Moved left margin to the left.

Last checkin had the same commit message. But I ran it in ./styles, so the notebooks were not commited.
2014-05-16 14:01:37 -07:00
1 parent cce95dc9f8
commit b8cfefc8f0
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:35f8f47267a42738f2da4b329547de79758e5299ece9361cd407c64a8960b18b"
+  "signature": "sha256:effec8da00a64ca2176d7b2e8c409aebee9e9defbf38ad9d1908494be9694875"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -22,6 +22,7 @@
     "collapsed": false,
     "input": [
      "#format the book\n",
+      "%matplotlib inline\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -33,7 +34,7 @@
        "<style>\n",
        "    div.cell{\n",
        "        width: 850px;\n",
-        "        margin-left: 10% !important;\n",
+        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
        "    h1 {\n",
@@ -237,13 +238,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 8,
       "text": [
-        "<IPython.core.display.HTML at 0x1389590>"
+        "<IPython.core.display.HTML at 0x3eb3dd0>"
       ]
      }
     ],
-     "prompt_number": 1
+     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
@@ -274,24 +275,16 @@
     "language": "python",
     "metadata": {},
     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/usr/local/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['monospace'] not found. Falling back to Bitstream Vera Sans\n",
-        "  (prop.get_family(), self.defaultFamily[fontext]))\n"
-       ]
-      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2ba9650>"
+        "<matplotlib.figure.Figure at 0x3eb3e50>"
       ]
      }
     ],
-     "prompt_number": 2
+     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
@@ -371,9 +364,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2ee8250>"
+        "<matplotlib.figure.Figure at 0x3f07f10>"
       ]
      },
      {
@@ -385,7 +378,7 @@
       ]
      }
     ],
-     "prompt_number": 3
+     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
@@ -432,13 +425,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2ec69d0>"
+        "<matplotlib.figure.Figure at 0x3ef3c10>"
       ]
      }
     ],
-     "prompt_number": 4
+     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
@@ -473,13 +466,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x303f210>"
+        "<matplotlib.figure.Figure at 0x3f07950>"
       ]
      }
     ],
-     "prompt_number": 5
+     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
@@ -522,21 +515,21 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x336abd0>"
+        "<matplotlib.figure.Figure at 0x3bebfd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 6,
+       "prompt_number": 13,
       "text": [
        "<function __main__.plt_g>"
       ]
      }
     ],
-     "prompt_number": 6
+     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
@@ -586,7 +579,7 @@
        "<style>\n",
        "    div.cell{\n",
        "        width: 850px;\n",
-        "        margin-left: 10% !important;\n",
+        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
        "    h1 {\n",
@@ -790,13 +783,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 7,
+       "prompt_number": 14,
       "text": [
-        "<IPython.core.display.HTML at 0x32f2810>"
+        "<IPython.core.display.HTML at 0x3be7650>"
       ]
      }
     ],
-     "prompt_number": 7
+     "prompt_number": 14
    }
   ],
   "metadata": {}
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:8accd92cc45907eefd7795f396b9f0472df32023d57bc6305d8c9def5e7ebde3"
+  "signature": "sha256:e0ecac7790474bde2b505f02a8ac90ba8b03bc7b61101e309b6e7ce83f8d7abc"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -20,6 +20,7 @@
     "collapsed": false,
     "input": [
      "#format the book\n",
+      "%matplotlib inline\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -31,7 +32,7 @@
        "<style>\n",
        "    div.cell{\n",
        "        width: 850px;\n",
-        "        margin-left: 10% !important;\n",
+        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
        "    h1 {\n",
@@ -235,13 +236,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 9,
       "text": [
-        "<IPython.core.display.HTML at 0x2765590>"
+        "<IPython.core.display.HTML at 0x2118f50>"
       ]
      }
     ],
-     "prompt_number": 1
+     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:7f0e4714da29bebd7f295be9d05ed70731b3573b9f5195ce553f68fc05a0f3f0"
+  "signature": "sha256:f25d2a0f796f1111451e1f65bc09a5db2fa05d439022118e16d8410eb07e755f"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -21,6 +21,7 @@
     "collapsed": false,
     "input": [
