Changes to bring page count down.

Made plots smaller, moved legends outside of plot (so smallersize
is still readable), moved plotting code into external modules.

01_g-h_filter.ipynb

@@ -109,7 +109,7 @@
        "        color: #1d3b84;\n",
        "        font-size: 16pt;\n",
        "        margin-bottom: 0em;\n",
-       "        margin-top: 1.5em;\n",
+       "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "        white-space: nowrap;\n",
        "    }\n",
@@ -133,6 +133,11 @@
        "        overflow-y: scroll;\n",
        "        max-height: 50000px;\n",
        "    }\n",
+       "    div.output_wrapper{\n",
+       "        margin-top:0.2em;\n",
+       "        margin-bottom:0.2em;\n",
+       "}\n",
+       "\n",
        "    code{\n",
        "      font-size: 70%;\n",
        "    }\n",
@@ -253,6 +258,8 @@
    "source": [
     "#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",
@@ -315,9 +322,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAEPCAYAAACJC+iCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7401ef0>"
+       "<matplotlib.figure.Figure at 0x6e162e8>"
       ]
      },
      "metadata": {},
@@ -325,13 +332,9 @@
     }
    ],
    "source": [
-    "plt.errorbar([160], [1], xerr=8, fmt='o', label='A', capthick=2, capsize=10)\n",
-    "plt.errorbar([170], [1.05], xerr=8, fmt='o', label='B', capthick=2, capsize=10)\n",
-    "plt.ylim(0, 2)\n",
-    "plt.xlim(145, 185)\n",
-    "plt.legend()\n",
-    "plt.gca().axes.yaxis.set_ticks([])\n",
-    "plt.show()"
+    "import book_plots\n",
+    "with book_format.figsize(y=1.5):\n",
+    "   book_plots.plot_errorbars([(160, 8, 'A'), (170, 8, 'B')], xlims=(145, 185))"
    ]
   },
   {
@@ -354,9 +357,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7446518>"
+       "<matplotlib.figure.Figure at 0x6e16be0>"
       ]
      },
      "metadata": {},
@@ -364,13 +367,8 @@
     }
    ],
    "source": [
-    "plt.errorbar([160], [1], xerr=3, fmt='o', label='A', capthick=2, capsize=10)\n",
-    "plt.errorbar([170], [1.05], xerr=3*3, fmt='o', label='B', capthick=2, capsize=10)\n",
-    "plt.ylim(0, 2)\n",
-    "plt.xlim(145, 185)\n",
-    "plt.legend()\n",
-    "plt.gca().axes.yaxis.set_ticks([])\n",
-    "plt.show()"
+    "with book_format.figsize(y=1.5):\n",
+    "    book_plots.plot_errorbars([(160, 3, 'A'), (170, 9, 'B')], xlims=(145, 185))"
    ]
   },
   {
@@ -391,9 +389,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7401dd8>"
+       "<matplotlib.figure.Figure at 0x7637780>"
       ]
      },
      "metadata": {},
@@ -401,12 +399,8 @@
     }
    ],
    "source": [
-    "plt.errorbar([160], [1], xerr=1, fmt='o', label='A', capthick=2, capsize=10)\n",
-    "plt.errorbar([170], [1.05], xerr=9, fmt='o', label='B', capthick=2, capsize=10)\n",
-    "plt.ylim(0, 2)\n",
-    "plt.legend()\n",
-    "plt.gca().axes.yaxis.set_ticks([])\n",
-    "plt.show()"
+    "with book_format.figsize(y=1.5):\n",
+    "    book_plots.plot_errorbars([(160, 1, 'A'), (170, 9, 'B')], xlims=(145, 185))"
    ]
   },
   {
@@ -433,22 +427,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0292\n"
+      "Average of measurements is 165.0636\n"
      ]
     }
    ],
    "source": [
-    "N = 10000\n",
-    "import random\n",
-    "weight_sum = 0\n",
-    "for i in range(N):\n",
-    "    # choose a random number between 160 and 170, assuming true\n",
-    "    # weight of 165\n",
-    "    measurement = random.uniform(160, 170) \n",
-    "    weight_sum += measurement\n",
-    "    \n",
-    "average = weight_sum / N\n",
-    "print('Average of measurements is {:.4f}'.format(average))"
+    "import numpy as np\n",
+    "\n",
+    "measurements = np.random.uniform(160, 170, size=10000)\n",
+    "print('Average of measurements is {:.4f}'.format(measurements.mean()))"
    ]
   },
   {
@@ -457,7 +444,7 @@
    "source": [
     "The exact number printed depends on your random number generator, but it should be very close to 165.\n",
     "\n",
-    "This code makes one assumption that probably isn't true - that the scale is just as likely to read 160 as 165 for a true weight of 165 lbs. This is almost never true. Real sensors are more likely to get readings nearer the true value, and are less and less likely to get readings the further away from the true value it gets. We will cover this in detail in the Gaussian chapter. For now, I will use without further explanation the `random.gauss()` function, which will produce more values nearer 165 lbs, and fewer further away. Take it on faith for now that this will produce noisy measurements very similar to how a real scale would."
+    "This code makes one assumption that probably isn't true - that the scale is just as likely to read 160 as 165 for a true weight of 165 lbs. This is almost never true. Real sensors are more likely to get readings nearer the true value, and are less and less likely to get readings the further away from the true value it gets. We will cover this in detail in the Gaussian chapter. For now, I will use without further explanation the `numpy.random.normal()` function, which will produce more values nearer 165 lbs, and fewer further away. Take it on faith for now that this will produce noisy measurements very similar to how a real scale would."
    ]
   },
   {
@@ -471,20 +458,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9868\n"
+      "Average of measurements is 165.0104\n"
      ]
     }
    ],
    "source": [
-    "N = 10000\n",
-    "weight_sum = 0\n",
-    "for i in range(N):\n",
-    "    # choose a random number centered around 165 lbs, with a 5 lb error.\n",
-    "    measurement = random.gauss(165, 5.) \n",
-    "    weight_sum += measurement\n",
-    "    \n",
-    "average = weight_sum / N\n",
-    "print('Average of measurements is {:.4f}'.format(average))"
+    "measurements = np.random.normal(165, 5, size=10000)\n",
+    "print('Average of measurements is {:.4f}'.format(measurements.mean()))"
    ]
   },
   {
@@ -509,9 +489,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvwAAAEtCAYAAAB00rSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAAERCAYAAAAwgetTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c90c50>"
+       "<matplotlib.figure.Figure at 0x4faa240>"
       ]
      },
      "metadata": {},
@@ -519,17 +499,14 @@
     }
    ],
    "source": [
-    "plt.errorbar([1, 2, 3], [170, 161, 169], \n",
-    "             xerr=0, yerr=10, fmt='o', capthick=2, capsize=10)\n",
+    "# a lot of the plotting code is not particularly useful to read, \n",
+    "# so for each chapter I have placed the uninteresting code in a \n",
+    "# file named xxx_internal. I import this  file and call whatever \n",
+    "# function I need. If you want to read the code, the file exists\n",
+    "# in the code subdirectory\n",
     "\n",
-    "plt.plot([1, 3], [180, 160], c='g', ls='--')\n",
-    "plt.plot([1, 3], [170, 170], c='g', ls='--')\n",
-    "plt.plot([1, 3], [160, 175], c='g', ls='--')\n",
-    "plt.plot([1, 2, 3], [180, 152, 179], c='g', ls='--')\n",
-    "plt.xlim(0,4); plt.ylim(150, 185)\n",
-    "plt.xlabel('day')\n",
-    "plt.ylabel('lbs')\n",
-    "plt.show()"
+    "import gh_internal\n",
+    "gh_internal.plot_hypothesis()"
    ]
   },
   {
@@ -550,9 +527,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74f8630>"
+       "<matplotlib.figure.Figure at 0x4faa3c8>"
       ]
      },
      "metadata": {},
@@ -560,13 +537,7 @@
     }
    ],
    "source": [
-    "plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170], \n",
-    "             xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)\n",
-    "plt.plot([1, 10], [169, 170.5], c='g', ls='--')\n",
-    "plt.xlim(0, 11); plt.ylim(150, 185)\n",
-    "plt.xlabel('day')\n",
-    "plt.ylabel('lbs')\n",
-    "plt.show()"
+    "gh_internal.plot_hypothesis2()"
    ]
   },
   {
@@ -587,7 +558,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtU1NX+//HXDBelNC/clFBRQzFKE9FMvJAaQpqXU0lZ\nAuoySzNNyzTtUN9lWtb3JGSeY3Y0A/uip1/Ho9ZRD3nBW4UEeSU1jdQT3lL8SmrCzO8Pv85p5OKQ\nwMxnfD7WYi3n89nzmfdnbZUXm/3Z22S1Wq0CAAAAYChmZxcAAAAAoOoI8gAAAIABEeQBAAAAAyLI\nAwAAAAZEkAcAAAAMiCAPAAAAGBBBHgAAADAgpwX5rKwsDRw4UMHBwTKbzVqyZInd+XPnzmns2LFq\n1qyZbrnlFoWFhWnu3Ll2baKjo2U2m+2+hg0bVpu3AQAAADiFp7M+uLi4WO3bt1diYqISEhJkMpns\nzk+cOFGbNm1Senq6WrZsqU2bNmn06NHy8/PTk08+KUkymUwaOXKkZs2aZXufj49Prd4HAAAA4AxO\nG5GPi4vTzJkz9fDDD8tsLltGdna2EhIS1KtXLzVv3lzDhw9X165d9fXXX9u18/HxUUBAgO2rfv36\ntXULAAAAgNO47Bz5uLg4rVy5UkePHpUkbdu2TXl5eYqNjbVrl5GRIX9/f91111168cUXdf78eWeU\nCwAAANQqp02tuZ4333xTCQkJat68uTw9r5Q5b948Pfjgg7Y2w4YNU0hIiIKCgrR7925NmzZNO3fu\n1Nq1a51VNgAAAFArXDbIv/DCC/rqq6+0atUqtWjRQps2bdLkyZPVokUL9evXT5I0evRoW/vw8HC1\nbt1aXbp0UW5urjp27Gg7V1RUVOv1AwAAANWlQYMGZY65ZJAvLi5WSkqK/v73v6t///6SpLvuukt5\neXl6++23bUH+WhEREfLw8NDBgwftgjwAAADgblxyjrzVapXVai3zEKzZbJbVaq3wfbt27VJpaama\nNm1a0yUCAAAATuXU5ScPHDggSbJYLCooKFBeXp58fX3VrFkz9enTR1OnTlW9evXUvHlzbdq0SWlp\naXrrrbckSYcOHVJ6err69+8vX19f7d27V5MnT1ZERISioqIq/Nzyfi0B17Bjxw5JUmRkpJMrQWXo\nJ2Ogn4yBfjIG+skY3LGfrjc93Gkj8tnZ2YqIiFBERIQuXryo5ORkRUREKDk5WZK0dOlS3XvvvXry\nyScVHh6uOXPmaObMmRo3bpwkydvbW+vXr1e/fv0UFhamCRMmKDY2VpmZmWXWpAcAAADcjdNG5KOj\no2WxWCo87+/vrw8++KDC88HBwdq4cWMNVAYAAAC4PpecIw8AAACgcgR5AAAAwIAI8gAAAIABEeQB\nAAAAAyLIAwAAAAZEkAcAAAAMiCAPAAAAGBBBHgAAADAggjwAAABgQAR5AAAAwIAI8gAAAIABEeQB\nAAAAAyLIAwAAAAZEkAcAAAAMiCAPAAAAGBBBHgAAADAggjwAAABgQAR5AAAAwIAI8gAAAIABOS3I\nZ2VlaeDAgQoODpbZbNaSJUvszp87d05jx45Vs2bNdMsttygsLExz5861a3Pp0iWNHz9e/v7+qlev\nngYNGqRjx47V5m0AAAAATuG0IF9cXKz27dsrJSVFPj4+MplMducnTpyotWvXKj09Xfn5+Zo+fbqm\nTp2q9PR0uzaffvqpMjIytHnzZp07d04DBgyQxWKp7dsBAAAAapXTgnxcXJxmzpyphx9+WGZz2TKy\ns7OVkJCgXr16qXnz5ho+fLi6du2qr7/+WpJUVFSkRYsW6e2331afPn3UsWNHpaWlaefOncrMzKzt\n2wEAAABqlcvOkY+Li9PKlSt19OhRSdK2bduUl5en2NhYSVJOTo4uX76smJgY23uCg4PVrl07bdu2\nzSk1AwAAALXF09kFVOTNN99UQkKCmjdvLk/PK2XOmzdPDz74oCSpsLBQHh4e8