removed autoreload magic

06_Multivariate_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:bada82d75b441f9a719a56fd63f5fd1eb4281c1c69b404134e61d0c9a24ccb6e"
+  "signature": "sha256:eb4d01a9103a6af4c69dca8fb129a395d91fe330b2f85573a83c9b6c1f1c5f15"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -29,8 +29,6 @@
      "input": [
       "#format the book\n",
       "%matplotlib inline\n",
-      "%load_ext autoreload\n",
-      "%autoreload 2\n",
       "from __future__ import division, print_function\n",
       "import matplotlib.pyplot as plt\n",
       "import book_format\n",
@@ -39,14 +37,6 @@
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "The autoreload extension is already loaded. To reload it, use:\n",
-        "  %reload_ext autoreload\n"
-       ]
-      },
       {
        "html": [
         "<style>\n",
@@ -267,13 +257,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 24,
+       "prompt_number": 66,
        "text": [
-        "<IPython.core.display.HTML at 0x7f7e34d2d160>"
+        "<IPython.core.display.HTML at 0x7fd239ba6f60>"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 66
     },
     {
      "cell_type": "heading",
@@ -403,11 +393,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d2a3a58>"
+        "<matplotlib.figure.Figure at 0x7fd22da8d208>"
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 67
     },
     {
      "cell_type": "markdown",
@@ -455,7 +445,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 68
     },
     {
      "cell_type": "markdown",
@@ -477,7 +467,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 27
+     "prompt_number": 69
     },
     {
      "cell_type": "markdown",
@@ -495,7 +485,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 28
+     "prompt_number": 70
     },
     {
      "cell_type": "markdown",
@@ -514,7 +504,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 29
+     "prompt_number": 71
     },
     {
      "cell_type": "markdown",
@@ -540,7 +530,7 @@
        ]
       }
      ],
-     "prompt_number": 30
+     "prompt_number": 72
     },
     {
      "cell_type": "markdown",
@@ -571,11 +561,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d2748d0>"
+        "<matplotlib.figure.Figure at 0x7fd22da64550>"
        ]
       }
      ],
-     "prompt_number": 31
+     "prompt_number": 73
     },
     {
      "cell_type": "markdown",
@@ -603,13 +593,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d2d19e8>"
+        "<matplotlib.figure.Figure at 0x7fd22dbaf320>"
        ]
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 74
     },
     {
      "cell_type": "markdown",
@@ -660,17 +650,15 @@
      "metadata": {},
      "outputs": [
       {
-       "ename": "ImportError",
-       "evalue": "No module named 'stats'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-1-d64612a878e3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mP\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\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      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m131\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m stats.plot_covariance_ellipse((2, 7), cov=P, facecolor='g', alpha=0.2, \n",
-        "\u001b[1;31mImportError\u001b[0m: No module named 'stats'"
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fd22d6a5898>"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 75
     },
     {
      "cell_type": "markdown",
@@ -705,11 +693,11 @@
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        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d357c18>"
+        "<matplotlib.figure.Figure at 0x7fd22dbab5f8>"
        ]
       }
      ],
-     "prompt_number": 34
+     "prompt_number": 76
     },
     {
      "cell_type": "markdown",
@@ -787,11 +775,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d2b7940>"
+        "<matplotlib.figure.Figure at 0x7fd22da405f8>"
        ]
       }
      ],
-     "prompt_number": 35
+     "prompt_number": 77
     },
     {
      "cell_type": "markdown",
@@ -824,11 +812,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d0e1400>"
+        "<matplotlib.figure.Figure at 0x7fd22d8662e8>"
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 78
     },
     {
      "cell_type": "markdown",
@@ -867,11 +855,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d29d438>"
+        "<matplotlib.figure.Figure at 0x7fd22d9e0390>"
        ]
       }
      ],
-     "prompt_number": 37
+     "prompt_number": 79
     },
     {
      "cell_type": "markdown",
@@ -894,11 +882,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d2226a0>"
+        "<matplotlib.figure.Figure at 0x7fd22dc07400>"
        ]
       }
      ],
-     "prompt_number": 38
+     "prompt_number": 80
     },
     {
      "cell_type": "markdown",