      "#format the book\n",
+      "%matplotlib inline\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -32,7 +33,7 @@
        "<style>\n",
        "    div.cell{\n",
        "        width: 850px;\n",
-        "        margin-left: 10% !important;\n",
+        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
        "    h1 {\n",
@@ -236,13 +237,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 6,
       "text": [
-        "<IPython.core.display.HTML at 0x16a2590>"
+        "<IPython.core.display.HTML at 0x3809290>"
       ]
      }
     ],
-     "prompt_number": 1
+     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
@@ -284,7 +285,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
@@ -298,7 +299,6 @@
     "cell_type": "code",
     "collapsed": false,
     "input": [
-      "from __future__ import print_function\n",
      "for i in range(20):\n",
      "    print(\"%.4f\" % random.randn(),end='\\t')"
     ],
@@ -309,11 +309,11 @@
       "output_type": "stream",
       "stream": "stdout",
       "text": [
-        "-0.0658\t-0.9407\t0.7251\t0.3994\t0.8913\t1.3388\t0.8315\t0.0733\t-0.4343\t0.1143\t-0.1462\t0.1979\t-1.6322\t0.8444\t-0.8237\t0.2480\t1.0962\t-1.5944\t-0.4201\t-1.9672\t"
+        "-0.7392\t0.9900\t1.6445\t-0.0367\t-0.3023\t2.3646\t-0.4459\t-0.9159\t-0.4865\t1.0919\t1.0198\t0.3202\t-2.9384\t-0.6675\t0.0283\t1.9098\t1.5129\t-1.3451\t-1.4038\t0.5213\t"
       ]
      }
     ],
-     "prompt_number": 3
+     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
@@ -328,7 +328,6 @@
     "cell_type": "code",
     "collapsed": false,
     "input": [
-      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.pylab as pylab\n",
      "\n",
@@ -351,24 +350,16 @@
        "1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 10.0000 "
       ]
      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/usr/local/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['monospace'] not found. Falling back to Bitstream Vera Sans\n",
-        "  (prop.get_family(), self.defaultFamily[fontext]))\n"
-       ]
-      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2eb78d0>"
+        "<matplotlib.figure.Figure at 0x3801750>"
       ]
      }
     ],
-     "prompt_number": 4
+     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
@@ -407,13 +398,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2fbe150>"
+        "<matplotlib.figure.Figure at 0x37d69d0>"
       ]
      }
     ],
-     "prompt_number": 5
+     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
@@ -440,13 +431,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x318f710>"
+        "<matplotlib.figure.Figure at 0x3801590>"
       ]
      }
     ],
-     "prompt_number": 6
+     "prompt_number": 11
    },
    {
     "cell_type": "code",
@@ -460,13 +451,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2fd2190>"
+        "<matplotlib.figure.Figure at 0x37bb950>"
       ]
      }
     ],
-     "prompt_number": 7
+     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
@@ -497,13 +488,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2fcac90>"
+        "<matplotlib.figure.Figure at 0x3800150>"
       ]
      }
     ],
-     "prompt_number": 8
+     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
@@ -535,13 +526,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2ec09d0>"
+        "<matplotlib.figure.Figure at 0x3bbfc50>"
       ]
      }
     ],
-     "prompt_number": 9
+     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
@@ -632,13 +623,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2ec7190>"
+        "<matplotlib.figure.Figure at 0x3bb8cd0>"
       ]
      }
     ],
-     "prompt_number": 10
+     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
@@ -680,13 +671,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x35a40d0>"
+        "<matplotlib.figure.Figure at 0x37c4390>"
       ]
      }
     ],
-     "prompt_number": 11
+     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
@@ -730,13 +721,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x35a4510>"
+        "<matplotlib.figure.Figure at 0x3bc7850>"
       ]
      }
     ],
-     "prompt_number": 12
+     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
@@ -768,7 +759,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
@@ -787,7 +778,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
@@ -816,30 +807,30 @@
       "output_type": "stream",
       "stream": "stdout",
       "text": [
-        "time: 0 \tposition = 0.203 \tvariance = 2.500\n",
-        "time: 1 \tposition = 0.282 \tvariance = 1.667\n",
-        "time: 2 \tposition = 0.201 \tvariance = 1.250\n",