vX1tXtfYGCgjh8/\nXuv1AgAAALXJZYP8Cy+8oK+++kqrVq1SixYttGnTJk2ePFktWrRQv379fvd1d+zYUY1VoibQR8ZA\nPxkD/WQM9JMx0E/G4E79FBoaWul5lwzyxcXFSklJ0d///nf1799fknTXXXcpLy9Pb7/9tvr166cm\nTZqotLRUp0+fthuVLywsVM+ePZ1VOgAAAFArXDLIW61WWa3WMg/Bms1mWa1WSVKnTp3k5eWldevW\n6fHHH5ckHT16VPn5+erWrVuF146MjKy5wnFDrv4ETR+5NvrJGOgnY6CfjIF+MgZ37KeioqJKzzst\nyBcXF+vAgQOSJIvFooKCAuXl5cnX11fNmjVTnz59NHXqVNWrV0/NmzfXpk2blJaWprfeekuS1KBB\nA40aNUpTpkxRQECAGjdurEmTJqlDhw7q27evs24LAAAAqBVOW7UmOztbERERioiI0MWLF5WcnKyI\niAglJydLkpYuXap7771XTz75pMLDwzVnzhzNnDlT48aNs11j7ty5GjJkiOLj49W9e3fddtttWrVq\nVZk16QEAAAB347QR+ejo6Eo3bvL399cHH3xQ6TW8vb2Vmpqq1NTU6i4PAAAAcGkuu448AAAAgIoR\n5AEAAAADcslVawAAAICrnksZ7HDbj7Zev03qhBU3UI3rYEQeAAAAMCBG5AEAAODSrjeCfu2IvbuM\nuF8PI/IAAACAARHkAQAAAAMiyAMAAAAGRJAHAAAADIggDwAAABgQq9YAAAC4kaqsue6Im2UFGCNi\nRB4AAAAwIEbkAQDATcsddwxlzfWbByPyAAAAgAExIg8AAG5ajF7DyBiRBwAAAAyIIA8AAAAYEEEe\nAAAAMCCCPAAAAGBATgvyWVlZGjhwoIKDg2U2m7VkyRK782azudyvZ5991tYmOjq6zPlhw4bV9q0A\nAAAAtc5pq9YUFxerffv2SkxMVEJCgkwmk935wsJCu9fZ2dl66KGHFB8fbztmMpk0cuRIzZo1y3bM\nx8enZgsHAFQ7dqIEgKpzWpCPi4tTXFycJCkpKanM+YCAALvXK1asUNu2bdWjRw+74z4+PmXaAgAA\nAO7OEOvInz9/XhkZGXrttdfKnMvIyFBGRoYCAwMVFxen5ORk1atXzwlVAgB+L9byBoCqM0SQ//jj\nj3X58mUlJibaHR82bJhCQkIUFBSk3bt3a9q0adq5c6fWrl3rpEoBAACA2mGIIL9w4UINHjxYvr6+\ndsdHjx5t+3N4eLhat26tLl26KDc3Vx07diz3Wjt27KjRWnHj6CNjoJ+MwV37yd3uy93ux525Y19x\nT64rNDS00vMuv/xkXl6ecnJy7EJ7RSIiIuTh4aGDBw/WQmUAAACA87j8iPz777+vVq1aqU+fPtdt\nu2vXLpWWlqpp06YVtomMjKzO8lCNrv70TB+5NvrJGNytnz7aav/aXe7L3frJHbnj3z3uyTiKiooq\nPe/U5ScPHDggSbJYLCooKFBeXp58fX3VrFkzSdIvv/yipUuXaurUqWXef+jQIaWnp6t///7y9fXV\n3r17NXnyZEVERCgqKqpW7wUAAACobU6bWpOdna2IiAhFRETo4sWLSk5OVkREhJKTk21tli1bpgsX\nLmjEiBFl3u/t7a3169erX79+CgsL04QJExQbG6vMzMwya9IDAAAA7sZpI/LR0dGyWCyVthkxYkS5\nIV6SgoODtXHjxhqoDAAAAHB9Lv+wKwAAAICyCPIAAACAARHkAQAAAANy+eUnAQAwoudSBjvc9tql\n88qTOmHFDVQDwB0xIg8AAAAYECPyAADUgOuNoF87Ys+IO4CqIsgDAADA0I59f0o/5p+U2WySxWLV\nuvA1iukb6+yyapzDQf7UqVPaunWr9u3bp1OnTslkMsnPz0/t2rVTt27d5OfnV5N1AgAAAGWsy1yj\nH/NP6L7+d9qOvbNgtiS5fZivNMhfunRJS5cu1eLFi7V1a+VP4nTr1k0jRozQk08+qTp16lRrkQAA\nAEB50pYttgvxkhTavZGWLv/w5g3yf/7zn/X666/r1KlTiomJ0dy5c9WpUye1atVKjRo1ktVq1Zkz\nZ3T48GHl5OToX//6l8aNG6fk5GTNmDFDTz/9dG3eBwAAqGFVWYnHETwXgOpgsZaUe7zEcrmWK6l9\nFQb5119/XZMnT9bIkSPVoEGDctv4+PgoKChIUVFReu6553T27FktWrRIr7/+OkEeAAAANc5s8pRU\nWua4p9mr9oupZRUG+UOHDsnb27tKF2vYsKEmTZqkZ5999oYLAwAAroWVeOCKhseP0PQ5z9lNr9m/\n+WdNevplJ1ZVOyoM8lUN8dX1XgAAAMBRMX1jtWBlgL78fJ9MJpOsVqtmvpji9vPjpSqsWlNYWKif\nfvpJHTt2tB3bt2+f3nnnHRUVFSk+Pl5/+MMfaqRIAAAAoCK3t/bT7a3/s4LizRDipSoE+WeffVYn\nTpxQVlaWJOnnn39Wr169dPbsWdWtW1effPKJVqxYoYceeqjGigUAAMCNuVnXXHdHDgf57du3a+zY\nsbbX6enpOnPmjL755huFhYWpT58+evvttwnyAADAbbhb6L2Z11x3R2ZHG54+fVpBQUG216tWrVKP\nHj109913y8vLS/Hx8dq9e3eNFAkAAFDb/hN62+neuDDd17+d3lkwW+sy1zi7tN+tsjXXYTwOB/nG\njRvrp59+kiT98ssv2rp1q2JiYmznTSaTLl68WP0VAgAAOIE7ht6bec11d+Tw1Jru3btr/vz5CgsL\n05o1a3Tx4kUNHDjQdn7//v26/fbba6RIAACA2uaOofdmXnPdHTk8Ij9r1izVqVNHjzzyiD744ANN\nmjRJd9555afUkpIS/e1vf1OvXr0c/uCsrCwNHDhQwcHBMpvNWrJkiX1hZnO5X79doLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74f8668>"
+       "<matplotlib.figure.Figure at 0x77dee10>"
       ]
      },
      "metadata": {},
@@ -595,15 +566,7 @@
     }
    ],
    "source": [
-    "weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,\n",
-    "           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]\n",
-    "plt.errorbar(range(1, 13), weights,\n",
-    "             xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)\n",
-    "\n",
-    "plt.xlim(0, 13); plt.ylim(145, 185)\n",
-    "plt.xlabel('day')\n",
-    "plt.ylabel('weight (lbs)')\n",
-    "plt.show()"
+    "gh_internal.plot_hypothesis3()"
    ]
   },
   {
@@ -624,9 +587,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAEtCAYAAABqG4QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA48AAAEtCAYAAACoIlXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e2c668>"
+       "<matplotlib.figure.Figure at 0x786f128>"
       ]
      },
      "metadata": {},
@@ -634,16 +597,7 @@
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "ave = np.sum(weights) / len(weights)\n",
-    "plt.errorbar(range(1,13), weights, label='weights',\n",
-    "             yerr=6, fmt='o', capthick=2, capsize=10)\n",
-    "plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')\n",
-    "plt.xlim(0, 13); plt.ylim(145, 185)\n",
-    "plt.xlabel('day')\n",
-    "plt.ylabel('weight (lbs)')\n",
-    "plt.legend(loc='best')\n",
-    "plt.show()"
+    "gh_internal.plot_hypothesis4()"
    ]
   },
   {
@@ -659,14 +613,15 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA48AAAEtCAYAAACoIlXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7eb1d68>"
+       "<matplotlib.figure.Figure at 0x77deb70>"
       ]
      },
      "metadata": {},
@@ -674,16 +629,7 @@
     }
    ],
    "source": [
-    "xs = range(1, len(weights)+1)\n",
-    "line = np.poly1d(np.polyfit(xs, weights, 1))\n",
-    "plt.errorbar(range(1, 13), weights, label='weights',\n",
-    "             yerr=5, fmt='o', capthick=2, capsize=10)\n",
-    "plt.plot (xs, line(xs), c='r', label='hypothesis')\n",
-    "plt.xlim(0, 13); plt.ylim(145, 185)\n",
-    "plt.xlabel('day')\n",
-    "plt.ylabel('weight (lbs)')\n",
-    "plt.legend(loc='best')\n",
-    "plt.show()"
+    "gh_internal.plot_hypothesis5()"
    ]
   },
   {
@@ -710,7 +656,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAAEtCAYAAABDOSyFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7dc6da0>"
+       "<matplotlib.figure.Figure at 0x6e32710>"
       ]
      },
      "metadata": {},
@@ -718,12 +664,6 @@
     }
    ],
    "source": [
-    "# a lot of the plotting code is not particularly useful to read, \n",
-    "# so for each chapter I have placed the uninteresting code in a \n",
-    "# file named xxx_internal. I import this  file and call whatever \n",
-    "# function I need.\n",
-    "\n",
-    "import gh_internal\n",
     "gh_internal.plot_estimate_chart_1()"
    ]
   },
@@ -745,7 +685,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x748a400>"
+       "<matplotlib.figure.Figure at 0x7812a58>"
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      },
      "metadata": {},
@@ -787,7 +727,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7493b38>"
+       "<matplotlib.figure.Figure at 0x7682a90>"
       ]
      },
      "metadata": {},
@@ -843,9 +783,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7490c88>"
+       "<matplotlib.figure.Figure at 0x7739cc0>"
       ]
      },
      "metadata": {},
@@ -853,7 +793,7 @@
     }
    ],
    "source": [
-    "import book_plots as bp\n",
+    "import book_plots\n",
     "\n",
     "weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6, \n",
     "           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]\n",
@@ -884,11 +824,11 @@
     "    # plot results\n",
     "    n = len(weights)\n",
     " \n",
-    "    bp.plot_filter(range(n+1), estimates)\n",
-    "    bp.plot_measurements(range(1, n+1), weights, c='b', label='Scale')\n",
-    "    bp.plot_track([1, n],[160, 160+n], c='k', label='Actual Weight')\n",
-    "    bp.plot_track(range(1, n+1), predictions, c='r', label='Predictions')\n",
-    "    plt.legend(loc=2)\n",
+    "    book_plots.plot_filter(range(n+1), estimates)\n",
+    "    book_plots.plot_measurements(range(1, n+1), weights, c='b', label='Scale')\n",
+    "    book_plots.plot_track([1, n],[160, 160+n], c='k', label='Actual Weight')\n",
+    "    book_plots.plot_track(range(1, n+1), predictions, c='r', label='Predictions')\n",
+    "    book_plots.show_legend()\n",
     "    plt.xlabel('day')\n",
     "    plt.ylabel('weight (lbs)')\n",
     "    plt.xlim([0, n])\n",
@@ -936,9 +876,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7bf22e8>"
+       "<matplotlib.figure.Figure at 0x7b26b38>"
       ]
      },
      "metadata": {},
@@ -978,9 +918,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d9f438>"
+       "<matplotlib.figure.Figure at 0x7760fd0>"
       ]
      },
      "metadata": {},
@@ -1016,11 +956,11 @@
     "plt.xlim([1, n])\n",
     "   \n",
     "days = np.linspace(1, n, n)\n",
-    "bp.plot_filter(estimates)\n",
-    "bp.plot_measurements(days, weights, c='b', label='Scale')\n",
-    "bp.plot_track([1, n], [160, 160+n], c='k', label='Actual weight')\n",
-    "bp.plot_track(range(1, n+1), predictions, c='r', label='Predictions')\n",
-    "plt.legend(loc=2)\n",
+    "book_plots.plot_filter(estimates)\n",
+    "book_plots.plot_measurements(days, weights, c='b', label='Scale')\n",
+    "book_plots.plot_track([1, n], [160, 160+n], c='k', label='Actual weight')\n",
+    "book_plots.plot_track(range(1, n+1), predictions, c='r', label='Predictions')\n",
+    "book_plots.show_legend()\n",
     "plt.xlabel('day')\n",
     "plt.ylabel('weight (lbs)')\n",
     "plt.show()"
@@ -1080,7 +1020,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7bbf7f0>"
+       "<matplotlib.figure.Figure at 0x6edf898>"
       ]
      },
      "metadata": {},
@@ -1172,71 +1112,39 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d2b208>"
+       "<matplotlib.figure.Figure at 0x7667b00>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12\n"
-     ]
     }
    ],
    "source": [
-    "def g_h_filter(data, x0, dx, g, h, dt=1., pred=None):\n",
-    "    \"\"\"\n",
-    "    Performs g-h filter on 1 state variable with a fixed g and h.\n",
-    "    \n",
-    "    'data' contains the data to be filtered.\n",
-    "    'x0' is the initial value for our state variable\n",
-    "    'dx' is the initial change rate for our state variable\n",
-    "    'g' is the g-h's g scale factor\n",
-    "    'h' is the g-h's h scale factor\n",
-    "    'dt' is the length of the time step \n",
-    "    'pred' is an optional list. If provided, each prediction will\n",
-    "    be stored in it\n",
-    "    \"\"\"\n",
-    "    \n",
