/B77nceToDeobAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAABLCAYAAAAvf8D7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958d5d48d0>"
+       "<matplotlib.figure.Figure at 0x248c0297128>"
       ]
      },
      "metadata": {},
@@ -1396,7 +1421,9 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def multinomal_resample(weights):\n",
@@ -1416,14 +1443,15 @@
    "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 0x1958d6c56d8>"
+       "<matplotlib.figure.Figure at 0x248c02aa748>"
       ]
      },
      "metadata": {},
@@ -1501,13 +1529,15 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958d63bb70>"
+       "<matplotlib.figure.Figure at 0x248c0319390>"
       ]
      },
      "metadata": {},
@@ -1544,13 +1574,15 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958e8b97b8>"
+       "<matplotlib.figure.Figure at 0x248c02fc0b8>"
       ]
      },
      "metadata": {},
@@ -1617,13 +1649,15 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958e7a34e0>"
+       "<matplotlib.figure.Figure at 0x248c0288390>"
       ]
      },
      "metadata": {},
@@ -1694,14 +1728,15 @@
    "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 0x1958d60d0b8>"
+       "<matplotlib.figure.Figure at 0x248c01b8d68>"
       ]
      },
      "metadata": {},
@@ -1709,9 +1744,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958fa0c080>"
+       "<matplotlib.figure.Figure at 0x248c032c0b8>"
       ]
      },
      "metadata": {},
@@ -1719,9 +1754,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958e76d6d8>"
+       "<matplotlib.figure.Figure at 0x248c0084be0>"
       ]
      },
      "metadata": {},
@@ -1729,9 +1764,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1958e94a780>"
+       "<matplotlib.figure.Figure at 0x248c00219e8>"
       ]
      },
      "metadata": {},
@@ -1796,7 +1831,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
@@ -1810,7 +1845,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,