Implemeenting PDF in 6x9 format.

I did the work on chapter 1 just to start somewhere. Trying to make
the book fit nicely on a 6x9 book format.

.gitignore

@@ -8,5 +8,6 @@ short.pdf
 book.ipynb
 *.tex
 *.toc
+book.pdf
 Kalman_and_Bayesian_Filters_in_Python.pdf
 book_files

01-g-h-filter.ipynb

@@ -315,7 +315,7 @@
     "\n",
     "We will deal with this more formally later, but for now I hope it is clear that our best estimate is the average of A and B. $\\frac{160+170}{2} = 165$.\n",
     "\n",
-    "We can look at this graphically. I have plotted the measurements of A and B with an assumed error of $\\pm$ 8 lbs. The overlap falls between 160 and 170 so the only weight that makes sense must lie within 160 and 170 pounds."
+    "We can look at this graphically. I have plotted the measurements of A and B with an assumed error of $\\pm$ 8 lbs. The overlap falls between 160 and 170 so the only weight that makes sense must lie within 160 and 170 pounds.  a lot of the plotting code is not particularly useful to read, so for each chapter I have placed the uninteresting code in a file named xxx_internal. I import this file and call whatever function I need. If you want to read the code, the file exists in the code subdirectory."
    ]
   },
   {
@@ -329,7 +329,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x51d69b0>"
+       "<matplotlib.figure.Figure at 0x462c438>"
       ]
      },
      "metadata": {},
@@ -337,10 +337,8 @@
     }
    ],
    "source": [
-    "import book_format\n",
-    "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))"
+    "import gh_internal as gh\n",
+    "gh.plot_errorbar1()"
    ]
   },
   {
@@ -365,7 +363,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x51d6978>"
+       "<matplotlib.figure.Figure at 0x6eac080>"
       ]
      },
      "metadata": {},
@@ -373,8 +371,7 @@
     }
    ],
    "source": [
-    "with book_format.figsize(y=1.5):\n",
-    "    book_plots.plot_errorbars([(160, 3, 'A'), (170, 9, 'B')], xlims=(145, 185))"
+    "gh.plot_errorbar2()"
    ]
   },
   {
@@ -397,7 +394,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAB6CAYAAADXnk6rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7887358>"
+       "<matplotlib.figure.Figure at 0x7453dd8>"
       ]
      },
      "metadata": {},
@@ -405,8 +402,7 @@
     }
    ],
    "source": [
-    "with book_format.figsize(y=1.5):\n",
-    "    book_plots.plot_errorbars([(160, 1, 'A'), (170, 9, 'B')], xlims=(145, 185))"
+    "gh.plot_errorbar3()"
    ]
   },
   {
@@ -433,15 +429,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0330\n"
+      "Average of measurements is 164.9799\n"
      ]
     }
    ],
    "source": [
     "import numpy as np\n",
-    "\n",
     "measurements = np.random.uniform(160, 170, size=10000)\n",
-    "print('Average of measurements is {:.4f}'.format(measurements.mean()))"
+    "print('Average of measurements is {:.4f}'.format(\n",
+    "      measurements.mean()))"
    ]
   },
   {
@@ -464,13 +460,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9686\n"
+      "Average of measurements is 164.9642\n"
      ]
     }
    ],
    "source": [
     "measurements = np.random.normal(165, 5, size=10000)\n",
-    "print('Average of measurements is {:.4f}'.format(measurements.mean()))"
+    "print('Average of measurements is {:.4f}'.format(\n",
+    "      measurements.mean()))"
    ]
   },
   {
@@ -497,7 +494,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x51d65c0>"
+       "<matplotlib.figure.Figure at 0x74cae10>"
       ]
      },
      "metadata": {},
@@ -505,14 +502,7 @@
     }
    ],
    "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. If you want to read the code, the file exists\n",
-    "# in the code subdirectory\n",
-    "\n",
-    "import gh_internal\n",
-    "gh_internal.plot_hypothesis()"
+    "gh.plot_hypothesis()"
    ]
   },
   {
@@ -535,7 +525,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x51d6198>"
+       "<matplotlib.figure.Figure at 0x7554e48>"
       ]
      },
      "metadata": {},
@@ -543,7 +533,7 @@
     }
    ],
    "source": [
-    "gh_internal.plot_hypothesis2()"