+    "from gh_internal import plot_g_h_results\n",
+    "\n",
+    "def g_h_filter(data, x0, dx, g, h, dt=1., pred=None):    \n",
     "    x = x0\n",
     "    results = []\n",
     "    for z in data:\n",
     "        #prediction step\n",
     "        x_est = x + (dx*dt)\n",
-    "        dx = dx\n",
-    "        \n",
+    "        dx = dx        \n",
     "        if pred is not None:\n",
     "            pred.append(x_est)\n",
     "        \n",
     "        # update step\n",
     "        residual = z - x_est\n",
     "        dx = dx    + h * (residual) / dt\n",
-    "        x  = x_est + g * residual\n",
-    "        \n",
-    "        results.append(x)\n",
-    "        \n",
+    "        x  = x_est + g * residual     \n",
+    "        results.append(x)  \n",
+    "\n",
     "    return np.asarray(results)\n",
     "\n",
-    "def plot_g_h_results(measurements, filtered_data, \n",
-    "                     title='', z_label='Scale', ):    \n",
-    "    bp.plot_measurements(measurements, label=z_label)\n",
-    "    bp.plot_filter(filtered_data)\n",
-    "    plt.legend(loc=4)\n",
-    "    plt.title(title)\n",
-    "    plt.gca().set_xlim(left=0,right=len(measurements))\n",
-    "    plt.show()\n",
-    "\n",
-    "\n",
-    "plt.xlim([0, 10])\n",
-    "bp.plot_track([0, 11],[160, 172], label='Actual weight')\n",
+    "book_plots.plot_track([0, 11], [160, 172], label='Actual weight')\n",
     "data = g_h_filter(data=weights, x0=160, dx=1, g=6./10, h=2./3, dt=1.)\n",
-    "plot_g_h_results(weights, data)\n",
-    "print(len(weights))"
+    "plot_g_h_results(weights, data)"
    ]
   },
   {
@@ -1298,9 +1206,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d29550>"
+       "<matplotlib.figure.Figure at 0x76a9ba8>"
       ]
      },
      "metadata": {},
@@ -1358,9 +1266,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7dba2b0>"
+       "<matplotlib.figure.Figure at 0x776e9b0>"
       ]
      },
      "metadata": {},
@@ -1421,9 +1329,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7dbafd0>"
+       "<matplotlib.figure.Figure at 0x787fb00>"
       ]
      },
      "metadata": {},
@@ -1486,9 +1394,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8edbd30>"
+       "<matplotlib.figure.Figure at 0x8e46400>"
       ]
      },
      "metadata": {},
@@ -1563,9 +1471,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90ba8d0>"
+       "<matplotlib.figure.Figure at 0x94de0b8>"
       ]
      },
      "metadata": {},
@@ -1573,9 +1481,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90ba7f0>"
+       "<matplotlib.figure.Figure at 0x8e46668>"
       ]
      },
      "metadata": {},
@@ -1583,9 +1491,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x96dbdd8>"
+       "<matplotlib.figure.Figure at 0x77ef1d0>"
       ]
      },
      "metadata": {},
@@ -1594,15 +1502,13 @@
    ],
    "source": [
     "zs = gen_data(x0=5, dx=5, count=100, noise_factor=50)\n",
+    "data1 = g_h_filter(data=zs, x0=0., dx=5., dt=1., g=0.1, h=0.01)\n",
+    "data2 = g_h_filter(data=zs, x0=0., dx=5., dt=1., g=0.5, h=0.01)\n",
+    "data3 = g_h_filter(data=zs, x0=0., dx=5., dt=1., g=0.9, h=0.01)\n",
     "\n",
-    "data = g_h_filter(data=zs, x0=0., dx=5., dt=1., g=0.1, h=0.01)\n",
-    "plot_g_h_results (zs, data, 'g = 0.1')\n",
-    "\n",
-    "data = g_h_filter(data=zs, x0=0., dx=5., dt=1., g=0.5, h=0.01)\n",
-    "plot_g_h_results (zs, data, 'g = 0.5')\n",
-    "\n",
-    "data = g_h_filter(data=zs, x0=0., dx=5., dt=1., g=0.9, h=0.01)\n",
-    "plot_g_h_results(zs, data, 'g = 0.9')"
+    "plot_g_h_results(zs, data1, 'g = 0.1')\n",
+    "plot_g_h_results(zs, data2, 'g = 0.5')\n",
+    "plot_g_h_results(zs, data3, 'g = 0.9')"
    ]
   },
   {
@@ -1621,9 +1527,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90a5e80>"
+       "<matplotlib.figure.Figure at 0x7779b00>"
       ]
      },
      "metadata": {},
@@ -1631,9 +1537,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90a5b70>"
+       "<matplotlib.figure.Figure at 0x7779dd8>"
       ]
      },
      "metadata": {},
@@ -1641,9 +1547,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e13208>"
+       "<matplotlib.figure.Figure at 0x75f8550>"
       ]
      },
      "metadata": {},
@@ -1652,18 +1558,16 @@
    ],
    "source": [
     "zs = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n",
-    "\n",
     "for i in range(100):\n",
     "    zs.append(14)\n",
     "\n",
-    "data = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=0.1, h=0.01)\n",
-    "plot_g_h_results(zs, data, 'g = 0.1')\n",
+    "data1 = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=0.1, h=0.01)\n",
+    "data2 = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=0.5, h=0.01)\n",
+    "data3 = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=0.9, h=0.01)\n",
     "\n",
-    "data = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=0.5, h=0.01)\n",
-    "plot_g_h_results(zs, data, 'g = 0.5')\n",
-    "\n",
-    "data = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=0.9, h=0.01)\n",
-    "plot_g_h_results(zs, data, 'g = 0.9')"
+    "plot_g_h_results(zs, data1, 'g = 0.1')\n",
+    "plot_g_h_results(zs, data2, 'g = 0.5')\n",
+    "plot_g_h_results(zs, data3, 'g = 0.9')"
    ]
   },
   {
@@ -1684,9 +1588,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x96f0358>"
+       "<matplotlib.figure.Figure at 0x75f8b70>"
       ]
      },
      "metadata": {},
@@ -1694,9 +1598,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAEjCAYAAAAfTOaLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0IAAAEjCAYAAAAfabGiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7dfb080>"
+       "<matplotlib.figure.Figure at 0x7779d68>"
       ]
      },
      "metadata": {},
@@ -1706,11 +1610,11 @@
    "source": [
     "zs = [5,6,7,8,9,9,9,9,9,10,11,12,13,14,15,16,16,16,16,16,16,16,16,16,16,16]\n",
     "    \n",
-    "data = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=.302, h=0.054)\n",
-    "plot_g_h_results(zs, data, 'g = 0.302, h = 0.054')\n",
+    "data1 = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=.302, h=0.054)\n",
+    "data2 = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=.546, h=0.205)\n",
     "\n",
-    "data = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=.546, h=0.205)\n",
-    "plot_g_h_results(zs, data, 'g = 0.546, h = 0.205')"
+    "plot_g_h_results(zs, data1, 'g = 0.302, h = 0.054')\n",
+    "plot_g_h_results(zs, data2, 'g = 0.546, h = 0.205')"
    ]
   },
   {
@@ -1738,9 +1642,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ce55f8>"
+       "<matplotlib.figure.Figure at 0x7b7eb70>"
       ]
      },
      "metadata": {},
@@ -1748,9 +1652,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ce5f98>"
+       "<matplotlib.figure.Figure at 0x7b7ec18>"
       ]
      },
      "metadata": {},
@@ -1758,9 +1662,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e60470>"
+       "<matplotlib.figure.Figure at 0x77103c8>"
       ]
      },
      "metadata": {},
@@ -1770,16 +1674,15 @@
    "source": [
     "zs = np.linspace(0, 1, 50)\n",
     "\n",
-    "data = g_h_filter(data=zs, x0=0, dx=0., dt=1., g=.2, h=0.05)\n",
-    "plot_g_h_results(zs, data, 'dx=0, h = 0.05')\n",
+    "data1 = g_h_filter(data=zs, x0=0, dx=0., dt=1., g=.2, h=0.05)\n",
+    "data2 = g_h_filter(data=zs, x0=0, dx=2., dt=1., g=.2, h=0.05)\n",
+    "data3 = g_h_filter(data=zs, x0=0, dx=2., dt=1., g=.2, h=0.5)\n",
     "\n",
-    "data = g_h_filter(data=zs, x0=0, dx=2., dt=1., g=.2, h=0.05)\n",
+    "plot_g_h_results(zs, data1, 'dx=0, h = 0.05')\n",
     "plt.ylim([-1, 5])\n",
-    "plot_g_h_results(zs, data, 'dx=2, h = 0.05')\n",
-    "\n",
-    "data = g_h_filter(data=zs, x0=0, dx=2., dt=1., g=.2, h=0.5)\n",
+    "plot_g_h_results(zs, data2, 'dx=2, h = 0.05')\n",
     "plt.ylim([-1, 5])\n",
-    "plot_g_h_results(zs, data, 'dx=2, h = 0.5')"
+    "plot_g_h_results(zs, data3, 'dx=2, h = 0.5')"
    ]
   },
   {
@@ -1806,9 +1709,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7beee80>"
+       "<matplotlib.figure.Figure at 0x8b6b8d0>"
       ]
      },
      "metadata": {},
@@ -1883,9 +1786,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8f109b0>"
+       "<matplotlib.figure.Figure at 0x7845e80>"
       ]
      },
      "metadata": {},
@@ -1899,21 +1802,17 @@
     "    \"\"\" dt is the time delta in seconds.\"\"\"\n",
     "    return pos + (vel * dt)\n",
     "\n",
-    "\n",
     "def measure_position(pos):\n",
     "    return pos + randn()*500\n",
     "\n",
-    "\n",
     "def gen_train_data(pos, vel, count):\n",
     "    zs = []\n",
     "    for t in range(count):\n",
     "        pos = compute_new_position(pos, vel)\n",
     "        zs.append(measure_position(pos))\n",
     "    return np.asarray(zs)\n",
-    "\n",
-    "    \n",
-    "pos = 23*1000\n",
-    "vel = 15\n",
+    "  \n",
+    "pos, vel = 23*1000, 15\n",
     "zs = gen_train_data(pos, vel, 100)\n",
     "\n",
     "plt.plot(zs/1000) # convert to km\n",
@@ -1940,9 +1839,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8f1ca20>"
+       "<matplotlib.figure.Figure at 0x9504f28>"
       ]
      },
      "metadata": {},
@@ -1971,9 +1870,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e42978>"
+       "<matplotlib.figure.Figure at 0x781c518>"
       ]
      },
      "metadata": {},
@@ -2004,9 +1903,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ddaa90>"
+       "<matplotlib.figure.Figure at 0x776ea90>"
       ]
      },
      "metadata": {},
@@ -2054,9 +1953,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c8ce48>"
+       "<matplotlib.figure.Figure at 0x6eb71d0>"
       ]
      },
      "metadata": {},
@@ -2085,9 +1984,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c13780>"
+       "<matplotlib.figure.Figure at 0x7645128>"
       ]
      },
      "metadata": {},

