Fixed headers for nbconvert.

nbconvert does not work with the ### style headers, so I
converted to using the header cells where I was using the hash
headers.

01_gh_filter/g-h_filter.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:76b18f6f726f3b248980421ac520323a20d39a7e8530125174d604238ad883ef"
+  "signature": "sha256:3629e2fdae20e09b4bc3545d5894210b005707cdba2c96a26450fcac7a6df90e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fb59dec11d0>"
+        "<IPython.core.display.HTML at 0x7f6d1ef1ac50>"
        ]
       }
      ],
@@ -335,7 +335,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAGGCAYAAAANcKzOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb585028208>"
+        "<matplotlib.figure.Figure at 0x7f6d06a4c160>"
        ]
       }
      ],
@@ -372,7 +372,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAGGCAYAAAANcKzOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584e1bf98>"
+        "<matplotlib.figure.Figure at 0x7f6d067203c8>"
        ]
       }
      ],
@@ -406,7 +406,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb585028198>"
+        "<matplotlib.figure.Figure at 0x7f6d06a4c1d0>"
        ]
       }
      ],
@@ -448,7 +448,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Average of measurements is 164.9743\n"
+        "Average of measurements is 165.0019\n"
        ]
       }
      ],
@@ -484,7 +484,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Average of measurements is 164.9831\n"
+        "Average of measurements is 164.9496\n"
        ]
       }
      ],
@@ -527,7 +527,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584edab70>"
+        "<matplotlib.figure.Figure at 0x7f6d066a7198>"
        ]
       }
      ],
@@ -563,7 +563,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584f61a58>"
+        "<matplotlib.figure.Figure at 0x7f6d06689240>"
        ]
       }
      ],
@@ -598,7 +598,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584fabf60>"
+        "<matplotlib.figure.Figure at 0x7f6d0677d4a8>"
        ]
       }
      ],
@@ -636,7 +636,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584ffc978>"
+        "<matplotlib.figure.Figure at 0x7f6d066d03c8>"
        ]
       }
      ],
@@ -674,7 +674,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584db6ac8>"
+        "<matplotlib.figure.Figure at 0x7f6d0653e0b8>"
        ]
       }
      ],
@@ -713,7 +713,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584f61da0>"
+        "<matplotlib.figure.Figure at 0x7f6d066e2d68>"
        ]
       }
      ],
@@ -740,7 +740,7 @@
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584cc2eb8>"
+        "<matplotlib.figure.Figure at 0x7f6d06430dd8>"
        ]
       }
      ],
@@ -780,7 +780,7 @@
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584db06d8>"
+        "<matplotlib.figure.Figure at 0x7f6d0652d278>"
        ]
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@@ -848,7 +848,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584efab38>"
+        "<matplotlib.figure.Figure at 0x7f6d064437b8>"
        ]
       }
      ],
@@ -877,7 +877,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584efb9e8>"
+        "<matplotlib.figure.Figure at 0x7f6d06682dd8>"
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb585e37f28>"
+        "<matplotlib.figure.Figure at 0x7f6d0662dcf8>"
        ]
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@@ -1012,7 +1012,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584f58208>"
+        "<matplotlib.figure.Figure at 0x7f6d06769320>"
        ]
       }
      ],
@@ -1031,12 +1031,18 @@
       "Most of this book is devoted to expressing the concerns in the last three paragraphs mathematically, which then allows us to find an optimal solution (in a least squares sense). In this chapter we will merely be assigning different values to *g* and *h* in a more intuitive, and thus less optimal way. But the fundamental idea is just to blend somewhat inaccurate measurements with somewhat inaccurate models of how the systems behaves to get a filtered estimate that is better than either information source by itself."
