Simplified namedtuple explanation.

04-One-Dimensional-Kalman-Filters.ipynb

@@ -17,7 +17,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -307,14 +309,15 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAEkCAYAAACvyM6aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d75fefbf28>"
+       "<matplotlib.figure.Figure at 0x1dca9506240>"
       ]
      },
      "metadata": {},
@@ -339,20 +342,22 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean of readings is 9.949\n"
+      "Mean of readings is 10.001\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d760ef7400>"
+       "<matplotlib.figure.Figure at 0x1dca9cc60f0>"
       ]
      },
      "metadata": {},
@@ -403,14 +408,15 @@
    "cell_type": "code",
    "execution_count": 4,
    "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 0x1d76090f390>"
+       "<matplotlib.figure.Figure at 0x1dcab0e9c18>"
       ]
      },
      "metadata": {},
@@ -504,21 +510,22 @@
     "\n",
     "It makes sense that the predicted position is the previous position plus the movement. What about the variance? It is harder to form an intuition about this. However, recall that with the `predict()` function for the discrete Bayes filter we always lost information. We don't really know where the dog is moving, so the confidence should get smaller (variance gets larger). $\\mu_{f_x}^2$ is the amount of uncertainty added to the system due to the imperfect prediction about the movement, and so we would add that to the existing uncertainty. \n",
     "\n",
-    "Let's take advantage of the `namedtuple` class in Python's `collection` module to implement a Gaussian object. We could implement a Gaussian using a tuple, where $\\mathcal N(10, 0.04)$ is implemented in Python as `g = (10., 0.04)`. We would acces the mean with `g[0]` and the variance with `g[1]`.\n",
+    "Let's take advantage of the `namedtuple` class in Python's `collection` module to implement a Gaussian object. We could implement a Gaussian using a tuple, where $\\mathcal N(10, 0.04)$ is implemented in Python as `g = (10., 0.04)`. We would access the mean with `g[0]` and the variance with `g[1]`.\n",
     "\n",
-    "`namedtuple` works the same as a tuple, except you provide it with a type name and field names. "
+    "`namedtuple` works the same as a tuple, except you provide it with a type name and field names. It's not important to understand, but I modified the `__repr__` method to display its value using the notation in this chapter. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
     "from collections import namedtuple\n",
-    "gaussian = namedtuple('Gaussian', ['mean', 'var'])"
+    "gaussian = namedtuple('Gaussian', ['mean', 'var'])\n",
+    "gaussian.__repr__ = lambda s: '𝒩(μ={:.3f}, 𝜎²={:.3f})'.format(s[0], s[1])"
    ]
   },
   {
@@ -532,6 +539,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
+    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -539,8 +547,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Gaussian(mean=3.4, var=10.1)\n",
-      "Gaussian(mean=4.5, var=0.04000000000000001)\n"
+      "𝒩(μ=3.400, 𝜎²=10.100)\n",
+      "𝒩(μ=4.500, 𝜎²=0.040)\n"
      ]
     }
    ],
@@ -551,50 +559,6 @@
     "print(g2)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "I find that the printed value is a bit verbose, so I replace the `__repr__` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def gaussian_repr(self):\n",
-    "    return '𝒩(μ={:.3f}, 𝜎²={:.3f})'.format(self[0], self[1])\n",
-    "gaussian.__repr__ = gaussian_repr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This gives us the more readable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "𝒩(μ=4.500, 𝜎²=0.040)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(g2)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -604,7 +568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "collapsed": true
    },
@@ -629,8 +593,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -638,7 +604,7 @@
        "𝒩(μ=25.000, 𝜎²=0.530)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -703,8 +669,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -712,7 +680,7 @@
        "𝒩(μ=10.377, 𝜎²=0.037)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -758,8 +726,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
+    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -772,9 +741,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEdCAYAAAC12imkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d75eeb26a0>"
+       "<matplotlib.figure.Figure at 0x1dcab1fdba8>"
       ]
      },
      "metadata": {},
@@ -811,14 +780,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEdCAYAAAC12imkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d762185208>"
+       "<matplotlib.figure.Figure at 0x1dcab513f98>"
       ]
      },
      "metadata": {},
@@ -864,14 +835,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 12,
+   "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 0x1d76229acc0>"
+       "<matplotlib.figure.Figure at 0x1dcab184320>"
       ]
      },
      "metadata": {},
@@ -879,9 +852,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEdCAYAAAC12imkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d7622c6f28>"
+       "<matplotlib.figure.Figure at 0x1dcab184a58>"
       ]
      },
      "metadata": {},
@@ -916,20 +889,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
+    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "575bf26bb2794e9094e1ccf47eb34876",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEdCAYAAAC12imkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "A Jupyter Widget"
+       "<matplotlib.figure.Figure at 0x1dcab529b70>"
       ]
      },
      "metadata": {},
@@ -962,7 +932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -1001,8 +971,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {
+    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