02_Discrete_Bayes.ipynb

@@ -109,7 +109,7 @@
        "        color: #1d3b84;\n",
        "        font-size: 16pt;\n",
        "        margin-bottom: 0em;\n",
-       "        margin-top: 1.5em;\n",
+       "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "        white-space: nowrap;\n",
        "    }\n",
@@ -133,6 +133,11 @@
        "        overflow-y: scroll;\n",
        "        max-height: 50000px;\n",
        "    }\n",
+       "    div.output_wrapper{\n",
+       "        margin-top:0.2em;\n",
+       "        margin-bottom:0.2em;\n",
+       "}\n",
+       "\n",
        "    code{\n",
        "      font-size: 70%;\n",
        "    }\n",
@@ -344,9 +349,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6759748>"
+       "<matplotlib.figure.Figure at 0x78a95c0>"
       ]
      },
      "metadata": {},
@@ -356,11 +361,9 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import book_plots as bp\n",
-    "import numpy as np\n",
     "\n",
     "pos_belief = np.array([1./3, 1./3, 0, 0, 0, 0, 0, 0, 1./3, 0])\n",
-    "\n",
-    "set_figsize(y=3)\n",
+    "set_figsize(y=2)\n",
     "bp.bar_plot(pos_belief)"
    ]
   },
@@ -484,9 +487,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x67bbb38>"
+       "<matplotlib.figure.Figure at 0x792ceb8>"
       ]
      },
      "metadata": {},
@@ -519,22 +522,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'np' 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-1-6ec9c7c03a6e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mnormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbelief\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mpos_belief\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;36m0.2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhallway\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpos_belief\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprob_correct\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m.75\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sum = 1.0\n",
+      "probability of door = 0.1875\n",
+      "probability of wall = 0.0625\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x805d9e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -604,9 +615,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACrCAYAAACt8bDOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAH2ZJREFUeJzt3X9UlGX+//HXDGqCIv4gFBUBNyPb8AeQBRmia9OSm3ky\nOWWrSZYrW6ZRrdHxk5ZmZruYukq/DKzMcPthZ5VcKUEjrWSJcs1+iUu2MmyQoVmCMff3Dw/zbWKA\nEW93ZvT5OMdz5Lqv677f93Affc19rvu6LYZhGAIAAABw2qzeLgAAAAA4WxCuAQAAAJMQrgEAAACT\nEK4BAAAAkxCuAQAAAJMQrgEAAACTEK4BnBNWrlypX//61woKCpLVatVDDz3k7ZL8TkpKiqxW1/82\niouLz/jnGRUVpejo6DO2fwAwE+EagN9avny5rFarrFarPvjggxb7vfzyy5o9e7YaGxs1e/ZsLViw\nQCkpKVqwYIGsVqvWrl37P6zav1ksllNq90RUVFSz0P7LfZ/O/gHgf6mDtwsAgPZ6+umnXf4+YsQI\nt/02bdokSXr++edd+hQVFUk6vWB4rrvsssv06aefKjQ09LT209rvYNu2bae1bwD4X+LONQC/9M47\n72jfvn268cYb1b9/f+Xn5+vo0aNu+x46dEiS1Lt3b7fbeVFt+wUGBurCCy9Uz549z9gxoqOjmRYC\nwG8QrgH4paa71rfddpumTp2qY8eOad26dS59mqZ9FBcXSzoZ0pqmkYwePVoPP/ywJCk9Pd3ZbrVa\n9dVXXzn34XA49Oyzz+qKK65QSEiIAgMDFRsbqyVLlujEiRPN6rJarYqOjtaRI0c0Z84cRUZGqmPH\njlq+fHmr59M0dzk9PV2ffPKJxo8fr549e6pr165KTk7W22+/3WxMXl6ec77ze++9p9TUVPXo0UNW\nq1VHjhxpV/3SyWk08fHxCgoKUu/evTV16lTnF5SW6nY35/rw4cP6v//7P8XGxqpr164KCQlRbGys\n7rvvPn333Xf697//7fy8DcNw+R2MHj3auZ+W5lw3NDTo8ccf17Bhw9SlSxd169ZNiYmJys3NdVtr\n0+/mhx9+0H333acBAwaoc+fOGjRokJYuXep2DACcKqaFAPA73377rV555RVFRkZqzJgxioyM1OLF\ni/XMM89o5syZzn6jR4+WxWJRXl6eKisrNWfOHHXv3l2SFBkZKUnavn27JkyYoGHDhjnHhYSESJJ+\n+uknXX/99dq0aZNiYmJ08803q3PnziouLtYDDzygt99+W1u2bFFAQIBLffX19Ro9erSOHDmicePG\nKSgoSBERER6d24EDB3TFFVdo2LBhysjI0Ndff60NGzbo6quv1oYNG3T99dc3G7Nz504tXrxYKSkp\nmjFjhqqqqhQQENCu+pctW6Z77rlH3bt319SpU9WjRw/94x//cIbzlvxyWseBAwc0evRoffXVV4qL\ni3P+Xj7//HOtXr1aU6ZMUWRkpObPn68nnnhCdXV1WrBggXN8VFRUq/s/ceKEUlNTVVRUpJiYGP3x\nj39UfX29XnvtNU2fPl0lJSVas2ZNszpPnDghm82mqqoqjRs3Th06dNDrr7+u+++/X8ePH9eDDz7Y\n4jkCgEcMAPAzy5YtMywWizF//nxnW3JysmGxWIzdu3c36z9q1CjDYrEYlZWVLu3z5883LBaLsXbt\nWrfHWbhwoWGxWIxZs2YZDofD2e5wOIwZM2YYFovFWLlypcsYi8ViWCwWw2azGT/++KPH51RUVOQc\n+6c//cll2/vvv2906NDB6NWrl/H9998723Nzc51jnnnmmdOu/8CBA0bHjh2NHj16GAcOHHDpn5aW\nZlgsFsNqtbqt+6GHHnJpT0xMNCwWi7Fo0aJmddXV1bmcR2RkZLP9/lxkZKQRHR3t0rZkyRLDYrEY\nv/3tb40TJ0647PuSSy4xLBaL8corr7iMafqsxo0bZxw/ftzZ/t///tfo3r270b17d5d9AUB7EK4B\n+J3BgwcbAQEBLgEwLy/PsFgsxu23396sf3vCdWNjoxEaGmr06dPHaGxsbLb98OHDhtVqNS677DKX\n9qYA+vHHH5/SOTWF1B49ergEzyaTJ082LBaLsW7dOmdbU7iOi4trd/0jRoxwti1atMiwWCzGvHnz\nmvU/cOCAERAQ4FG4Li0tNSwWizF06FCPzr094fqCCy4wrFar8cknnzTr//e//92wWCzG1Vdf7dLe\n9LvZv39/szFTp041LBaLsXfvXo9qBoCWMC0EgF9555139OmnnyolJcVl6sCkSZM0a9Ysvfzyy8rO\nzlbXrl1P6ziff/65amtr9atf/co5N/uXOnfurE8++cRte2xsbLuOGxcXpy5dujRrT05O1vr161Ve\nXq7Jkye7bHO3Soqn9e/bt8/5c1lZmSRp1KhRzfpGRUUpIiLCZT56S9577z1Jks1ma7Nvexw9elT7\n9+9Xnz59NHjw4Gbbx4wZI0n68MMPm20LCQnRwIEDm7U3Tds5fPiwydUCONcQrgH4laeeekqSdMst\nt7i0BwUFadKkScrNzdVLL72kGTNmnNZxamtrJUn79+9vMZxK7peQCwsLa/dxW1rRpKm9rq6u2bY+\nffo0a2tP/U37bq0GT8L1d999J0nq169fm33bo6lOd+ctnbwWunXr5qzj55rm3P9Shw4n/ztsbGw0\nqUoA5ypWCwHgN5oeZJSar/BhtVqdq0T8fP3r9mp6eG/8+PFyOBwt/nEXxk5n3ezq6upW2909VOju\neO2pv2lMWzW0pSnAfv311x71P1VNddrtdrfbf/jhBx05cqTVBzAB4EzhzjUAv7F27Vo1NDRo+PDh\nio+Pd9vnzTffVFlZmcrKyhQXF9fq/ppWyXAXkAcPHqzu3bvr/fff14kTJ9SxY8fTPwEPlJWV6fvv\nv282rWX79u2SpOHDh3u0n/bUHx8fr9dff13FxcUaO3asy7YDBw7o4MGDHh07MTFRkrR161YtXbq0\nzS8bTb8HwzA8+mISHBysCy64QF9++aX27dvXbGpI00tnWrpGAOBM4s41AL/x9NNPy2KxaMWKFXr6\n6afd/rn77rudfX/OXWjr1auXJKmysrLZtoCAAM2ePVvV1dW644479OOPPzbrU1NTo48++siMU3P6\n7rvvmk3jeP/997Vhwwb17NlT1113nUf7aU/9N998szp27KhVq1bpwIEDznaHw6H7779fDofDo2PH\nxcUpKSlJe/bs0SOPPNJse11dnY4dO+b8uVevXjIMw+3voSXTp0+XJN1zzz366aefnO1HjhzRAw88\nIOnkGugA8L/GnWsAfmHHjh367LPPNHjwYF1xxRUt9ps6daqysrK0fv16/eUvf3E+HGi4eQvjb37z\nG1mtVj3xxBOqra11zjW+66671K1bN82bN0979uzRs88+q82bN2vMmDHq37+/vvnmG+3fv1/vvvuu\n7rzzTmVnZ5t2nldeeaWeeeYZffDBB0pKStJ//vMf5efny2Kx6Omnn1ZQUJDH+zrV+iMjI7VkyRLd\nc889iouLU1pamnOd67q6Og0ZMkQff/yxR8d+8cUXlZKSogcffFAbN25USkqKpJNzwLdu3apdu3Zp\nyJAhkk4++FhaWqrrr79eqampCgwMVFRUlH7/+9+3uP/MzExt2bJFW7ZsUWxsrMaNG6cTJ07o1Vdf\n1aFDh3TLLbdo4sSJHn9WAGCatpYT2b59u3Httdca/fr1MywWi5GXl9fmEiQff/yxkZycbAQGBhr9\n+vUzHn744dNf1wTAOW3y5MmG1Wo1li1b1mbftLQ0w2q1Gs8++6xhGIaRkpJiWK3WZkvxGYZhrF+/\n3oiPjzeCgoKcS7X9st/69esNm81m9OrVy+jUqZPRt29fIzEx0ViwYIHx5ZdfuvS1WCzNlo3zRNOS\ndunp6ca+ffuM8ePHGz169DC6dOliJCcnG2+//XazMXl5eYbVam22xrS7c/S0/qb+cXFxRufOnY2w\nsDBjypQpRlVVlfNzdFe3uxpqa2uNrKws46KLLjI6d+5sdO/e3RgyZIgxd+5c47vvvnP2++GHH4xZ\ns2YZAwYMMDp27GhYLBZj9OjRzu1RUVFuP9P6+nrjscceM4YMGWIEBgYaXbt2NS6//HJjzZo1bj+H\n1n43CxYsMKxWq7F9+3b3HyIAeMhiGG5u5/zMm2++qXfffVfDhw/X1KlTlZOTo6lTp7bY/8iRI7rw\nwguddyz27dun9PR0LViwQJmZmaZ/OQCAs0FxcbHGjBmjadOm6bnnnvN2OQCAdmpzWkhqaqpSU1Ml\nSdOmTWtzh+vWrdPx48e1du1anXfeebr44ov16aefKjs7m3ANAACAs5rpDzTu2rVLV155pc477zxn\nm81m06FDh07pYRUAAADA35j+QKPdbteAAQNc2poeErLb7YqMjHS2u3sZAgCci77//ntJUkNDA/82\nAoAf+eWa+qaH69N5eQIAnKuuvPJKXr0NAGcB06eF9OnTp9lbs5re6tXSq2oBAACAs4Hpd64TExM1\nd+5c1dfXO+ddFxYWql+/fi5TQn6J19SeVFpaKklKSEjwciVA27he4U+4XuFPuF59W2vT99q8c33s\n2DGVl5ervLxcDodDlZWVKi8vd74GNysry+U1uZMnT1ZQUJCmTZumvXv36rXXXtNjjz3GSiEAAAA4\n67UZrnfv3q24uDjFxcXp+PHjmj9/vuLi4jR//nxJJx9SrKiocPbv1q2bCgsLdejQISUkJGjWrFm6\n9957na8kBgAAAM5WbU4LSUlJkcPhaHF7bm5us7ZLLrlE27dvP73KAAAAAD9j+gONAAAAwLmKcA0A\nAACYhHANAAAAmIRwDQAAAJiEcA0AAACYhHANAAAAmIRwDQAAAJiEcA0AAACYhHANALine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6f9a048>"