@@ -927,11 +915,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d149978>"
+        "<matplotlib.figure.Figure at 0x7fd22db79d30>"
        ]
       }
      ],
-     "prompt_number": 46
+     "prompt_number": 81
     },
     {
      "cell_type": "markdown",
@@ -954,11 +942,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d0f2860>"
+        "<matplotlib.figure.Figure at 0x7fd22dbc0a20>"
        ]
       }
      ],
-     "prompt_number": 48
+     "prompt_number": 82
     },
     {
      "cell_type": "markdown",
@@ -981,11 +969,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d379cc0>"
+        "<matplotlib.figure.Figure at 0x7fd22d8113c8>"
        ]
       }
      ],
-     "prompt_number": 51
+     "prompt_number": 83
     },
     {
      "cell_type": "markdown",
@@ -1026,11 +1014,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d13f748>"
+        "<matplotlib.figure.Figure at 0x7fd22db794e0>"
        ]
       }
      ],
-     "prompt_number": 58
+     "prompt_number": 84
     },
     {
      "cell_type": "markdown",
@@ -1067,11 +1055,11 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f7e2d263240>"
+        "<matplotlib.figure.Figure at 0x7fd22e3c3940>"
        ]
       }
      ],
-     "prompt_number": 66
+     "prompt_number": 85
     },
     {
      "cell_type": "markdown",
@@ -1146,11 +1134,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e19a2cf8>"
+        "<matplotlib.figure.Figure at 0x7fd22dbd9c18>"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 86
     },
     {
      "cell_type": "heading",
@@ -1276,7 +1264,7 @@
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 87
     },
     {
      "cell_type": "markdown",
@@ -1305,7 +1293,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 88
     },
     {
      "cell_type": "markdown",
@@ -1334,7 +1322,7 @@
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 89
     },
     {
      "cell_type": "markdown",
@@ -1379,7 +1367,7 @@
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 90
     },
     {
      "cell_type": "markdown",
@@ -1412,7 +1400,7 @@
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 91
     },
     {
      "cell_type": "markdown",
@@ -1447,7 +1435,7 @@
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 92
     },
     {
      "cell_type": "markdown",
@@ -1621,7 +1609,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 22
+     "prompt_number": 93
     },
     {
      "cell_type": "heading",
@@ -2001,7 +1989,7 @@
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 94
     },
     {
      "cell_type": "code",
@@ -2016,14 +2004,14 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 24,
+       "prompt_number": 95,
        "text": [
         "array([[ 0.00005875,  0.001175  ],\n",
         "       [ 0.001175  ,  0.0235    ]])"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 95
     },
     {
      "cell_type": "heading",
@@ -2084,7 +2072,7 @@
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 96
     },
     {
      "cell_type": "markdown",
@@ -2109,7 +2097,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 97
     },
     {
      "cell_type": "markdown",
@@ -2193,7 +2181,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 27
+     "prompt_number": 98
     },
     {
      "cell_type": "markdown",
@@ -2287,7 +2275,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 28
+     "prompt_number": 99
     },
     {
      "cell_type": "markdown",
@@ -2308,9 +2296,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1bd95c0>"
+        "<matplotlib.figure.Figure at 0x7fd22d803a20>"
        ]
       },
       {
@@ -2318,11 +2306,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1b5dba8>"
+        "<matplotlib.figure.Figure at 0x7fd239ba66a0>"
        ]
       }
      ],
-     "prompt_number": 29
+     "prompt_number": 100
     },
     {
      "cell_type": "markdown",
@@ -2398,7 +2386,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 30
+     "prompt_number": 101
     },
     {
      "cell_type": "markdown",
@@ -2427,7 +2415,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 31
+     "prompt_number": 102
     },
     {
      "cell_type": "markdown",
@@ -2485,9 +2473,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e164acf8>"
+        "<matplotlib.figure.Figure at 0x7fd22d911dd8>"
        ]
       },
       {
@@ -2495,11 +2483,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAxEAAAGaCAYAAABwj85UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1b6e828>"
+        "<matplotlib.figure.Figure at 0x7fd22da56828>"
        ]
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 103
     },
     {
      "cell_type": "markdown",
@@ -2663,7 +2651,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 33
+     "prompt_number": 104
     },
     {
      "cell_type": "markdown",