-        "time: 3 \tposition = 0.341 \tvariance = 1.000\n",
-        "time: 4 \tposition = 0.313 \tvariance = 0.833\n",
-        "time: 5 \tposition = 0.239 \tvariance = 0.714\n",
-        "time: 6 \tposition = -0.085 \tvariance = 0.625\n",
-        "time: 7 \tposition = -0.184 \tvariance = 0.556\n",
-        "time: 8 \tposition = -0.042 \tvariance = 0.500\n",
-        "time: 9 \tposition = 0.042 \tvariance = 0.455\n",
-        "time: 10 \tposition = -0.020 \tvariance = 0.417\n",
-        "time: 11 \tposition = 0.027 \tvariance = 0.385\n",
-        "time: 12 \tposition = 0.095 \tvariance = 0.357\n",
-        "time: 13 \tposition = 0.041 \tvariance = 0.333\n",
-        "time: 14 \tposition = -0.086 \tvariance = 0.312\n",
-        "time: 15 \tposition = 0.008 \tvariance = 0.294\n",
-        "time: 16 \tposition = 0.039 \tvariance = 0.278\n",
-        "time: 17 \tposition = 0.042 \tvariance = 0.263\n",
-        "time: 18 \tposition = 0.020 \tvariance = 0.250\n",
-        "time: 19 \tposition = 0.008 \tvariance = 0.238\n"
+        "time: 0 \tposition = 1.279 \tvariance = 2.500\n",
+        "time: 1 \tposition = 0.494 \tvariance = 1.667\n",
+        "time: 2 \tposition = 0.575 \tvariance = 1.250\n",
+        "time: 3 \tposition = 0.403 \tvariance = 1.000\n",
+        "time: 4 \tposition = 0.338 \tvariance = 0.833\n",
+        "time: 5 \tposition = 0.389 \tvariance = 0.714\n",
+        "time: 6 \tposition = 0.214 \tvariance = 0.625\n",
+        "time: 7 \tposition = 0.113 \tvariance = 0.556\n",
+        "time: 8 \tposition = 0.081 \tvariance = 0.500\n",
+        "time: 9 \tposition = 0.055 \tvariance = 0.455\n",
+        "time: 10 \tposition = 0.004 \tvariance = 0.417\n",
+        "time: 11 \tposition = 0.039 \tvariance = 0.385\n",
+        "time: 12 \tposition = -0.001 \tvariance = 0.357\n",
+        "time: 13 \tposition = -0.005 \tvariance = 0.333\n",
+        "time: 14 \tposition = 0.002 \tvariance = 0.312\n",
+        "time: 15 \tposition = 0.056 \tvariance = 0.294\n",
+        "time: 16 \tposition = 0.137 \tvariance = 0.278\n",
+        "time: 17 \tposition = 0.038 \tvariance = 0.263\n",
+        "time: 18 \tposition = 0.018 \tvariance = 0.250\n",
+        "time: 19 \tposition = -0.012 \tvariance = 0.238\n"
       ]
      }
     ],
-     "prompt_number": 15
+     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
@@ -900,7 +891,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 16
+     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
@@ -950,47 +941,47 @@
       "stream": "stdout",
       "text": [
        "UPDATE: 1.0000,\t502.0000\n",
-        "SENSE:  3.3611,\t9.8047\n",
+        "SENSE:  2.1520,\t9.8047\n",
        "\n",
-        "UPDATE: 4.3611,\t11.8047\n",
-        "SENSE:  1.4162,\t5.4138\n",
+        "UPDATE: 3.1520,\t11.8047\n",
+        "SENSE:  1.5245,\t5.4138\n",
        "\n",
-        "UPDATE: 2.4162,\t7.4138\n",
-        "SENSE:  2.9041,\t4.2574\n",
+        "UPDATE: 2.5245,\t7.4138\n",
+        "SENSE:  4.3510,\t4.2574\n",
        "\n",
-        "UPDATE: 3.9041,\t6.2574\n",
-        "SENSE:  5.3093,\t3.8490\n",
+        "UPDATE: 5.3510,\t6.2574\n",
+        "SENSE:  2.1933,\t3.8490\n",
        "\n",
-        "UPDATE: 6.3093,\t5.8490\n",
-        "SENSE:  6.1276,\t3.6904\n",
+        "UPDATE: 3.1933,\t5.8490\n",
+        "SENSE:  3.9438,\t3.6904\n",
        "\n",
-        "UPDATE: 7.1276,\t5.6904\n",
-        "SENSE:  5.3634,\t3.6267\n",
+        "UPDATE: 4.9438,\t5.6904\n",
+        "SENSE:  5.8211,\t3.6267\n",
        "\n",
-        "UPDATE: 6.3634,\t5.6267\n",
-        "SENSE:  5.7098,\t3.6007\n",
+        "UPDATE: 6.8211,\t5.6267\n",
+        "SENSE:  9.0421,\t3.6007\n",
        "\n",
-        "UPDATE: 6.7098,\t5.6007\n",
-        "SENSE:  7.8917,\t3.5900\n",
+        "UPDATE: 10.0421,\t5.6007\n",
+        "SENSE:  10.0587,\t3.5900\n",
        "\n",
-        "UPDATE: 8.8917,\t5.5900\n",
-        "SENSE:  8.3542,\t3.5856\n",
+        "UPDATE: 11.0587,\t5.5900\n",
+        "SENSE:  9.2783,\t3.5856\n",
        "\n",
-        "UPDATE: 9.3542,\t5.5856\n",
-        "SENSE:  10.7324,\t3.5838\n",
+        "UPDATE: 10.2783,\t5.5856\n",
+        "SENSE:  10.0624,\t3.5838\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3338350>"
+        "<matplotlib.figure.Figure at 0x3a4f250>"
       ]
      }
     ],
-     "prompt_number": 17
+     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
@@ -1112,17 +1103,17 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x33289d0>"
+        "<matplotlib.figure.Figure at 0x3c5e9d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x391bb10>"
+        "<matplotlib.figure.Figure at 0x4004690>"
       ]
      },
      {
@@ -1133,7 +1124,7 @@
