Reran so output cells are in order.

09-Nonlinear-Filtering.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -254,7 +254,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -364,16 +364,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEPCAYAAACTNwgMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89719b0>"
+       "<matplotlib.figure.Figure at 0x73a6828>"
       ]
      },
      "metadata": {},
@@ -421,16 +421,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "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 0xa1f3f60>"
+       "<matplotlib.figure.Figure at 0x7390f60>"
       ]
      },
      "metadata": {},
@@ -454,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -462,9 +462,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1f3f28>"
+       "<matplotlib.figure.Figure at 0x7f86e48>"
       ]
      },
      "metadata": {},
@@ -503,9 +503,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa775eb8>"
+       "<matplotlib.figure.Figure at 0xa65ca90>"
       ]
      },
      "metadata": {},
@@ -545,9 +545,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa8c9e10>"
+       "<matplotlib.figure.Figure at 0xa76bc88>"
       ]
      },
      "metadata": {},
@@ -579,8 +579,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input  mean, variance: -0.0017, 0.9984\n",
-      "output mean, variance: -0.1220, 2.4009\n"
+      "input  mean, variance: 0.0006, 1.0045\n",
+      "output mean, variance: -0.1256, 2.4130\n"
      ]
     }
    ],
@@ -607,9 +607,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa7980b8>"
+       "<matplotlib.figure.Figure at 0x7f72f60>"
       ]
      },
      "metadata": {},
@@ -619,7 +619,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output mean, variance: 0.0025, 2.2465\n"
+      "output mean, variance: -0.0009, 2.2601\n"
      ]
     }
    ],
@@ -651,8 +651,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "linear    output mean, variance: 0.1453, 7470.2044\n",
-      "nonlinear output mean, variance: -8.8565, 30504.9480\n"
+      "linear    output mean, variance: -0.0517, 7515.5491\n",
+      "nonlinear output mean, variance: -9.3449, 30643.4883\n"
      ]
     }
    ],
@@ -687,9 +687,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e97978>"
+       "<matplotlib.figure.Figure at 0x8146b70>"
       ]
      },
      "metadata": {},
@@ -729,20 +729,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xa1f39e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'P' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-11-9dc12e2312c8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnonlinear_internal\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnonlinear_internal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mC:\\Users\\Roger\\Dropbox\\book\\code\\nonlinear_internal.py\u001b[0m in \u001b[0;36mplot1\u001b[1;34m()\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mplot1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_covariance_ellipse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'g'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'P' is not defined"
+     ]
     }
    ],
    "source": [
@@ -759,22 +761,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x83d6780>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nonlinear_internal.plot2()"
    ]
@@ -788,22 +779,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x819f160>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nonlinear_internal.plot3()"
    ]
@@ -817,22 +797,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8297588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "nonlinear_internal.plot4()"
    ]