+    "gh.plot_hypothesis2()"
    ]
   },
   {
@@ -564,7 +554,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a27c18>"
+       "<matplotlib.figure.Figure at 0x75bc780>"
       ]
      },
      "metadata": {},
@@ -572,7 +562,7 @@
     }
    ],
    "source": [
-    "gh_internal.plot_hypothesis3()"
+    "gh.plot_hypothesis3()"
    ]
   },
   {
@@ -595,7 +585,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA48AAAEtCAYAAACoIlXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlclFX///H3DIui5IaiEgpquIRLIpqJKCEpqKktRmoC\n6lcrl/Snd5ZlN9ZtLuWdQtttmiuaS9/uFislNcGtQpLcU3NJTdJc6IZcmfn94de5HQFnVGCY8fV8\nPObxYM51ruv6HA4onznnOsdgNpvNAgAAAADgBoyODgAAAAAAUPaRPAIAAAAAbCJ5BAAAAADYRPII\nAAAAALCJ5BEAAAAAYBPJIwAAAADAJpJHAAAAAIBNDkse09PT1aNHD/n7+8toNGr+/PlWx//8808N\nHTpUderUUYUKFdS4cWPNmDHDqk5ERISMRqPVq2/fvqXZDAAAAAC4I7g76sZ5eXlq3ry54uPjFRcX\nJ4PBYHV81KhRSktLU0pKiurVq6e0tDQNHjxY1atX11NPPSVJMhgMGjhwoCZNmmQ5z8vLq1TbAQAA\nAAB3AoeNPMbExGjixIl67LHHZDQWDCMjI0NxcXHq2LGj6tatq/79+6tt27b64YcfrOp5eXnJ19fX\n8rrrrrtKqwkAAAAAcMcos888xsTE6PPPP9fRo0clSZs2bVJWVpaio6Ot6i1ZskQ1atRQ06ZN9fzz\nzys3N9cR4QIAAACAS3PYtFVbpk6dqri4ONWtW1fu7lfCfOedd9S1a1dLnb59+yowMFB+fn7asWOH\nxo0bp23btmnVqlWOChsAAAAAXFKZTR7/9re/6fvvv9cXX3yhgIAApaWlacyYMQoICFCXLl0kSYMH\nD7bUDw4OVoMGDdSmTRtt3bpVLVu2tBzLyckp9fgBAACA4lK5cmVHhwCUzeQxLy9PSUlJ+ve//61u\n3bpJkpo2baqsrCxNmzbNkjxeLyQkRG5ubtq/f79V8ggAAAAAuD1l8plHs9kss9lcYCEdo9Eos9lc\n5Hnbt29Xfn6+ateuXdIhAgAAAMAdxaFbdezbt0+SZDKZdPjwYWVlZcnHx0d16tRRp06d9OKLL8rb\n21t169ZVWlqaFi5cqDfffFOSdODAAaWkpKhbt27y8fHRrl27NGbMGIWEhCgsLKzI+zLkX3Zt2bJF\nkhQaGurgSHAj9JNzoJ+cA/3kHOgn5+CK/cSjVyhrHDbymJGRoZCQEIWEhOj8+fNKTExUSEiIEhMT\nJUmLFi3S/fffr6eeekrBwcF64403NHHiRA0bNkyS5OnpqbVr16pLly5q3LixRo4cqejoaK1evbrA\nnpEAAAAAgNvjsJHHiIgImUymIo/XqFFDs2fPLvK4v7+/1q1bVwKRAQAAAACuVyafeQQAAAAAlC0k\njwAAAAAAm0geAQAAAAA2kTwCAAAAAGwieQQAAAAA2ETyCAAAAACwieQRAAAAAGATySMAAAAAwCaS\nRwAAAACATSSPAAAAAACbSB4BAAAAADaRPAIAAAAAbCJ5BAAAAADYRPIIAAAAALCJ5BEAAAAAYBPJ\nIwAAAADAJpJHAAAAAIBNJI8AAAAAAJtIHgEAAAAANjkseUxPT1ePHj3k7+8vo9Go+fPnWx3/888/\nNXToUNWpU0cVKlRQ48aNNWPGDKs6Fy5c0IgRI1SjRg15e3urZ8+eOnbsWGk2AwAAAADuCA5LHvPy\n8tS8eXMlJSXJy8tLBoPB6vioUaO0atUqpaSkaM+ePXr55Zf14osvKiUlxarOJ598oiVLlmj9+vX6\n888/1b17d5lMptJuDgAAAAC4NIcljzExMZo4caIee+wxGY0Fw8jIyFBcXJw6duyounXrqn///mrb\ntq1++OEHSVJOTo7mzJmjadOmqVOnTmrZsqUWLlyobdu2afXq1aXdHAAAAABwaWX2mceYmBh9/vnn\nOnr0qCRp06ZNysrKUnR0tCQpMzNTly5dUufOnS3n+Pv7q0mTJtq0aZNDYgYAAAAAV+Xu6ACKMnXq\nVMXFxalu3bpyd78S5jvvvKOuXbtKkrKzs+Xm5iYfHx+r82rWrKnff/+91OMFAAAAAFdWZpPHv/3t\nb/r+++/1xRdfKCAgQGlpaRozZowCAgLUpUuXW77uli1bijFKlAT6yDnQT86BfnIO9JNzoJ+cgyv1\nU1BQkKNDAKyUyeQxLy9PSUlJ+ve//61u3bpJkpo2baqsrCxNmzZNXbp0Ua1atZSfn69Tp05ZjT5m\nZ2erQ4cOjgodAAAAAFxSmUwezWazzGZzgYV0jEajzGazJKlVq1by8PBQamqq+vTpI0k6evSo9uzZ\no3bt2hV57dDQ0JILHLfl6ieF9FHZRj85B/rJOdBPzoF+cg6u2E85OTmODgGw4rDkMS8vT/v27ZMk\nmUwmHT58WFlZWfLx8VGdOnXUqVMnvfjii/L29lbdunWVlpamhQsX6s0335QkVa5cWYMGDdLYsWPl\n6+uratWqafTo0WrRooWioqIc1SwAAAAAcEkOW201IyNDISEhCgkJ0fnz55WYmKiQkBAlJiZKkhYt\nWqT7779fTz31lIKDg/XGG29o4sSJGjZsmOUaM2bM0COPPKLY2Fi1b99elSpV0hdffFFgz0gAAAAA\nwO1x2MhjRESETCZTkcdr1Kih2bNn3/Aanp6eSk5OVnJycnGHBwAAAAC4Rpnd5xEAAAAAUHaQPAIA\nAAAAbCqTq60CAAAAVz2X1Mvuugs22q6TPPLT24gGuHMx8ggAAAAAsImRRwAAAJRp9owUXjs6ycgi\nUDIYeQQAAAAA2ETyCAAAAACwieQRAAAAAGATySMAAAAAwCaSRwAAAACATay2CgAA4EJuZk9Ee7By\nKYCrGHkEAAAAANjEyCMAALhj3cwo3YKNtuuUhVE69kQEUFIYeQQAAAAA2MTIIwAAuGMxSgcA9mPk\nEQAAAABgE8kjAAAAAMAmkkcAAAAAgE0kjwAAAAAAmxyWPKanp6tHjx7y9/eX0WjU/PnzrY4bjcZC\nX8OHD7fUiYiIKHC8b9++pd0UAAAAAHB5DlttNS8vT82bN1d8fLzi4uJkMBisjmdnZ1u9z8jI0MMP\nP6zY2FhLmcFg0MCBAzVp0iRLmZeXV8kGDgAodjez1549WBETAIDi57DkMSYmRjExMZKkhISEAsd9\nfX2t3n/66adq1KiRwsPDrcq9vLwK1AUAAAAAFC+n2OcxNzdXS5Ys0auvvlrg2JIlS7RkyRLVrFlT\nMTExSkxMlLe3twOiBADcKvbaAwCg7HOK5HHx4sW6dOmS4uPjrcr79u2rwMBA+fn5aceOHRo3bpy2\nbdumVatWOShSAAAAAHBNTpE8zpo1S7169ZKPj49V+eDBgy1fBwcHq0GDBmrTpo22bt2qli1bFnqt\nLVu2lGisuH30kXOgn5yDq/aTq7XL1drjylyxr2hT2RUUFOToEAArZX6rjqysLGVmZlolikUJCQmR\nm5ub9u/fXwqRAQAAAMCdo8yPPH7wwQeqX7++OnXqZLPu9u3blZ+fr9q1axdZJzQ0tDjDQzG6+ikh\nfVS20U/OwRX7acHG/37tKu1yxX5yRa74s0ebnENOTo6jQwCsOHSrjn379kmSTCaTDh8+rKysLPn4\n+KhOnTqSpL/++kuLFi3Siy++WOD8AwcOKCUlRd26dZOPj4927dqlMWPGKCQkRGFhYaXaFgAAAABw\ndQ6btpqRkaGQkBCFhITo/PnzSkxMVEhIiBITEy11li5dqnPnzmnAgAEFzvf09NTatWvVpUsXNW7c\nWCNHjlR0dLRWr15dYM9IAAAAAMDtcdjIY0REhEwm0w3rDBgwoNDEUZL8/f21bt26EogMAAAAAHC9\nMr9gDgAAAADA8UgeAQAAAAA2kTwCAAAAAGwq81t1AADgjJ5L6mV33Wu3GChK8shPbyMaAABuHyOP\nAAAAAACbGHkEAKAE2DNSeO3oJCOLAICyjuQRAAAATi119Upt/nK3jEaDTCazUoNXqnNUtKPDAlyO\n3cnjH3/8oY0bN2r37t36448/ZDAYVL16dTVp0kTt2rVT9erVSzJOAAAAoIDU1Ss1feZkPdCtiaVs\n+szJkkQCCRSzGyaPFy5c0KJFizR37lxt3Hjjp/nbtWunAQMG6KmnnlK5cuWKNUgAAACgMAuXzlVQ\n+6pWZUHtq2rRsnkkj0AxKzJ5fP/99/X666/rjz/+UOfOnTVjxgy1atVK9evXV9WqVWU2m3XmzBkd\nPHhQmZmZ+uabbzRs2DAlJiZq/PjxeuaZZ0qzHQAAoITdzAqy9uA5TxQHk/lyoeWXTZdKORLA9RWZ\nPL7++usaM2aMBg4cqMqVKxdax8vLS35+fgoLC9Nzzz2ns2fPas6cOXr99ddJHgEAAFDijAZ3SfkF\nyt2NHqUfDODiikweDxw4IE9Pz5u6WJUqVTR69GgNHz78tgMDAABlCyvIoizqHztA02dOtpq6unf9\naY1+5iUHRgW4piKTx5tNHIvrXAAAAMBeV59rHP/mSBkMBpnNZk18PonnHYESYPdqq9nZ2Tp+/Lha\ntmxpKdu9e7emT5+unJwcxcbG6tFHHy2RIAEAAICidI6K1oqdTazeAyh+diePw4cP14kTJ5Seni5J\nOn36tDp27KizZ8+qfPny+vjjj/Xpp5/q4YcfLrFgAQAAcHvYExHArbI7edy8ebOGDh1qeZ+SkqIz\nZ87oxx9/VOPGjdWpUydNmzaN5BEAALgMV0u02BMRwO0w2lvx1KlT8vPzs7z/4osvFB4ermbNmsnD\nw0OxsbHasWNHiQQJAABQ2q5NtO6PaawHujXR9JmTlbp6paNDu2U32hMRAGyxO3msVq2ajh8/Lkn6\n66+/tHHjRnXu3Nly3GAw6Pz588UfIQAAgAO4YqLFnogAbofd01bbt2+v9957T40bNLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ab7b38>"
+       "<matplotlib.figure.Figure at 0x7634e10>"
       ]
      },
      "metadata": {},
@@ -603,7 +593,7 @@
     }
    ],
    "source": [
-    "gh_internal.plot_hypothesis4()"
+    "gh.plot_hypothesis4()"
    ]
   },
   {
@@ -625,9 +615,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA48AAAEtCAYAAACoIlXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a27160>"
+       "<matplotlib.figure.Figure at 0x76b20b8>"
       ]
      },
      "metadata": {},
@@ -635,7 +625,7 @@
     }
    ],
    "source": [
-    "gh_internal.plot_hypothesis5()"
+    "gh.plot_hypothesis5()"
    ]
   },
   {
@@ -662,7 +652,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAAEtCAYAAABDOSyFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71094e0>"
+       "<matplotlib.figure.Figure at 0x7621c18>"
       ]
      },
      "metadata": {},
@@ -670,7 +660,7 @@
     }
    ],
    "source": [
-    "gh_internal.plot_estimate_chart_1()"
+    "gh.plot_estimate_chart_1()"
    ]
   },
   {
@@ -691,7 +681,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a5ef98>"
+       "<matplotlib.figure.Figure at 0x6e8ae10>"
       ]
      },
      "metadata": {},
@@ -699,7 +689,7 @@
     }
    ],
    "source": [
-    "gh_internal.plot_estimate_chart_2()"
+    "gh.plot_estimate_chart_2()"
    ]
   },
   {
@@ -734,7 +724,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x795add8>"
+       "<matplotlib.figure.Figure at 0x76d8eb8>"
       ]
      },
      "metadata": {},
@@ -742,7 +732,7 @@
     }
    ],
    "source": [
-    "gh_internal.plot_estimate_chart_3()"
+    "gh.plot_estimate_chart_3()"
    ]
   },
   {
@@ -795,7 +785,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d6f4e0>"
+       "<matplotlib.figure.Figure at 0x7429668>"
       ]
      },
      "metadata": {},
@@ -814,9 +804,7 @@
     "\n",
     "def predict_using_gain_guess(weight, gain_rate, do_print=True): \n",
     "    # store the filtered results\n",
-    "    estimates = []\n",
-    "    predictions = []\n",
-    "    estimates.append(weight)\n",
+    "    estimates, predictions = [weight], []\n",
     "\n",
     "    # most filter literature uses 'z' for measurements\n",
     "    for z in weights: \n",
@@ -826,25 +814,14 @@
     "        # update filter \n",
     "        weight = prediction + scale_factor * (z - prediction)\n",
     "\n",
-    "        # save for plotting\n",
+    "        # save\n",
     "        estimates.append(weight)\n",
     "        predictions.append(prediction)\n",
     "        if do_print:\n",
-    "            print('previous: {:.2f}, prediction: {:.2f} estimate {:.2f}'.format(\n",
-    "                  estimates[-2], prediction, weight))\n",
+    "            gh.print_results(estimates, prediction, weight)\n",
     "\n",
     "    # plot results\n",