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Notation and Nomenclature"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Notation and Nomenclature\n",
-      "\n",
       "I'll begin to introduce the nomenclature and variable names used in the literature. Some of this was already used in the above charts. Measurement is typically denoted $z$ and that is what we will use in this book (some literature uses $y$). Subscript $k$ indicates the time step, so $z_k$ is the data for this time step. A bold font denotes a vector or matrix. So far we have only considered having one sensor, and hence one sensor measurement, but in general we may have *n* sensors and *n* measurements. $\\mathbf{x}$ denotes our data, and is bold to denote that it is a vector. For example, for our scale example, it represents both the initial weight and initial weight gain rate, like so:\n",
       "\n",
       "$$\\mathbf{x} = \\begin{bmatrix}x \\\\ \\dot{x}\\end{bmatrix}$$\n",
@@ -1145,7 +1151,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584e797f0>"
+        "<matplotlib.figure.Figure at 0x7f6d066eab70>"
        ]
       }
      ],
@@ -1222,9 +1228,9 @@
       {
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584f25f98>"
+        "<matplotlib.figure.Figure at 0x7f6d06623828>"
        ]
       }
      ],
@@ -1278,9 +1284,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584cad9e8>"
+        "<matplotlib.figure.Figure at 0x7f6d0650ba58>"
        ]
       }
      ],
@@ -1341,9 +1347,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584d251d0>"
+        "<matplotlib.figure.Figure at 0x7f6d064c3278>"
        ]
       }
      ],
@@ -1417,7 +1423,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584fda630>"
+        "<matplotlib.figure.Figure at 0x7f6d0651d1d0>"
        ]
       }
      ],
@@ -1490,25 +1496,25 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb58500add8>"
+        "<matplotlib.figure.Figure at 0x7f6d06764438>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb59dec1400>"
+        "<matplotlib.figure.Figure at 0x7f6d065450b8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb59dec17f0>"
+        "<matplotlib.figure.Figure at 0x7f6d064db2b0>"
        ]
       }
      ],
@@ -1547,7 +1553,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584d5d828>"
+        "<matplotlib.figure.Figure at 0x7f6d0651d160>"
        ]
       },
       {
@@ -1555,7 +1561,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584d5d710>"
+        "<matplotlib.figure.Figure at 0x7f6d065c19e8>"
        ]
       },
       {
@@ -1563,7 +1569,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb585002198>"
+        "<matplotlib.figure.Figure at 0x7f6d064c8dd8>"
        ]
       }
      ],
@@ -1598,7 +1604,7 @@
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584d5da58>"
+        "<matplotlib.figure.Figure at 0x7f6d064302b0>"
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       },
       {
@@ -1606,7 +1612,7 @@
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584d5de10>"
+        "<matplotlib.figure.Figure at 0x7f6d04be41d0>"
        ]
       }
      ],
@@ -1654,7 +1660,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb5843df9b0>"
+        "<matplotlib.figure.Figure at 0x7f6d04bf65c0>"
        ]
       },
       {
@@ -1662,7 +1668,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584452c88>"
+        "<matplotlib.figure.Figure at 0x7f6d04b378d0>"
        ]
       },
       {
@@ -1670,18 +1676,24 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584472fd0>"
+        "<matplotlib.figure.Figure at 0x7f6d04bdd668>"
        ]
       }
      ],
      "prompt_number": 32
     },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Tracking a Train"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Tracking a Train\n",
-      "\n",
       "Now let's try a practical example. Earlier in the chapter we talked about tracking a train. Trains are heavy and slow, thus they cannot change speed quickly. They are on a track, so they cannot change direction except by slowing to a stop and then reversing course. Hence, we can conclude that if we already know the train's approximate position and velocity then we can predict its position in the near future with a great deal of accuracy. The train just cannot change its velocity much in a second or two. \n",
       "\n",
       "So let's write a filter for a train. Its position is expressed as its position on the track in relation to some fixed point which we say is 0 km. I.e., a position of 1 means that the train is 1 km away from the fixed point. Velocity is expresses as meters per second. We perform measurement of position once per second, and the error is $\\pm$ 500 meters. How should we implement our filter?\n",
@@ -1741,13 +1753,13 @@
       {
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        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584f61a90>"
+        "<matplotlib.figure.Figure at 0x7f6d06705b70>"
        ]
       }
      ],
-     "prompt_number": 35
+     "prompt_number": 33
     },
     {
      "cell_type": "markdown",
@@ -1772,13 +1784,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584ef4c18>"
+        "<matplotlib.figure.Figure at 0x7f6d06638908>"
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 34
     },
     {
      "cell_type": "markdown",
@@ -1801,13 +1813,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584fe4f98>"
+        "<matplotlib.figure.Figure at 0x7f6d066e8c50>"
        ]
       }
      ],
-     "prompt_number": 37
+     "prompt_number": 35
     },
     {
      "cell_type": "markdown",
@@ -1832,13 +1844,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584ecdf98>"
+        "<matplotlib.figure.Figure at 0x7f6d064c5c88>"
        ]
       }
      ],
-     "prompt_number": 38
+     "prompt_number": 36
     },
     {
      "cell_type": "markdown",
@@ -1864,7 +1876,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 39
+     "prompt_number": 37
     },
     {
      "cell_type": "code",
@@ -1880,13 +1892,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584ecd0f0>"
+        "<matplotlib.figure.Figure at 0x7f6d04b2b470>"
        ]
       }
      ],
-     "prompt_number": 40
+     "prompt_number": 38
     },
     {
      "cell_type": "markdown",
@@ -1909,13 +1921,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fb584fbc668>"
+        "<matplotlib.figure.Figure at 0x7f6d066342b0>"
        ]
       }
      ],
-     "prompt_number": 41
+     "prompt_number": 39
     },
     {
      "cell_type": "markdown",
@@ -1927,12 +1939,11 @@
      ]
     },
     {
-     "cell_type": "markdown",
+     "cell_type": "heading",
+     "level": 2,
      "metadata": {},
      "source": [
-      "## g-h Filters with FilterPy\n",
-      "\n",
-      "blah blah"
+      "g-h Filters with FilterPy"
      ]
     },
     {