Layout changes to make PDF read better.

Mostly just shortening lines of code. There was a SymPy font render issue as well.

01_gh_filter/g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ccf1289e0126a8e295c808727490b0379358aa10f7b39bee76c2752b020a4d54"
+  "signature": "sha256:b8b47f06752bcd373881f57282ed37ec8aee5434acd6a5476d94e6454106c397"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,13 +259,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 4,
+       "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f72b8c41518>"
+        "<IPython.core.display.HTML at 0x7f85acbb8320>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 1
     },
     {
      "cell_type": "heading",
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55a9f28>"
+        "<matplotlib.figure.Figure at 0x7f859071e7b8>"
        ]
       }
      ],
@@ -390,9 +390,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5376fd0>"
+        "<matplotlib.figure.Figure at 0x7f8590477780>"
        ]
       }
      ],
@@ -426,9 +426,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55d4240>"
+        "<matplotlib.figure.Figure at 0x7f85904062e8>"
        ]
       }
      ],
@@ -451,9 +451,11 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# a lot of the plotting code is not particularly useful to read, so for each chapter\n",
-      "# I have placed the uninteresting code in a file named xxx_internal. I import this\n",
-      "# file and call whatever function I need.\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.\n",
+      "\n",
       "import gh_internal\n",
       "gh_internal.plot_estimate_chart_1()"
      ],
@@ -463,9 +465,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5584e10>"
+        "<matplotlib.figure.Figure at 0x7f85acbbdba8>"
        ]
       }
      ],
@@ -490,9 +492,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55d4940>"
+        "<matplotlib.figure.Figure at 0x7f85902e8be0>"
        ]
       }
      ],
@@ -525,9 +527,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f61100622e8>"
+        "<matplotlib.figure.Figure at 0x7f85903ea898>"
        ]
       }
      ],
@@ -593,9 +595,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f546fdd8>"
+        "<matplotlib.figure.Figure at 0x7f85901085c0>"
        ]
       }
      ],
@@ -622,9 +624,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f550ed30>"
+        "<matplotlib.figure.Figure at 0x7f85900efa58>"
        ]
       }
      ],
@@ -694,9 +696,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55385c0>"
+        "<matplotlib.figure.Figure at 0x7f85901f1a90>"
        ]
       }
      ],
@@ -759,9 +761,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5466828>"
+        "<matplotlib.figure.Figure at 0x7f85901bba20>"
        ]
       }
      ],
@@ -872,9 +874,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5438ac8>"
+        "<matplotlib.figure.Figure at 0x7f85900fc9e8>"
        ]
       }
      ],
@@ -951,9 +953,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f553d978>"
+        "<matplotlib.figure.Figure at 0x7f8590129ef0>"
        ]
       }
      ],
@@ -1007,9 +1009,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5576d30>"
+        "<matplotlib.figure.Figure at 0x7f859027e2b0>"
        ]
       }
      ],
@@ -1070,9 +1072,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f54a0278>"
+        "<matplotlib.figure.Figure at 0x7f85902e5da0>"
        ]
       }
      ],
@@ -1125,18 +1127,18 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def gen_data (x0, dx, count, noise_factor, accel=0):\n",
+      "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",
+      "    for i in range(count):\n",
+      "        zs.append(x0 + dx*i + random.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",
+      "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",
       "plt.xlim([0,20])\n",
-      "plot_g_h_results  (measurements=zs, filtered_data=data)"
+      "plot_g_h_results(measurements=zs, filtered_data=data)"
      ],
      "language": "python",
      "metadata": {},
@@ -1144,9 +1146,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f60f54dcb00>"
+        "<matplotlib.figure.Figure at 0x7f859034b128>"
        ]
       }
      ],
@@ -1202,42 +1204,26 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "zs = gen_data (x0=5, dx=5, count=100, noise_factor=50)\n",
+      "zs = gen_data(x0=5, dx=5, count=100, noise_factor=50)\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",
+      "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",
+      "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')"
+      "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')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5496f28>"
+       "ename": "SyntaxError",
+       "evalue": "invalid syntax (<ipython-input-22-786b45432a0a>, line 10)",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-22-786b45432a0a>\"\u001b[1;36m, line \u001b[1;32m10\u001b[0m\n\u001b[1;33m    plot_g_h_results  zs, data, 'g = 0.9')\u001b[0m\n\u001b[1;37m                       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
        ]
       }
      ],
@@ -1259,44 +1245,18 @@
       "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",
+      "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",
       "\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",
+      "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')"
+      "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')"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f53c9d68>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5466a58>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5446a20>"
-       ]
-      }
-     ],
-     "prompt_number": 23
+     "outputs": []
     },
     {
      "cell_type": "markdown",
@@ -1313,33 +1273,15 @@
      "input": [
       "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",
+      "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",
       "\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')"
+      "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')"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55969b0>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f55d4630>"
-       ]
-      }
-     ],
-     "prompt_number": 24
+     "outputs": []
     },
     {
      "cell_type": "heading",
@@ -1364,46 +1306,20 @@
      "input": [
       "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",
+      "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",
       "\n",
-      "data = g_h_filter (data=zs, x0=0, dx=2., dt=1.,g=.2, h=0.05)\n",
+      "data = g_h_filter(data=zs, x0=0, dx=2., dt=1.,g=.2, h=0.05)\n",
       "plt.ylim([-1,5])\n",
-      "plot_g_h_results  (zs, data, 'dx=2, h = 0.05')\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",
+      "data = g_h_filter(data=zs, x0=0, dx=2., dt=1.,g=.2, h=0.5)\n",
       "plt.ylim([-1,5])\n",
-      "plot_g_h_results  (zs, data, 'dx=2, h = 0.5')"
+      "plot_g_h_results(zs, data, 'dx=2, h = 0.5')"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "output_type": "display_data",
-       "png": 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truncated
-       "text": [
-        "<matplotlib.figure.Figure at 0x7f60f5494748>"
-       ]
-      }
-     ],
-     "prompt_number": 25
+     "outputs": []
     },
     {
      "cell_type": "heading",
@@ -1470,7 +1386,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "-  [1] [NASA Kalman Filtering Presentation](http://nescacademy.nasa.gov/review/downloadfile.php?file=FundamentalsofKalmanFiltering_Presentation.pdf&amp;id=199&amp;distr=Public)"
+      "*  [1] NASA Kalman Filtering Presentation<p> http://nescacademy.nasa.gov/review/downloadfile.php?file=FundamentalsofKalmanFiltering_Presentation.pdf&amp;id=199&amp;distr=Public\n",
+      "\n",
+      "\n"
      ]
     }
    ],