-    "    n = len(weights)\n",
-    " \n",
-    "    xs = list(range(n+1))\n",
-    "    book_plots.plot_filter(xs, estimates, marker='o')\n",
-    "    book_plots.plot_measurements(xs[1:], weights, color='k', label='Scale', lines=False)\n",
-    "    book_plots.plot_track([0, n], [160, 160+n], c='k', label='Actual Weight')\n",
-    "    book_plots.plot_track(xs[1:], predictions, c='r', label='Predictions', marker='v')\n",
-    "    book_plots.show_legend()\n",
-    "    book_plots.set_labels(x='day', y='weight (lbs)')\n",
-    "    plt.xlim([0, n])\n",
-    "    plt.show()\n",
+    "    gh.plot_gh_results(weights, estimates, predictions)\n",
     "\n",
     "initial_guess = 160.\n",
     "predict_using_gain_guess (weight=initial_guess, gain_rate=1)"
@@ -872,7 +849,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d787f0>"
+       "<matplotlib.figure.Figure at 0x7501f28>"
       ]
      },
      "metadata": {},
@@ -880,7 +857,7 @@
     }
    ],
    "source": [
-    "predict_using_gain_guess (weight=initial_guess, gain_rate=0, do_print=False)"
+    "predict_using_gain_guess(initial_guess, 0, do_print=False)"
    ]
   },
   {
@@ -912,9 +889,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6kAAAEmCAYAAACAptaXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a5e9b0>"
+       "<matplotlib.figure.Figure at 0x7519be0>"
       ]
      },
      "metadata": {},
@@ -945,17 +922,7 @@
     "  \n",
     "    estimates.append(weight)\n",
     "\n",
-    "# plot results\n",
-    "n = len(weights)\n",
-    "plt.xlim([1, n])\n",
-    "   \n",
-    "days = np.linspace(1, n, n)\n",
-    "book_plots.plot_filter(estimates)\n",
-    "book_plots.plot_measurements(days, weights, c='b', label='Scale')\n",
-    "book_plots.plot_track([0, 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",
-    "book_plots.set_labels(x='day', y='weight (lbs)');"
+    "gh.plot_gh_results(weights, estimates, predictions);"
    ]
   },
   {
@@ -1017,7 +984,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a8d6a0>"
+       "<matplotlib.figure.Figure at 0x74ed588>"
       ]
      },
      "metadata": {},
@@ -1025,7 +992,7 @@
     }
    ],
    "source": [
-    "gh_internal.create_predict_update_chart()"
+    "gh.create_predict_update_chart()"
    ]
   },
   {
@@ -1047,18 +1014,18 @@
     "**Initialization**\n",
     "\n",
     "    1. Initialize the state of the filter\n",
-    "    2. Initialize our belief in the state - how accurate do we think it is\n",
+    "    2. Initialize our belief in the state\n",
     "    \n",
     "**Predict**\n",
     "\n",
     "    1. Based on the system behavior, predict state at the next time step\n",
-    "    2. Adjust belief in that state to account for the uncertainty in prediction\n",
+    "    2. Adjust beliefto account for the uncertainty in prediction\n",
     "    \n",
     "**Update**\n",
     "\n",
     "    1. Get a measurement and associated belief about its accuracy\n",
-    "    2. Compute difference (residual) between our estimated state and measurement\n",
-    "    3. Update state based on predicted state plus some proportion of the residual\n",
+    "    2. Compute residual between estimated state and measurement\n",
+    "    3. New estimate is somewhere on the residual line\n",
     "    \n",
     "We will use this same algorithm throughout the book, albeit with some modifications. "
    ]
@@ -1426,7 +1393,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78b65f8>"
+       "<matplotlib.figure.Figure at 0x745f4a8>"
       ]
      },
      "metadata": {},
@@ -1455,7 +1422,7 @@
     "\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)"
+    "plot_g_h_results(weights, data);"
    ]
   },
   {
@@ -1520,9 +1487,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78aa438>"
+       "<matplotlib.figure.Figure at 0x7506d30>"
       ]
      },
      "metadata": {},
@@ -1530,9 +1497,9 @@
     }
    ],
    "source": [
-    "import numpy.random as random\n",
+    "from numpy.random import randn\n",
     "def gen_data(x0, dx, count, noise_factor):\n",
-    "    return [x0 + dx*i + random.randn()*noise_factor for i in range (count)]\n",