@@ -2729,13 +2717,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1b83978>"
+        "<matplotlib.figure.Figure at 0x7fd22da297f0>"
        ]
       }
      ],
-     "prompt_number": 34
+     "prompt_number": 105
     },
     {
      "cell_type": "markdown",
@@ -2801,7 +2789,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1aecac8>"
+        "<matplotlib.figure.Figure at 0x7fd22d859898>"
        ]
       },
       {
@@ -2809,11 +2797,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e183cda0>"
+        "<matplotlib.figure.Figure at 0x7fd22db9a4e0>"
        ]
       }
      ],
-     "prompt_number": 35
+     "prompt_number": 106
     },
     {
      "cell_type": "markdown",
@@ -2852,11 +2840,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1637d68>"
+        "<matplotlib.figure.Figure at 0x7fd23a740710>"
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 107
     },
     {
      "cell_type": "markdown",
@@ -2881,11 +2869,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1c0da20>"
+        "<matplotlib.figure.Figure at 0x7fd22daef240>"
        ]
       }
      ],
-     "prompt_number": 37
+     "prompt_number": 108
     },
     {
      "cell_type": "markdown",
@@ -2910,11 +2898,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1883ef0>"
+        "<matplotlib.figure.Figure at 0x7fd23a6d3978>"
        ]
       }
      ],
-     "prompt_number": 38
+     "prompt_number": 109
     },
     {
      "cell_type": "markdown",
@@ -2979,11 +2967,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1aa2710>"
+        "<matplotlib.figure.Figure at 0x7fd22da2efd0>"
        ]
       }
      ],
-     "prompt_number": 39
+     "prompt_number": 110
     },
     {
      "cell_type": "markdown",
@@ -3009,11 +2997,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1a17d30>"
+        "<matplotlib.figure.Figure at 0x7fd22dc07518>"
        ]
       }
      ],
-     "prompt_number": 40
+     "prompt_number": 111
     },
     {
      "cell_type": "markdown",
@@ -3081,11 +3069,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e194ec50>"
+        "<matplotlib.figure.Figure at 0x7fd22d844160>"
        ]
       }
      ],
-     "prompt_number": 41
+     "prompt_number": 112
     },
     {
      "cell_type": "markdown",
@@ -3111,11 +3099,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e181c2e8>"
+        "<matplotlib.figure.Figure at 0x7fd22da10f60>"
        ]
       }
      ],
-     "prompt_number": 42
+     "prompt_number": 113
     },
     {
      "cell_type": "markdown",
@@ -3144,11 +3132,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e180e0b8>"
+        "<matplotlib.figure.Figure at 0x7fd22db49be0>"
        ]
       }
      ],
-     "prompt_number": 43
+     "prompt_number": 114
     },
     {
      "cell_type": "markdown",
@@ -3175,11 +3163,11 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1b1b358>"
+        "<matplotlib.figure.Figure at 0x7fd22db3ce10>"
        ]
       }
      ],
-     "prompt_number": 44
+     "prompt_number": 115
     },
     {
      "cell_type": "markdown",
@@ -3246,21 +3234,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1a9a208>"
+        "<matplotlib.figure.Figure at 0x7fd22d911e48>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e18b1f28>"
+        "<matplotlib.figure.Figure at 0x7fd22db88be0>"
        ]
       }
      ],
-     "prompt_number": 45
+     "prompt_number": 116
     },
     {
      "cell_type": "markdown",
@@ -3331,11 +3319,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1a9cb70>"
+        "<matplotlib.figure.Figure at 0x7fd22d6bb940>"
        ]
       }
      ],
-     "prompt_number": 46
+     "prompt_number": 117
     },
     {
      "cell_type": "markdown",
@@ -3408,13 +3396,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e175cf98>"
+        "<matplotlib.figure.Figure at 0x7fd22d724748>"
        ]
       }
      ],
-     "prompt_number": 47
+     "prompt_number": 118
     },
     {
      "cell_type": "heading",
@@ -3466,11 +3454,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e199aa58>"
+        "<matplotlib.figure.Figure at 0x7fd22d7242e8>"
        ]
       }
      ],
-     "prompt_number": 48
+     "prompt_number": 119
     },
     {
      "cell_type": "markdown",
@@ -3510,11 +3498,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e199ae80>"
+        "<matplotlib.figure.Figure at 0x7fd22da2b908>"
        ]
       }
      ],
-     "prompt_number": 49
+     "prompt_number": 120
     },
     {
      "cell_type": "markdown",
@@ -3544,11 +3532,11 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff8e1747978>"
+        "<matplotlib.figure.Figure at 0x7fd22db35978>"
        ]
       }
      ],
-     "prompt_number": 50
+     "prompt_number": 121
     },
     {
      "cell_type": "heading",