@@ -1054,8 +1025,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1155,7 +1128,7 @@
        "        };\n",
        "\n",
        "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
+       "        this.ws.close();\n",
        "    }\n",
        "\n",
        "    this.ws.onmessage = this._make_on_message_function(this);\n",
@@ -1802,12 +1775,9 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -1856,7 +1826,7 @@
     {
      "data": {
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+       "<img src=\"data:image/png;base64,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 truncated
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2018,8 +1988,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -2034,9 +2006,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d762255400>"
+       "<matplotlib.figure.Figure at 0x1dcaa0c34e0>"
       ]
      },
      "metadata": {},
@@ -2180,14 +2152,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
+   "execution_count": 18,
+   "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 0x1d762261518>"
+       "<matplotlib.figure.Figure at 0x1dcaa0c30f0>"
       ]
      },
      "metadata": {},
@@ -2208,7 +2182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {
     "collapsed": true
    },
@@ -2330,14 +2304,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAACICAYAAADjyzHQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAC8R+ABAADeI/AAAADvEXgAAID3/g/UwF1MheIWqgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d7620457b8>"
+       "<matplotlib.figure.Figure at 0x1dcaa023a90>"
       ]
      },
      "metadata": {},
@@ -2361,16 +2337,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d7622d6278>"
+       "<matplotlib.figure.Figure at 0x1dcab53cd68>"
       ]
      },
      "metadata": {},
@@ -2402,14 +2379,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
+   "execution_count": 22,
+   "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 0x1d762221048>"
+       "<matplotlib.figure.Figure at 0x1dcab55df28>"
       ]
      },
      "metadata": {},
@@ -2454,7 +2433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "metadata": {
     "collapsed": true
    },
@@ -2477,16 +2456,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {
+    "collapsed": false,
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d76242ebe0>"
+       "<matplotlib.figure.Figure at 0x1dcab626630>"
       ]
      },
      "metadata": {},
@@ -2501,9 +2481,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d762424710>"
+       "<matplotlib.figure.Figure at 0x1dcab69e588>"
       ]
      },
      "metadata": {},
@@ -2596,14 +2576,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
+   "execution_count": 25,
+   "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 0x1d76227cc88>"
+       "<matplotlib.figure.Figure at 0x1dcaa00c160>"
       ]
      },
      "metadata": {},
@@ -2652,14 +2634,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1d762103c18>"
+       "<matplotlib.figure.Figure at 0x1dcac77df60>"
       ]
      },
      "metadata": {},
@@ -2707,14 +2691,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
+   "execution_count": 27,
+   "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 0x1d7622007f0>"
+       "<matplotlib.figure.Figure at 0x1dcac786438>"
       ]
      },
      "metadata": {},
@@ -2759,14 +2745,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
+   "execution_count": 28,
+   "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 0x1d7622eb1d0>"
+       "<matplotlib.figure.Figure at 0x1dcab1694e0>"
       ]
      },
      "metadata": {},
@@ -2805,14 +2793,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
+   "execution_count": 29,
+   "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 0x1d761040160>"
+       "<matplotlib.figure.Figure at 0x1dcab1019b0>"
       ]
      },
      "metadata": {},
@@ -2859,18 +2849,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "41913d828ed64349bcefc35aa3bad9ad",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
-       "A Jupyter Widget"
+       "<matplotlib.figure.Figure at 0x1dcab21d9e8>"
       ]
      },
      "metadata": {},
@@ -2906,18 +2893,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8375a30f5dcd44699c7380d0ad65af2b",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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truncated
       "text/plain": [
-       "A Jupyter Widget"
+       "<matplotlib.figure.Figure at 0x1dcaa00c898>"
       ]
      },
      "metadata": {},
@@ -2984,7 +2969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {
     "collapsed": true
    },
@@ -3002,16 +2987,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "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 0x1d76222d128>"
+       "<matplotlib.figure.Figure at 0x1dcab2216a0>"
       ]
      },
      "metadata": {},
@@ -3102,8 +3088,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -3113,7 +3100,7 @@
        "(11.0, 7.0)"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3138,8 +3125,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -3147,7 +3136,7 @@
        "(11.364, 4.455)"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3185,7 +3174,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
@@ -3199,28 +3188,28 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.5.2"
   },
   "widgets": {
    "state": {
-    "58ed37dc23fe44cc9a0e20ffc9c47414": {
+    "59e10c8995bf474fb014aa9311c61adb": {
      "views": [
       {
-       "cell_index": 79
+       "cell_index": 83
       }
      ]
     },
-    "8b8fb12fbc4048a0af7d29dfc57d5fb9": {
+    "619dfa879db14f7884e414edbc91eabe": {
      "views": [
       {
-       "cell_index": 29
+       "cell_index": 85
       }
      ]
     },
-    "ec6802d3ecf14e67bbcf34b40aa0e3c4": {
+    "c2cf0c5a1b8249b2a8277302662a49e7": {
      "views": [
       {
-       "cell_index": 77
+       "cell_index": 33
       }
      ]
     }