       ]
      }
     ],
-     "prompt_number": 18
+     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
@@ -1183,7 +1174,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 19
+     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
@@ -1242,17 +1233,17 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x35d0650>"
+        "<matplotlib.figure.Figure at 0x3bdbc10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x35b3a50>"
+        "<matplotlib.figure.Figure at 0x4006150>"
       ]
      },
      {
@@ -1260,11 +1251,11 @@
       "stream": "stdout",
       "text": [
        "Variance converges to 0.0907297673624\n",
-        "Last voltage is 15.230349143\n"
+        "Last voltage is 16.3744898612\n"
       ]
      }
     ],
-     "prompt_number": 20
+     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
@@ -1289,7 +1280,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 21
+     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
@@ -1323,13 +1314,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3328190>"
+        "<matplotlib.figure.Figure at 0x3c7d9d0>"
       ]
      }
     ],
-     "prompt_number": 22
+     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
@@ -1386,21 +1377,21 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x35dad10>"
+        "<matplotlib.figure.Figure at 0x37c7450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3928690>"
+        "<matplotlib.figure.Figure at 0x40e5c10>"
       ]
      }
     ],
-     "prompt_number": 23
+     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
@@ -1459,13 +1450,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3333790>"
+        "<matplotlib.figure.Figure at 0x37c70d0>"
       ]
      }
     ],
-     "prompt_number": 24
+     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
@@ -1516,13 +1507,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x35b5790>"
+        "<matplotlib.figure.Figure at 0x3c75a90>"
       ]
      }
     ],
-     "prompt_number": 25
+     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
@@ -1573,13 +1564,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3940a90>"
+        "<matplotlib.figure.Figure at 0x4009a10>"
       ]
      }
     ],
-     "prompt_number": 26
+     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
@@ -1625,13 +1616,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x30c0710>"
+        "<matplotlib.figure.Figure at 0x3bb7c10>"
       ]
      }
     ],
-     "prompt_number": 27
+     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
@@ -1673,13 +1664,13 @@
      {
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 28,
+       "prompt_number": 33,
       "text": [
        "<function __main__.plot_kalman_filter>"
       ]
      }
     ],
-     "prompt_number": 28
+     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
@@ -1728,21 +1719,21 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3340d90>"
+        "<matplotlib.figure.Figure at 0x3c75ed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 29,
+       "prompt_number": 34,
       "text": [
        "<function __main__.plot_kalman_filter>"
       ]
      }
     ],
-     "prompt_number": 29
+     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
@@ -1772,7 +1763,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 30
+     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
@@ -1814,13 +1805,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3607a90>"
+        "<matplotlib.figure.Figure at 0x3c7bb50>"
       ]
      }
     ],
-     "prompt_number": 31
+     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
@@ -1846,7 +1837,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 32
+     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
@@ -1888,13 +1879,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x334d290>"
+        "<matplotlib.figure.Figure at 0x3bdb910>"
       ]
      }
     ],
-     "prompt_number": 33
+     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
@@ -1923,7 +1914,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 33
+     "prompt_number": 38
    },
    {
     "cell_type": "code",
@@ -1963,29 +1954,29 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x3340650>"
+        "<matplotlib.figure.Figure at 0x3ffc2d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 34,
+       "prompt_number": 39,
       "text": [
-        "[<matplotlib.lines.Line2D at 0x3928550>]"
+        "[<matplotlib.lines.Line2D at 0x3bdb1d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x35b6c90>"
+        "<matplotlib.figure.Figure at 0x3bbf290>"
       ]
      }
     ],
-     "prompt_number": 34