+       "<matplotlib.figure.Figure at 0x80ddba8>"
       ]
      },
      "metadata": {},
@@ -614,7 +625,6 @@
     }
    ],
    "source": [
-    "import numpy\n",
     "def perfect_predict(pos_belief, move):\n",
     "    \"\"\" move the position by 'move' spaces, where positive is to the right, \n",
     "    and negative is to the left\n",
@@ -675,9 +685,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x70a1d68>"
+       "<matplotlib.figure.Figure at 0x81524e0>"
       ]
      },
      "metadata": {},
@@ -689,13 +699,13 @@
     "    n = len(pos_belief)\n",
     "    result = np.zeros(n)\n",
     "    for i in range(n):\n",
-    "                result[i] = (\n",
+    "        result[i] = (\n",
     "            pos_belief[(i-move) % n]   * p_correct +\n",
     "            pos_belief[(i-move-1) % n] * p_over +\n",
     "            pos_belief[(i-move+1) % n] * p_under)      \n",
     "    pos_belief[:] = result\n",
     "\n",
-    "pos_belief = np.array([0, 0, 0, 1, 0, 0, 0, 0], dtype=float)\n",
+    "pos_belief = np.array([0., 0., 0., 1., 0., 0., 0., 0.])\n",
     "predict(pos_belief, 2, .8, .1, .1)\n",
     "print(pos_belief)\n",
     "bp.bar_plot(pos_belief)"
@@ -725,9 +735,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7094d30>"
+       "<matplotlib.figure.Figure at 0x81a9be0>"
       ]
      },
      "metadata": {},
@@ -759,9 +769,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAADaCAYAAABtj26qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGYlJREFUeJzt3X9MVffh//EXoChYyqeKKAhViBajRVSuTK9WzaJk1sa5\nVKlus5W6WYhaf8TEsrFUK2pbNlK1gMYZJO3ccDGxS2UOjFZgsAyKOOvU0bJZF7h0WoeEDIlwv3/4\nLemtCKjv23Pv9flITOB93ufeF+8S+vL45hw/p9PpFAAAAICH5m91AAAAAMBXUK4BAAAAQyjXAAAA\ngCGUawAAAMAQyjUAAABgCOUaAAAAMIRyDQAAABjSZ7kuKyvTokWLFBUVJX9/fxUWFvb5oufPn9ec\nOXMUHBysqKgobd++3UhYAAAAwJP1Wa7b2to0adIk7d69W0FBQfLz8+t1/s2bNzV//nxFRESopqZG\nu3fvVnZ2tnJycoyFBgAAADyR3/08oTEkJES5ubl68cUX7zknPz9fGRkZam5u1qBBgyRJO3bsUH5+\nvv79738/fGIAAADAQxnfc11VVaVnnnmmu1hLUnJyshobG3XlyhXTbwcAAAB4DOPl2uFwaMSIES5j\nX33ucDhMvx0AAADgMQaYfsG+9mR/XUtLi+m3BwAAAL41oaGhLp8bv3I9cuTIu65QNzc3dx8DAAAA\nfJXxcj1jxgyVl5fr1q1b3WOlpaUaNWqURo8ebfrtAAAAAI/R57aQtrY21dfXS5K6urp05coV1dXV\nadiwYYqOjlZGRoaqq6t18uRJSdIPf/hDbdu2TStXrlRmZqYuX76st956S1u3bu31fb55Sf3bVFNT\nI0my2WyWZfBFrKt7sK7uw9q6B+vqHqyre7Cu7uFr69rb1uY+r1xXV1dr6tSpmjp1qtrb2/X6669r\n6tSpev311yXd+SXFhoaG7vmPP/64SktL1djYKJvNpnXr1mnz5s3auHGjgS8FAAAA8Fx9XrmeO3eu\nurq67nm8oKDgrrGnn35aZ86cebhkAAAAgJcxvucaAAAAeFRRrgEAAABDKNcAAACAIZRrAAAAwBDK\nNQAAAGAI5RoAAAAwhHINAAAAGEK5BgAAAAyhXAMAAACGUK4BAAAAQyjXAAAAgCGUawAAAMAQyjUA\nAABgCOUaAAAAMIRyDQAAABhCuQYAAAAMoVwDAAAAhlCuAQAAAEMo1wAAAIAhlGsAAADAEMo1AAAA\nYAjlGgAAADCEcg0AAAAYQrkGAAAADKFcAwAAAIb0q1zn5eUpJiZGQUFBstlsqqio6HV+cXGxpk+f\nrscff1zDhw/X4sWLVV9fbyQwAAAA4Kn6LNdFRUXasGGDMjMzVVdXJ7vdrgULFujq1as9zv/000+1\nePFizZ07V3V1dTp58qTa29v17LPPGg8PAAAAeJI+y3VOTo5SU1O1atUqxcXFac+ePYqIiFB+fn6P\n8+vq6tTV1aVdu3YpNjZWCQkJ2rJliz777DN9+eWXxr8AAAAAwFP0Wq47OjpUW1ur5ORkl/Hk5GRV\nVlb2eM7MmTP12GOP6cCBA+rs7FRra6sOHTqkpKQkDR061FxyAAAAwMP4OZ1O570ONjY2KioqSmVl\nZZo1a1b3+BtvvKHDhw/r0qVLPZ5XWVmpxYsX68aNG+rq6tKUKVP0xz/+UcOHD3eZ19LS0v0xe7IB\nAADgDcaNG9f9cWhoqMsx43cLaWho0OLFi5Wamqqamhp99NFHCgkJUUpKinrp8QAAAIDXG9DbwbCw\nMAUEBKi5udllvLm5WRERET2es3//fkVHR+utt97qHnv//fcVHR2tqqoq2e32Hs+z2Wz3m92Ympoa\nyzP4ItbVPVhX92Ft3YN1dQ/W1T1YV/fwtXX9+u6Lb+r1ynVgYKASExNVUlLiMl5aWnrPkux0OuXv\n7/qyX33e1dXVr8AAAACAN+pzW8imTZt06NAhHTx4UBcvXtT69evlcDiUlpYmScrIyNC8efO65y9a\ntEi1tbXavn276uvrVVtbq9TUVD355JNKTEx031cCAAAAWKzXbSGSlJKSouvXrysrK0tNTU2Kj49X\ncXGxoqOjJUkOh0MNDQ3d82fNmqWioiK9+eabevvttxUcHKwZM2boxIkTCgoKct9XAgAAAFisz3It\nSenp6UpPT+/xWEFBwV1jS5Ys0ZIlSx4uGQAAAOBljN8tBAAAAHhUUa4BAAAAQyjXAAAAgCGUawAA\nAMAQyjUAAABgCOUaAAAAMIRyDQAAABhCuQYAAAAMoVwDAAAAhlCuAQAAAEMo1wAAAIAhlGsAAADA\nEMo1AAAAYAjlGgAAADCEcg0AAAAYQrkGAAAADKFcAwAAAIZQrgEAAABDKNcAAACAIZRrAAAAwBDK\nNQAAAGAI5RoAAAAwhHINAAAAGEK5BgAAAAyhXAMAAACG9Ktc5+XlKSYmRkFBQbLZbKqoqOjznHfe\neUfjx4/X4MGDFRkZqYyMjIcOCwAAAHiyAX1NKCoq0oYNG5Sfn69Zs2YpNzdXCxYs0N///ndFR0f3\neM6mTZt0/Phx/fKXv1R8fLxaWlrU1NRkPDwAAADgSfos1zk5OUpNTdWqVaskSXv27NGJEyeUn5+v\nnTt33jX/8uXLevfdd3X+/HnFxcV1jyckJBiMDQAAAHieXreFdHR0qLa2VsnJyS7jycnJqqys7PGc\nDz74QLGxsSouLlZsbKxiYmK0cuVK/ec//zGXGgAAAPBAfk6n03mvg42NjYqKilJZWZlmzZrVPf7G\nG2/o8OHDunTp0l3npKWlqbCwUJMnT1Z2drYkafPmzZKkqqoq+fn5dc9taWnp/ri+vv7hvxoAAADA\nzcaNG9f9cWhoqMuxPreF3K+uri7dunVL7733nsaOHStJeu+99xQXF6eamhpNmzbN9FsCAAAAHqHX\nch0WFqaAgAA1Nze7jDc3NysiIqLHcyIiIjRgwIDuYi1JY8eOVUBAgD7//PN7lmubzXa/2Y2pqamx\nPIMvYl3dg3V1H9bWPVhX92Bd3YN1dQ9fW9ev7774pl73XAcGBioxMVElJSUu46WlpbLb7T2eM2vW\nLN2+fVsNDQ3dYw0NDers7NTo0aPvJzcAAADgVfq8z/WmTZt06NAhHTx4UBcvXtT69evlcDiUlpYm\nScrIyNC8efO658+bN09Tp07Vyy+/rLq6Op09e1Yvv/yypk+f7jN/WwEAAAB60uee65SUFF2/fl1Z\nWVlqampSfHy8iouLu+9x7XA4XK5S+/n56cMPP9Srr76q2bNnKygoSMnJycrJyXHfVwEAAAB4gH79\nQmN6errS09N7PFZQUHDX2MiRI3XkyJGHSwYAAAB4mX49/hwAAABA3yjXAAAAgCGUawAAAMAQyjUA\nAABgCOUaAAAAMIRyDQAAABhCuQYAAAAMoVwDAAAAhlCuAQAAAEP69YRGAABwR8O1VjW23bY6hloH\nh0uSKq7csDiJFDlkgGLDQqyOAXgEyjUAAPehse221pVdszrG19yyOoD2zg5TbJjVKQDPwLYQAAAA\nwBDKNQAAAGAI5RoAAAAwhHINAAAAGEK5BgAAAAyhXAMAAACGUK4BAAAAQyjXAAAAgCGUawAAAMAQ\nyjUAAABgCOUaAAAAMIRyDQAAABhCuQYAAAAMoVwDAAAAhvSrXOfl5SkmJkZBQUGy2WyqqKjo14vX\n19crJCREISEhDxUSAAAA8AZ9luuioiJt2LBBmZmZqqurk91u14IFC3T16tVez+vo6NCyZcs0Z84c\n+fn5GQsMAAAAeKo+y3VOTo5SU1O1atUqxcXFac+ePYqIiFB+fn6v523ZskWTJ0/W0qVL5XQ6jQUG\nAAAAPFWv5bqjo0O1tbVKTk52GU9OTlZlZeU9zzt+/LiOHz+uvXv3UqwBAADwyBjQ28Fr166ps7NT\nI0aMcBkPDw+Xw+Ho8ZzGxkatXr1ax44dU3BwcL+D1NTU9Huuu3hCBl/EuroH6+o+rK17+Mq6tg4O\ntzqCx2ltbVVNzWdWxzDKV75fPY2vrOu4cePuecz43UJWrFih9PR0TZs2zfRLAwAAAB6t1yvXYWFh\nCggIUHNzs8t4c3OzIiIiejzn9OnTKisr07Zt2yRJTqdTXV1dGjhwoPLz8/WTn/ykx/NsNtuD5Dfi\nq79FWZnBF7Gu7sG6ug9r6x6+tq4VV25IumV1DI8SEhIi29NPWh3DCF/7fvUUvrauLS0t9zzWa7kO\nDAxUYmKiSkpK9Pzzz3ePl5aWaunSpT2e88knn7h8fuzYMe3YsUPV1dWKjIy8n9wAAACAV+m1XEvS\npk2btGLFCiUlJclut2vfvn1yOBxKS0uTJGVkZKi6ulonT56UJE2YMMHl/L/+9a/y9/e/axwAAADw\nNX2W65SUFF2/fl1ZWVlqampSfHy8iouLFR0dLUlyOBxqaGjo9TW4zzUAfPsarrWqse221TG6fwHw\nznYKa0UOGaDYMB5sBsB9+izXkpSenq709PQejxUUFPR67sqVK7Vy5cr7DgYAeDiNbbe1ruya1TG+\nxvp9yntnhyk2zOoUAHyZ8buFAAAAAI8qyjUAAABgCOUaAAAAMIRyDQAAABhCuQYAAAAMoVwDAAAA\nhlCuAQAAAEMo1wAAAIAhlGsAAADAEMo1AAAAYAjlGgAAADCEcg0AAAAYQrkGAAAADKFcAwAAAIZQ\nrgEAAABDKNcAAACAIZRrAAAAwBDKNQAAAGAI5RoAAAAwhHINAAAAGEK5BgAAAAyhXAMAAACGUK4B\nAAAAQyjXAAAAgCGUawAAAMCQfpfrvLw8xcTEKCgoSDabTRUVFfec+9FHH+n73/++IiMjNWTIECUk\nJKigoMBIYAAAAMBT9atcFxUVacOGDcrMzFRdXZ3sdrsWLFigq1ev9ji/qqpKCQkJOLine truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4a937b8>"
+       "<matplotlib.figure.Figure at 0x81a96d8>"
       ]
      },
      "metadata": {},
@@ -795,9 +805,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x67e64e0>"
+       "<matplotlib.figure.Figure at 0x79580f0>"
       ]
      },
      "metadata": {},
@@ -1030,9 +1040,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7033240>"