+    "    return [x0 + dx*i + randn()*noise_factor for i in range(count)]\n",
     "\n",
     "measurements = gen_data(0, 1, 30, 1)\n",
     "data = g_h_filter(data=measurements, x0=0, dx=1, dt=1, g=.2, h=0.02)\n",
@@ -1557,19 +1524,9 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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truncated
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x92e2dd8>]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7886710>"
+       "<matplotlib.figure.Figure at 0x7502cc0>"
       ]
      },
      "metadata": {},
@@ -1577,7 +1534,7 @@
     }
    ],
    "source": [
-    "plt.plot([random.randn() for _ in range(3000)], lw=1)"
+    "plt.plot([randn() for _ in range(3000)], lw=1);"
    ]
   },
   {
@@ -1621,9 +1578,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8dab550>"
+       "<matplotlib.figure.Figure at 0x751b7f0>"
       ]
      },
      "metadata": {},
@@ -1684,9 +1641,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9321400>"
+       "<matplotlib.figure.Figure at 0x763a2e8>"
       ]
      },
      "metadata": {},
@@ -1751,7 +1708,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x938a7b8>"
+       "<matplotlib.figure.Figure at 0x89670f0>"
       ]
      },
      "metadata": {},
@@ -1762,13 +1719,13 @@
     "def gen_data(x0, dx, count, noise_factor, accel=0):\n",
     "    zs = []\n",
     "    for i in range(count):\n",
-    "        zs.append(x0 + dx*i + random.randn()*noise_factor)\n",
+    "        zs.append(x0 + dx*i + randn()*noise_factor)\n",
     "        dx += accel\n",
     "    return zs\n",
     "   \n",
     "predictions = []\n",
     "zs = gen_data(x0=10, dx=0, count=20, noise_factor=0, accel=2)\n",
-    "data = g_h_filter(data=zs, x0=10, dx=0, g=0.2, h=0.02, pred=predictions)\n",
+    "data = g_h_filter(data=zs, x0=10, dx=0, g=0.2, h=0.02)\n",
     "plt.xlim([0, 20])\n",
     "plot_g_h_results(measurements=zs, filtered_data=data)"
    ]
@@ -1827,9 +1784,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAGNCAYAAACbjrWCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x93d45c0>"
+       "<matplotlib.figure.Figure at 0x89d19e8>"
       ]
      },
      "metadata": {},
@@ -1837,6 +1794,7 @@
     }
    ],
    "source": [
+    "import book_format\n",
     "np.random.seed(100)\n",
     "zs = gen_data(x0=5, dx=5, count=50, noise_factor=50)\n",
     "data1 = g_h_filter(data=zs, x0=0., dx=5., dt=1., g=0.1, h=0.01)\n",
@@ -1845,9 +1803,9 @@
     "\n",
     "with book_format.figsize(y=6):\n",
     "    book_plots.plot_measurements(zs, lw=1, color='k')\n",
-    "    book_plots.plot_filter(data1, label='g = 0.1', marker='+', lw=2, ms=15)\n",
-    "    book_plots.plot_filter(data2, label='g = 0.4', marker='v', lw=2, ms=10)\n",
-    "    book_plots.plot_filter(data3, label='g = 0.8', lw=2)\n",
+    "    book_plots.plot_filter(data1, label='g=0.1', marker='s')\n",
+    "    book_plots.plot_filter(data2, label='g=0.4', marker='v')\n",
+    "    book_plots.plot_filter(data3, label='g=0.8', lw=2)\n",
     "    book_plots.show_legend()\n",
     "    book_plots.set_limits([20,40], [0, 250])"
    ]
@@ -1868,9 +1826,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAFSCAYAAABrMVuMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAFSCAYAAABrMVuMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x937e320>"
+       "<matplotlib.figure.Figure at 0x8a1f0f0>"
       ]
      },
      "metadata": {},
@@ -1888,9 +1846,9 @@
     "\n",
     "with book_format.figsize(y=5):\n",
     "    book_plots.plot_measurements(zs)\n",
-    "    book_plots.plot_filter(data1, label='g = 0.1', marker='+', lw=2, ms=10)\n",
-    "    book_plots.plot_filter(data2, label='g = 0.5', marker='v', lw=2)\n",
-    "    book_plots.plot_filter(data3, label='g = 0.9', lw=2)\n",
+    "    book_plots.plot_filter(data1, label='g=0.1', marker='s')\n",
+    "    book_plots.plot_filter(data2, label='g=0.5', marker='v')\n",
+    "    book_plots.plot_filter(data3, label='g=0.9', lw=2)\n",
     "    book_plots.show_legend()\n",
     "    plt.ylim([6, 20])"
    ]