+     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:3d6dcb53cebe9cf8598551dcc370e439718b360c783852dd1b9c8c9eeb335e02"
+  "signature": "sha256:a6b3d341e235ad0a59766c340223d8f6a8c7a2128056e004ae5a109129dd293a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -22,6 +22,7 @@
     "collapsed": false,
     "input": [
      "#format the book\n",
+      "%matplotlib inline\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -33,7 +34,7 @@
        "<style>\n",
        "    div.cell{\n",
        "        width: 850px;\n",
-        "        margin-left: 10% !important;\n",
+        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
        "    h1 {\n",
@@ -237,13 +238,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 25,
       "text": [
-        "<IPython.core.display.HTML at 0x2a81610>"
+        "<IPython.core.display.HTML at 0x27fbb10>"
       ]
      }
     ],
-     "prompt_number": 1
+     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
@@ -328,7 +329,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
@@ -351,7 +352,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
@@ -369,7 +370,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
@@ -387,7 +388,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
@@ -413,7 +414,7 @@
       ]
      }
     ],
-     "prompt_number": 6
+     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
@@ -442,7 +443,7 @@
       ]
      }
     ],
-     "prompt_number": 7
+     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
@@ -493,29 +494,21 @@
      {
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 32,
       "text": [
        "(-10, 20)"
       ]
      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/usr/local/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['monospace'] not found. Falling back to Bitstream Vera Sans\n",
-        "  (prop.get_family(), self.defaultFamily[fontext]))\n"
-       ]
-      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x4869ed0>"
+        "<matplotlib.figure.Figure at 0x281ba10>"
       ]
      }
     ],
-     "prompt_number": 8
+     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
@@ -553,13 +546,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x4720350>"
+        "<matplotlib.figure.Figure at 0x2b7dcd0>"
       ]
      }
     ],
-     "prompt_number": 9
+     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
@@ -636,13 +629,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x4702650>"
+        "<matplotlib.figure.Figure at 0x2b6a550>"
       ]
      }
     ],
-     "prompt_number": 10
+     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
@@ -663,13 +656,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x48b9390>"
+        "<matplotlib.figure.Figure at 0x22ef390>"
       ]
      }
     ],
-     "prompt_number": 11
+     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
@@ -690,13 +683,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x45ce5d0>"
+        "<matplotlib.figure.Figure at 0x2bf4c50>"
       ]
      }
     ],
-     "prompt_number": 12
+     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
@@ -721,13 +714,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x4739390>"
+        "<matplotlib.figure.Figure at 0x2cb4550>"
       ]
      }
     ],
-     "prompt_number": 13
+     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
@@ -767,13 +760,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x4739050>"
+        "<matplotlib.figure.Figure at 0x2bf4a10>"
       ]
      }
     ],
-     "prompt_number": 14
+     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
@@ -915,7 +908,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
@@ -939,7 +932,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 16
+     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
@@ -1034,21 +1027,21 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x51fc990>"
+        "<matplotlib.figure.Figure at 0x2cd5290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x51f3d10>"
+        "<matplotlib.figure.Figure at 0x27f9290>"
       ]
      }
     ],
-     "prompt_number": 17
+     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
@@ -1091,13 +1084,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x4b595d0>"
+        "<matplotlib.figure.Figure at 0x284b110>"
       ]
      }
     ],
-     "prompt_number": 18
+     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
@@ -1166,21 +1159,21 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x4b5b210>"
+        "<matplotlib.figure.Figure at 0x285d710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x4c4af50>"
+        "<matplotlib.figure.Figure at 0x2ce57d0>"
       ]
      }
     ],
-     "prompt_number": 19