+       "<matplotlib.figure.Figure at 0x822b080>"
       ]
      },
      "metadata": {},
@@ -1069,9 +1079,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7033ba8>"
+       "<matplotlib.figure.Figure at 0x824c080>"
       ]
      },
      "metadata": {},
@@ -1100,9 +1110,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAADaCAYAAABtj26qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7162940>"
+       "<matplotlib.figure.Figure at 0x833f0b8>"
       ]
      },
      "metadata": {},
@@ -1131,9 +1141,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAADaCAYAAABtj26qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x716b518>"
+       "<matplotlib.figure.Figure at 0x8278fd0>"
       ]
      },
      "metadata": {},
@@ -1193,9 +1203,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7136518>"
+       "<matplotlib.figure.Figure at 0x792c438>"
       ]
      },
      "metadata": {},
@@ -1231,9 +1241,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAADaCAYAAABtj26qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71950b8>"
+       "<matplotlib.figure.Figure at 0x78f7cf8>"
       ]
      },
      "metadata": {},
@@ -1264,7 +1274,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAF9CAYAAADP4URIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtUVPXeP/D3DHe5KelwUY6Amh7TSBxvgEiF48HMPF5Q\nO/bEZJmYJppPRUdFifRk57DyBl4fNE8WHDuPVpKCoSJpBSHeMRIfNWE4QYphC/kJ398fLmY5ggzI\nnr3Beb/WYgXf2d/5fGcY333Ys2dvlRBCgIiIiIiI2kyt9AKIiIiIiB4WbK6JiIiIiCTC5pqIiIiI\nSCJsromIiIiIJMLmmoiIiIhIImyuiYiIiIgkwuaaiIiIiEgibK6pwwgPD4darcbly5cVW8Pt27ex\nevVq6PV6PPHEE7C3t4darcbGjRsVWxMRUUfXHvK9uLgYq1atQkREBP7whz/AwcEBGo0GkZGR+OKL\nLxRbF3U8tkovgKg1VCqVovWrq6uxYMECqFQqeHp6wtvbG1euXFF8XUREHZ3SObpkyRKkp6ejX79+\n+NOf/oRu3bqhpKQE//u//4v9+/dj0aJFWLVqlaJrpI6Be66pQxFCQMmLijo7O+Orr75CaWkpSktL\nodfrFVsLEdHDROl8/9Of/oT8/HycPXsWmzZtwnvvvYdPPvkEP/zwA9zc3PD3v/8dx48fV2x91HGw\nuaZ24YsvvkBERAR8fHzg6OgIHx8fhIaGYuXKlQAAtVqNnJwcAIC/vz/UajXUajX8/f1N7qeqqgpL\nly7FgAED4OzsDDc3N4wcORK7du1qVPPQoUNQq9XQ6/U4e/Ysxo8fDw8PD7i4uCAsLAxff/11ozl2\ndnYYM2YMPD09LfAsEBE9fDpKvkdHRyMoKKjR+GOPPYapU6ca75fIHB4WQorbtGkTZs+eDS8vL4wb\nNw4ajQYVFRU4c+YMNm7ciLi4OMTHx2Pbtm24dOkSYmNj0blzZwAw/hcArl69iieffBI//fQTwsLC\nMGbMGFRXV2Pv3r2IiopCfHw84uPjG9W/ePEiQkJC8MQTTyAmJgY///wz0tPTMWbMGKSnp2PixImy\nPRdERA+ThyXfbW1tTf5L1CxBpLCgoCDh6Ogo/vOf/zS6rbKy0vj9qFGjhEqlEpcuXWryfp5++mlh\nY2MjPv30U5PxGzduiKCgIKFWq8WJEyeM4wcPHhQqlUqoVCrx5ptvmsz57rvvhK2trXjkkUdEdXX1\nfdceHx8vVCqV2LhxY4seKxGRNenI+d7g+vXrwtPTU9ja2oqioiKz2xPxsBBqF2xsbJrcI+Dh4dGi\n+adOnUJ2djYmTJhgfPuugaurK5YtWwYhBD7++ONGczt37oylS5eajA0dOhRRUVH49ddfsWfPnlY8\nEiIiultHzvf6+nro9Xr85z//QUxMDPr27duiNZN14/sbpLgZM2bgjTfeQP/+/TF16lSMHDkSwcHB\n8Pb2bvF9fPPNNwDuHJO3bNmyRrf/8ssvAIBz5841ui0oKAjOzs6NxsPCwvDJJ5+gsLAQzz//fIvX\nQkREd3TkfBdCYM6cOdi9ezeefPJJJCUltXjNZN3YXJPiFixYAI1Gg5SUFKxfvx5r1qwBAAwfPhwr\nV67EqFGjzN5HZWUlAODrr79u8oMqwJ3TPN28ebPR+P0+nNgwXlVV1aLHQUREpjpqvgshMHv2bGze\nvBkRERH4/PPPebw1tRgPC6F24S9/+Qtyc3Nx7do17N+/H6+99hoKCgoQGRmJ4uJis/Pd3d0BAElJ\nSaivr2/yq66urslgLi8vb/I+G8Yb7puIiFqvo+V7XV0doqOjsXnzZkRGRuLLL7+Eo6Njax4yWTk2\n19SuuLi4YPTo0Vi7di3eeOMN1NTUYN++fQDuHLcH3Am+ewUHBwOA8XROrVFQUIDq6upG44cPHwYA\nDBo0qNX3SUREpjpCvv+///f/8Pzzz2PHjh2YMGEC9uzZA3t7+1bXJevG5poUl52d3eR4WVkZAKBT\np04AgEceeQQAcOnSpUbbBgUFYdSoUdizZw+2bNnS5P39+OOPuHLlSqPx69evIyEhwWTsu+++Q3p6\nOjw8PPDcc8+1/MEQEZFRR8r3W7duYdKkSfjXv/6FqVOnYteuXTwUhB6ISggFL4dEhDuf5nZxccHw\n4cPRs2dPqFQqfP/998jNzUXv3r3xww8/wNXVFVu2bMGsWbPQq1cvTJw4Ea6urujSpQtee+01AHfC\n+umnn0ZRUREGDhyIYcOGwcPDA1evXsWZM2dQWFiI3bt3Y/z48QDuXAzgqaeewsiRI3Hy5EkEBgYi\nODgYV69eRVpaGurq6pCWltboPKh/+9vfUFRUBAAoLCzEyZMnMXz4cDz66KMAgD/+8Y946623ZHwG\niYjap46U73q9Htu3b4e7uzvmzZsHtbrx/sdBgwZxhwuZZ+5cfYcPHxbPPvus6N69u1CpVGLbtm1m\nz+938uRJERYWJpycnET37t1FQkJCG88YSA+zDRs2iIkTJ4pevXoJZ2dn0blzZxEYGCiWL18ufv31\nV+N29fX1YunSpaJ3797C3t5eqFQq4e/vb3JfN2/eFO+//74YMmSIcHV1FY6OjsLf31+MGTNGrFu3\nTly7ds24bcN5UPV6vTh37pwYP3686NKli3B2dhZhYWHi66+/bnK94eHhQqVSCbVabfLVMPbkk09a\n5okikhjznSytI+V7eHi4SZ7f+6VWq4Ver7fck0UPDbPNdUZGhvjrX/8qdu3aJTp16iS2b9/e7PZV\nVVXC09NTTJ06VZw5c0bs2rVLuLq6in/84x+SLZpICneHL5E1Yr7Tw4r5TkoyezBRZGQkIiMjAQDR\n0dFm94R//PHHqKmpwfbt2+Hg4ID+/fujqKgISUlJWLhwYZv3tBMRkTSY70RE0pP8A43Hjh3DyJEj\n4eDgYBzT6XQoLS1t8oMKRETUMTDfiYjMk7y5NhgMjU7a3vCzwWCQuhwREcmE+U5EZJ7k55hRqVQt\n3pZXviMlDRo0CNeuXQPA1yJZ3sNwMSLmO3UUzHeS0735Lvmeay8vr0Z7MBquhOTl5SV1OSIikgnz\nnYjIPMmb6xEjRuDIkSO4deuWcSwrKwvdu3dHz549pS5HREQyYb4TEZln9rCQmzdvori4GABQX1+P\nS5cuobCwEI888gh8fX0RFxeHvLw8HDhwAADw/PPPY/ny5YiOjsbixYtx/vx5vP/++1i2bFmzdVry\nlml+fj4AQKvVmt3WEpSsb621la7P2vydt0V7fzua+a58baXrW2ttpetba22l68uV72b3XOfl5SEo\nKAhBQUGoqalBfHw8goKCEB8fD+DOh1hKSkqM27u5uSErKwulpaXQarWYN28eFi1ahAULFrT5gRAR\nkXSY70RE0jO75zo8PBz19fX3vT01NbXR2IABA3D48OG2rYyIiCyK+U5EJD3Jj7kmIiIiIrJWbK6J\niIiIiCTC5pqIiIiISCJsromIiIiIJMLmmoiIiIhIImyuiYiIiIgk0qLmOjk5Gf7+/nBycoJWq0Vu\nbm6z22dkZGD48OFwc3NDt27dMGHCBOOFCoiIqP1gvhMRSctsc52WlobY2FgsXrwYhYWFCA4ORmRk\nJK5cudLk9j/99BMmTJiA8PBwFBYW4sCBA6ipqcHYsWMlXzwRET045jsRkfTMNtdJSUnQ6/WYOXMm\n+vbtizVr1sDb2xspKSlNbl9YWIj6+nqsXLkSAQEBCAwMxFtvvYULFy7g119/lfwBEBHRg2G+ExFJ\nr9nmura2FgUFBdDpdCbjOp0OR48ebXJOSEgIXFxcsHnzZtTV1eG3337Dtm3bMHToUHh4eEi3ciIi\nemDMdyIiy2i2ua6oqEBdXR08PT1NxjUaDQwGQ5NzvL29kZGRgcWLF8PR0RGdO3fGmTNn8MUXX0i3\naiIiahPmOxGRZaiEEOJ+N5aWlqJHjx7IyclBaGiocTwhIQE7d+5EUVFRozklJSUYPnw49Ho9nn/+\nedy4cQNLly4FAGRnZ0OlUhm3raqqMn7PD8QQ0cOmT58+xu/d3d0VXEljzHciogfXXL7bNjexa9eu\nsLGxQXl5ucl4eXk5vL29m5yzceNG+Pr64v333zeO/fOf/4Svry+OHTuG4ODgVj8AIiKSFvOdiMgy\nmm2u7e3tMXjwYGRmZmLSpEnG8aysLEyZMqXJOUIIqNWmR5s0/FxfX3/fWlqt1uxi8/PzW7ytJShZ\n31prK12ftfk7b4u79962N8z39lFb6frWWlvp+tZaW+n6cuW72bOFLFy4ENu2bcPWrVtx7tw5zJ8/\nHwaDAbNnzwYAxMXFISIiwrj9+PHjUVBQgHfffRfFxcUoKCiAXq/HH/7wBwwePLjND4aIiKTBfCci\nkl6ze64BICoqCpWVlUhMTERZWRkGDhyIjIwM+Pr6AgAMBgNKSkqM24eGhiItLQ1/+9vfsGrVKnTq\n1AkjRozAvn374OTkZLlHQkRErcJ8JyKSntnmGgBiYmIQExPT5G2pqamNxiZPnozJkye3bWVERGRx\nzHciImm16PLnRERERERkHptrIiIiIiKJsLkmIiIiIpIIm2siIiIiIomwuSYiIiIikkiLmuvk5GT4\n+/vDyckJWq0Wubm5Zud8+OGH6NevHxwdHeHj44O4uLg2L5aIiKTFfCcikpbZU/GlpaUhNjYWKSkp\nCA0Nxfr16xEZGYmzZ88az4V6r4ULF2Lv3r34+9//joEDB6KqqgplZWWSL56IiB4c852ISHpmm+uk\npCTo9XrMnDkTALBmzRrs27cPKSkpWLFiRaPtz58/j3Xr1uHUqVPo27evcTwwMFDCZRMRUVsx34mI\npNfsYSG1tbUoKCiATqczGdfpdDh69GiTc/bs2YOAgABkZGQgICAA/v7+iI6Oxi+//Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x700b710>"
+       "<matplotlib.figure.Figure at 0x8071908>"
       ]
      },
      "metadata": {},
@@ -1322,9 +1332,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x682fcc0>"
+       "<matplotlib.figure.Figure at 0x825e9b0>"
       ]
      },
      "metadata": {},
@@ -1512,9 +1522,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ff6860>"
+       "<matplotlib.figure.Figure at 0x8326048>"
       ]
      },
      "metadata": {},
@@ -1570,9 +1580,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x67db278>"
+       "<matplotlib.figure.Figure at 0x83a3ac8>"
       ]
      },
      "metadata": {},
@@ -1606,7 +1616,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAADvCAYAAAA5OPi0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7377eb8>"
+       "<matplotlib.figure.Figure at 0x84c09b0>"
       ]
      },
      "metadata": {},