@@ -1913,9 +1871,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5AAAAFLCAYAAACgKmPhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5AAAAFLCAYAAACgKmPhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x935f390>"
+       "<matplotlib.figure.Figure at 0x7597780>"
       ]
      },
      "metadata": {},
@@ -1923,15 +1881,15 @@
     }
    ],
    "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",
+    "zs = [5,6,7,8,9,9,9,9,9,10,11,12,13,14,\n",
+    "      15,16,16,16,16,16,16,16,16,16,16,16]\n",
     "data1 = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=.302, h=.054)\n",
     "data2 = g_h_filter(data=zs, x0=4., dx=1., dt=1., g=.546, h=.205)\n",
     "\n",
     "with book_format.figsize(y=5):\n",
     "    book_plots.plot_measurements(zs)\n",
-    "    book_plots.plot_filter(data2, label='g = 0.546\\nh = 0.205', marker='o', lw=2)\n",
-    "    book_plots.plot_filter(data1, label='g = 0.302\\nh = 0.054', marker='v', lw=2)\n",
+    "    book_plots.plot_filter(data2, label='g=0.546\\nh=0.205', marker='s')\n",
+    "    book_plots.plot_filter(data1, label='g=0.302\\nh=0.054', marker='v')\n",
     "    book_plots.show_legend()"
    ]
   },
@@ -1960,9 +1918,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5gAAAFLCAYAAACz/SMVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5MAAAFLCAYAAABLHdjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d29780>"
+       "<matplotlib.figure.Figure at 0x761b6a0>"
       ]
      },
      "metadata": {},
@@ -1976,12 +1934,11 @@
     "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",
-    "\n",
     "with book_format.figsize(y=5):\n",
     "    book_plots.plot_measurements(zs)\n",
-    "    book_plots.plot_filter(data1, label='dx=0, h = 0.05', lw=2)\n",
-    "    book_plots.plot_filter(data2, label='dx=2, h = 0.05', marker='v', lw=2)\n",
-    "    book_plots.plot_filter(data3, label='dx=2, h = 0.5',  marker='o', lw=2)\n",
+    "    book_plots.plot_filter(data1, label='dx=0, h=0.05')\n",
+    "    book_plots.plot_filter(data2, label='dx=2, h=0.05', marker='v')\n",
+    "    book_plots.plot_filter(data3, label='dx=2, h=0.5',  marker='s')\n",
     "    book_plots.show_legend()"
    ]
   },
@@ -2011,7 +1968,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvMAAAEWCAYAAADrZBh7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79746a0>"
+       "<matplotlib.figure.Figure at 0x6e75470>"
       ]
      },
      "metadata": {},
@@ -2020,7 +1977,7 @@
    ],
    "source": [
     "from IPython.html.widgets import interact, interactive, fixed\n",
-    "import IPython.html.widgets as widgets\n",
+    "from IPython.html.widgets import FloatSliderWidget\n",
     "\n",
     "zs = gen_data(x0=5, dx=5, count=100, noise_factor=50)\n",
     "\n",
@@ -2032,10 +1989,10 @@
     "    plt.show()\n",
     "\n",
     "interact (interactive_gh,           \n",
-    "          x=widgets.FloatSliderWidget(value=0., min=-50, max=50.), \n",
-    "          dx=widgets.FloatSliderWidget(value=5., min=-50., max=50.), \n",
-    "          g=widgets.FloatSliderWidget(value=0.1, min=0.01, max=2, step=.02), \n",
-    "          h=widgets.FloatSliderWidget(value=0.02, min=0.0, max=0.5, step=0.01));"
+    "          x=FloatSliderWidget(value=0., min=-50, max=50.), \n",
+    "          dx=FloatSliderWidget(value=5., min=-50., max=50.), \n",
+    "          g=FloatSliderWidget(value=0.1, min=0.01, max=2, step=.02), \n",
+    "          h=FloatSliderWidget(value=0.02, min=0.0, max=0.5, step=0.01));"
    ]
   },
   {
@@ -2058,7 +2015,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9306208>"
+       "<matplotlib.figure.Figure at 0x8965d68>"
       ]
      },
      "metadata": {},
@@ -2094,7 +2051,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d5cb38>"
+       "<matplotlib.figure.Figure at 0x89efe48>"
       ]
      },
      "metadata": {},
@@ -2160,7 +2117,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwMAAAE6CAYAAABKyI5vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x93daf28>"
+       "<matplotlib.figure.Figure at 0x741b2e8>"
       ]
      },
      "metadata": {},