12_Designing_Nonlinear_Kalman_Filters.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -109,7 +109,7 @@
        "        color: #1d3b84;\n",
        "        font-size: 16pt;\n",
        "        margin-bottom: 0em;\n",
-       "        margin-top: 1.5em;\n",
+       "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "        white-space: nowrap;\n",
        "    }\n",
@@ -133,6 +133,11 @@
        "        overflow-y: scroll;\n",
        "        max-height: 50000px;\n",
        "    }\n",
+       "    div.output_wrapper{\n",
+       "        margin-top:0.2em;\n",
+       "        margin-bottom:0.2em;\n",
+       "}\n",
+       "\n",
        "    code{\n",
        "      font-size: 70%;\n",
        "    }\n",
@@ -245,7 +250,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -363,16 +368,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEPCAYAAACeKXhLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6a19278>"
+       "<matplotlib.figure.Figure at 0x7f50438>"
       ]
      },
      "metadata": {},
@@ -492,30 +497,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "text/latex": [
-       "$$\\phi$$"
-      ],
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAATBAMAAAC0B+rjAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAIokQdkQymVRmzbur3ZYT\n5QUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAABhSURBVAgdY2BgYGAEYiBgQqFMILwwCKUAotgSChuB\nVAXjBC4BBu4JnAJMBxh8gdrYLzBcB1JcGxheMkgyyDUwLGGYyLCdgSGWQYG7AKitcqcaSDvjBBDJ\nwCkApqC2sYA4ADr+DeweQRu1AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "\\phi"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import sympy\n",
-    "from sympy import  init_printing\n",
-    "#from sympy.interactive import printing\n",
-    "init_printing(use_latex='mathjax')\n",
+    "from sympy import init_printing\n",
+    "init_printing(use_latex='png')\n",
     "\n",
     "phi, x = sympy.symbols('\\phi, x')\n",
     "phi"
@@ -530,16 +532,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "text/latex": [
-       "$$\\frac{1}{2 \\sqrt{\\phi}}$$"
-      ],
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAACkAAAAwBAMAAAB+l06WAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZjKJ\nRFRer8KoAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABOklEQVQ4EWNggAMhOAuJYfIJiQNjqoRhE2Vg\nH76irBk/s2C+H3r0fyzgA328YQ+xGs2yXCCfGSxmGuINk2MWALJ4QDymBIbzF6DCrA+ADLAomwID\nRwNUVBNEq4MIjgkMbL9BDCCIAxG3QQTjJ7go0wSQwAEQAQSc0DTKs4CBgXtD8GOwIMP9AxAaZGwc\n8wJGAzB3CkSQoYqBgWUBlwFPAojP6sDAwAtyHNDYSqDDWMHGlwEFKgQYGNgLGBimAkUZgaoY+AIY\njJnV/wItBXJ+MVgx2D8AMrYyMBQzMH1hYFgD5IgzLGZwBdLMc86EAQ2KN2CYBOTdZDjAEgCkGYGB\nBxS1D+A6AORxR3mdBlIwwPaJUwHEZl4AImGA+89TMJPLACYCpiWEwBQ4HBES70GWAB2NEAGxoL6H\nCAIAAx9b8PUdS4gAAAAASUVORK5CYII=\n",
       "text/plain": [
        "   1   \n",
        "───────\n",
@@ -547,7 +547,7 @@
        "2⋅╲╱ φ "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -565,22 +565,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "text/latex": [
-       "$$\\left(\\phi - 1\\right) \\left(\\phi^{2} + 1\\right)$$"
-      ],
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIsAAAAcBAMAAACjVFE3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nCW5MapLtTWzvpPkP4lmKXRXJfWcAAAAASUVORK5CYII=\n",
       "text/plain": [
        "        ⎛ 2    ⎞\n",
        "(φ - 1)⋅⎝φ  + 1⎠"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -598,16 +596,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "text/latex": [
-       "$$\\left[\\begin{matrix}\\frac{x - x_{a}}{\\sqrt{\\left(x - x_{a}\\right)^{2} + \\left(y - y_{a}\\right)^{2}}} & 0 & \\frac{y - y_{a}}{\\sqrt{\\left(x - x_{a}\\right)^{2} + \\left(y - y_{a}\\right)^{2}}} & 0\\\\\\frac{x - x_{b}}{\\sqrt{\\left(x - x_{b}\\right)^{2} + \\left(y - y_{b}\\right)^{2}}} & 0 & \\frac{y - y_{b}}{\\sqrt{\\left(x - x_{b}\\right)^{2} + \\left(y - y_{b}\\right)^{2}}} & 0\\end{matrix}\\right]$$"
-      ],
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAABLCAMAAAB3JhEVAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA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\nMB2G8BXjPe1/W0n2gP6pJLOpfwGec844NmVfmwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "⎡           x - xₐ                           y - yₐ              ⎤\n",
        "⎢ ──────────────────────────   0   ──────────────────────────   0⎥\n",
@@ -622,7 +618,7 @@
        "⎣╲╱  (x - x_b)  + (y - y_b)       ╲╱  (x - x_b)  + (y - y_b)     ⎦"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -690,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -720,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -756,16 +752,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "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 0x7ee8518>"
+       "<matplotlib.figure.Figure at 0x872c860>"
       ]
      },
      "metadata": {},

code/book_plots.py

@@ -7,6 +7,26 @@ Created on Fri May  2 12:21:40 2014
 import matplotlib.pyplot as plt
 import numpy as np
 
+def plot_errorbars(bars, xlims):
+
+    i = 1.0
+    for bar in bars:
+        plt.errorbar([bar[0]], [i], xerr=[bar[1]], fmt='o', label=bar[2] , capthick=2, capsize=10)
+        i += 0.2
+
+    plt.ylim(0, 2)
+    plt.xlim(xlims[0], xlims[1])
+    show_legend()
+    plt.gca().axes.yaxis.set_ticks([])
+    plt.show()
+    
+        
+
+
+def show_legend():
+    plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+
+
 def bar_plot(pos, ylim=(0,1), title=None):
     plt.cla()
     ax = plt.gca()

code/gh_internal.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pylab as plt
 from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow
+import book_plots
 
 def create_predict_update_chart(box_bg = '#CCCCCC',
                 arrow1 = '#88CCFF',
@@ -119,6 +120,80 @@ def plot_estimate_chart_3():
     plt.ylabel('weight (lbs)')
     plt.show()
 
+def plot_hypothesis():
+    plt.errorbar([1, 2, 3], [170, 161, 169], 
+                 xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)
 
+    plt.plot([1, 3], [180, 160], color='g', ls='--')
+    plt.plot([1, 3], [170, 170], color='g', ls='--')
+    plt.plot([1, 3], [160, 175], color='g', ls='--')
+    plt.plot([1, 2, 3], [180, 152, 179], color='g', ls='--')
+    plt.xlim(0,4); plt.ylim(150, 185)
+    plt.xlabel('day')
+    plt.ylabel('lbs')
+    plt.tight_layout()
+    plt.show()
+    
+def plot_hypothesis2():
+    plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170], 
+                 xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
+    plt.plot([1, 10], [169, 170.5], color='g', ls='--')
+    plt.xlim(0, 11); plt.ylim(150, 185)
+    plt.xlabel('day')
+    plt.ylabel('lbs')
+    plt.show()
+    
+    
+def plot_hypothesis3():   
+    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
+           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
+    plt.errorbar(range(1, 13), weights,
+                 xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)
+
+    plt.xlim(0, 13); plt.ylim(145, 185)
+    plt.xlabel('day')
+    plt.ylabel('weight (lbs)')
+    plt.show()
+
+    
+def plot_hypothesis4():
+    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
+           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
+
+    ave = np.sum(weights) / len(weights)
+    plt.errorbar(range(1,13), weights, label='weights',
+                 yerr=6, fmt='o', capthick=2, capsize=10)
+    plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')
+    plt.xlim(0, 13); plt.ylim(145, 185)
+    plt.xlabel('day')
+    plt.ylabel('weight (lbs)')
+    book_plots.show_legend()
+    plt.show()
+
+    
+def plot_hypothesis5():   
+    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
+           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
+
+    xs = range(1, len(weights)+1)
+    line = np.poly1d(np.polyfit(xs, weights, 1))
+    plt.errorbar(range(1, 13), weights, label='weights',
+                 yerr=5, fmt='o', capthick=2, capsize=10)
+    plt.plot (xs, line(xs), c='r', label='hypothesis')
+    plt.xlim(0, 13); plt.ylim(145, 185)
+    plt.xlabel('day')
+    plt.ylabel('weight (lbs)')
+    book_plots.show_legend()
+    plt.show()
+
+def plot_g_h_results(measurements, filtered_data, 
+                     title='', z_label='Scale', ):    
+    book_plots.plot_measurements(measurements, label=z_label)
+    book_plots.plot_filter(filtered_data)
+    book_plots.show_legend()
+    plt.title(title)
+    plt.gca().set_xlim(left=0,right=len(measurements))
+    plt.show()
+    
 if __name__ == '__main__':
     create_predict_update_chart()

styles/custom2.css

@@ -83,7 +83,7 @@
         color: #1d3b84;
         font-size: 16pt;
         margin-bottom: 0em;
-        margin-top: 1.5em;
+        margin-top: 0.5em;
         display: block;
         white-space: nowrap;
     }
@@ -107,6 +107,11 @@
         overflow-y: scroll;
         max-height: 50000px;
     }
+    div.output_wrapper{
+        margin-top:0.2em;
+        margin-bottom:0.2em;
+}
+
     code{
       font-size: 70%;
     }