@@ -2211,7 +2168,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x93cea20>"
+       "<matplotlib.figure.Figure at 0x8970dd8>"
       ]
      },
      "metadata": {},
@@ -2219,9 +2176,9 @@
     }
    ],
    "source": [
-    "zs = gen_train_data(23000, 15, 100)\n",
-    "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1., g=.01, h=0.0001)\n",
-    "plot_g_h_results(zs/1000, data/1000., 'g=0.01, h=0.0001', z_label='Measurements')"
+    "zs = gen_train_data(pos, 15, 100)\n",
+    "data = g_h_filter(data=zs, x0=pos, dx=15., dt=1., g=.01, h=0.0001)\n",
+    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.0001')"
    ]
   },
   {
@@ -2242,7 +2199,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b4c1d0>"
+       "<matplotlib.figure.Figure at 0x898eb00>"
       ]
      },
      "metadata": {},
@@ -2250,9 +2207,9 @@
     }
    ],
    "source": [
-    "zs = gen_train_data(23000, 15, 100)\n",
-    "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1., g=.2, h=0.0001)\n",
-    "plot_g_h_results(zs/1000, data/1000, 'g=0.2, h=0.0001', z_label='Measurements')"
+    "zs = gen_train_data(pos, 15, 100)\n",
+    "data = g_h_filter(data=zs, x0=pos, dx=15., dt=1., g=.2, h=0.0001)\n",
+    "plot_g_h_results(zs/1000, data/1000, 'g=0.2, h=0.0001')"
    ]
   },
   {
@@ -2275,7 +2232,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ad71d0>"
+       "<matplotlib.figure.Figure at 0x87e8630>"
       ]
      },
      "metadata": {},
@@ -2283,9 +2240,9 @@
     }
    ],
    "source": [
-    "zs = gen_train_data(23000, 15, 100)\n",
-    "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1., g=0.01, h=0.1)\n",
-    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.1', z_label='Measurements')"
+    "zs = gen_train_data(pos, 15, 100)\n",
+    "data = g_h_filter(data=zs, x0=pos, dx=15., dt=1., g=0.01, h=0.1)\n",
+    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.1')"
    ]
   },
   {
@@ -2325,7 +2282,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ae5f60>"
+       "<matplotlib.figure.Figure at 0x8808358>"
       ]
      },
      "metadata": {},
@@ -2333,9 +2290,9 @@
     }
    ],
    "source": [
-    "zs = gen_train_data_with_acc(23000, 15, 100)\n",
-    "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1., g=.01, h=0.0001)\n",
-    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.0001', z_label='Measurements')"
+    "zs = gen_train_data_with_acc(pos, 15, 100)\n",
+    "data = g_h_filter(data=zs, x0=pos, dx=15., dt=1., g=.01, h=0.0001)\n",
+    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.0001')"
    ]
   },
   {
@@ -2356,7 +2313,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x795a5c0>"
+       "<matplotlib.figure.Figure at 0x74f7710>"
       ]
      },
      "metadata": {},
@@ -2364,9 +2321,9 @@
     }
    ],
    "source": [
-    "zs = gen_train_data_with_acc(23000, 15, 100)\n",
-    "data = g_h_filter(data=zs, x0=23000, dx=15., dt=1., g=.01, h=0.001)\n",
-    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.001', z_label='Measurements')"
+    "zs = gen_train_data_with_acc(pos, 15, 100)\n",
+    "data = g_h_filter(data=zs, x0=pos, dx=15., dt=1., g=.01, h=0.001)\n",
+    "plot_g_h_results(zs/1000, data/1000, 'g=0.01, h=0.001')"
    ]
   },
   {
@@ -2478,24 +2435,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 59,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(1.965, 0.375)"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2.136, 0.339)\n"
+     ]
     }
    ],
    "source": [
-    "f.update(z=2.1, g=.85, h=.15)"
+    "print(f.update(z=2.1, g=.85, h=.15))"
    ]
   },
   {
@@ -2507,27 +2461,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 60,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([[ 1.965 ,  0.375 ],\n",
-       "       [ 2.868 ,  0.507 ],\n",
-       "       [ 3.875 ,  0.632 ],\n",
-       "       [ 4.9014,  0.7306]])"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2.136   0.339 ]\n",
+      " [ 2.895   0.444 ]\n",
+      " [ 3.8678  0.5762]\n",
+      " [ 4.8888  0.6874]]\n"
+     ]
     }
    ],
    "source": [
-    "f.batch_filter([3., 4., 5.])"
+    "print(f.batch_filter([3., 4., 5.]))"
    ]
   },
   {
@@ -2539,20 +2490,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 62,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(array([   3.8 ,   13.2 ,  101.64]), array([ 8.2 ,  9.8 ,  0.56]))"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " x = [   3.8    13.2   101.64]\n",
+      "dx = [ 8.2   9.8   0.56]\n"
+     ]
     }
    ],
    "source": [
@@ -2560,7 +2509,9 @@
     "dx_0 = np.array([10., 12., .2])\n",
     "               \n",
     "f_air = GHFilter (x=x_0, dx=dx_0, dt=1., g=.8, h=.2)\n",
-    "f_air.update(z=np.array((2., 11., 102.)))"
+    "f_air.update(z=np.array((2., 11., 102.)))\n",
+    "print(' x =', x)\n",
+    "print('dx =', dx)"
    ]
   },
   {
@@ -2623,7 +2574,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.4.1"
   }
  },
  "nbformat": 4,

code/gh_internal.py

@@ -2,6 +2,41 @@ import numpy as np
 import pylab as plt
 from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow
 import book_plots
+import book_format
+
+def plot_errorbar1():
+    with book_format.figsize(y=1.5):
+        book_plots.plot_errorbars([(160, 8, 'A'), (170, 8, 'B')],
+                                   xlims=(145, 185))
+
+def plot_errorbar2():
+    with book_format.figsize(y=1.5):
+        book_plots.plot_errorbars([(160, 3, 'A'), (170, 9, 'B')],
+                                   xlims=(145, 185))
+
+def plot_errorbar3():
+    with book_format.figsize(y=1.5):
+        book_plots.plot_errorbars([(160, 1, 'A'), (170, 9, 'B')], xlims=(145, 185))
+
+
+
+def plot_gh_results(weights, estimates, predictions):
+    n = len(weights)
+
+    xs = list(range(n+1))
+    book_plots.plot_filter(xs, estimates, marker='o')
+    book_plots.plot_measurements(xs[1:], weights, color='k', label='Scale', lines=False)
+    book_plots.plot_track([0, n], [160, 160+n], c='k', label='Actual Weight')
+    book_plots.plot_track(xs[1:], predictions, c='r', label='Predictions', marker='v')
+    book_plots.show_legend()
+    book_plots.set_labels(x='day', y='weight (lbs)')
+    plt.xlim([0, n])
+    plt.show()
+
+
+def print_results(estimates, prediction, weight):
+    print('previous: {:.2f}, prediction: {:.2f} estimate {:.2f}'.format(
+          estimates[-2], prediction, weight))
 
 def create_predict_update_chart(box_bg = '#CCCCCC',
                 arrow1 = '#88CCFF',

pdf/6x9build_book.bat

@@ -0,0 +1,5 @@
+copy /Y  ..\01-g-h-filter.ipynb book.ipynb
+
+ipython nbconvert --to latex --template book6x9 book.ipynb
+ipython to_pdf.py
+REM move /Y book.pdf book6x9.pdf

pdf/book6x9.tplx

@@ -0,0 +1,42 @@
+% Inherit from report
+((* extends 'report.tplx' *))
+ 
+((* set cell_style = 'style_ipython.tplx' *))
+
+((* block margins *))
+\geometry{verbose,papersize={6in, 9in}, tmargin=.5in,bmargin=.5in,lmargin=.5in,rmargin=.5in}
+((* endblock margins *))
+
+((*- block in_prompt -*))
+\mbox{ }\\ \\
+{In [{}(((cell.execution_count)))]:}
+((*- endblock in_prompt -*))
+
+((* block input scoped *))
+    ((( cell.source | highlight2latex )))
+((* endblock input *))
+
+
+((* block output_prompt *))
+\mbox{ }\\
+Out [{}(((cell.execution_count)))]:
+((* endblock output_prompt *))
+
+
+((* block docclass *))
+\documentclass{book}
+\setcounter{chapter}{0}
+((* endblock docclass *))
+ 
+((* block preamble *))
+((* endblock preamble *))
+ 
+((* block title *))
+\title{Kalman and Bayesian Filters in Python}
+\author{Roger R Labbe Jr}
+((* endblock title *))
+ 
+((* block markdowncell scoped *))
+    ((( cell.source | citation2latex | strip_files_prefix | markdown2latex(extra_args=["--chapters"]) )))
+((* endblock markdowncell *))
+