+     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
@@ -1211,13 +1204,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x51f8dd0>"
+        "<matplotlib.figure.Figure at 0x2cd8d90>"
       ]
      }
     ],
-     "prompt_number": 20
+     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
@@ -1282,13 +1275,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x473d790>"
+        "<matplotlib.figure.Figure at 0x2beb810>"
       ]
      }
     ],
-     "prompt_number": 21
+     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
@@ -1311,13 +1304,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x4d29f90>"
+        "<matplotlib.figure.Figure at 0x285e3d0>"
       ]
      }
     ],
-     "prompt_number": 22
+     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
@@ -1402,21 +1395,21 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x4b50fd0>"
+        "<matplotlib.figure.Figure at 0x27f6850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x4c42e50>"
+        "<matplotlib.figure.Figure at 0x278b710>"
       ]
      }
     ],
-     "prompt_number": 23
+     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
@@ -1464,7 +1457,7 @@
        "<style>\n",
        "    div.cell{\n",
        "        width: 850px;\n",
-        "        margin-left: 10% !important;\n",
+        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
        "    h1 {\n",
@@ -1668,13 +1661,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 24,
+       "prompt_number": 48,
       "text": [
-        "<IPython.core.display.HTML at 0x4b4aa50>"
+        "<IPython.core.display.HTML at 0x2844350>"
       ]
      }
     ],
-     "prompt_number": 24
+     "prompt_number": 48
    }
   ],
   "metadata": {}
@@ -6,8 +6,6 @@ def load_style():
    styles = open("./styles/custom2.css", "r").read()
    return HTML(styles)

-
-pylab.rcParams['figure.figsize'] = 12,6
 pylab.rcParams['axes.color_cycle'] = '348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33'
 pylab.rcParams['lines.linewidth'] = 1

@@ -18,7 +16,7 @@ pylab.rcParams['patch.edgecolor'] = 'eeeeee'
 pylab.rcParams['patch.antialiased'] = True
 pylab.rcParams['font.family'] = 'monospace'
 pylab.rcParams['font.size'] = 10.0
-pylab.rcParams['font.monospace'] = 'Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace'
+#pylab.rcParams['font.monospace'] = 'Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace'
 pylab.rcParams['axes.facecolor'] = 'eeeeee'
 pylab.rcParams['axes.edgecolor'] = 'bcbcbc'
 pylab.rcParams['axes.linewidth'] = 1
@@ -37,4 +35,5 @@ pylab.rcParams['legend.fancybox'] = True
 pylab.rcParams['figure.facecolor'] = '0.85'
 pylab.rcParams['figure.edgecolor'] = '0.50'
 pylab.rcParams['figure.subplot.hspace'] = 0.5
+pylab.rcParams['figure.figsize'] = 12,6

@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+from IPython.core.display import HTML
+import matplotlib.pylab as pylab
+
+def load_style():
+    styles = open("./styles/custom2.css", "r").read()
+    return HTML(styles)
+
+pylab.rcParams['axes.color_cycle'] = '348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33'
+pylab.rcParams['lines.linewidth'] = 1
+
+pylab.rcParams['lines.antialiased'] = True
+pylab.rcParams['patch.linewidth'] = 0.5
+pylab.rcParams['patch.facecolor'] = '348ABD' #blue
+pylab.rcParams['patch.edgecolor'] = 'eeeeee'
+pylab.rcParams['patch.antialiased'] = True
+pylab.rcParams['font.family'] = 'monospace'
+pylab.rcParams['font.size'] = 10.0
+pylab.rcParams['font.monospace'] = 'Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace'
+pylab.rcParams['axes.facecolor'] = 'eeeeee'
+pylab.rcParams['axes.edgecolor'] = 'bcbcbc'
+pylab.rcParams['axes.linewidth'] = 1
+pylab.rcParams['axes.grid'] = True
+pylab.rcParams['axes.titlesize'] = 'x-large'
+pylab.rcParams['axes.labelsize'] = 'large'
+pylab.rcParams['axes.labelcolor'] = '555555'
+pylab.rcParams['axes.axisbelow'] = True
+pylab.rcParams['axes.color_cycle'] = '348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33'
+pylab.rcParams['xtick.major.pad'] = 6
+pylab.rcParams['xtick.minor.size'] = 0
+pylab.rcParams['xtick.minor.pad'] = 6
+pylab.rcParams['xtick.color'] = '555555'
+pylab.rcParams['ytick.direction'] = 'in'
+pylab.rcParams['legend.fancybox'] = True
+pylab.rcParams['figure.facecolor'] = '0.85'
+pylab.rcParams['figure.edgecolor'] = '0.50'
+pylab.rcParams['figure.subplot.hspace'] = 0.5
+pylab.rcParams['figure.figsize'] = 12,6
+
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:5d2660e0f612f8eba9585339a1f5ee844e45a59b1f388e4115e04a587c67b011"
+  "signature": "sha256:82faa9340e18d14b79321a236acd20d0ba6eba274151ae39fd0fa04f7496473a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -22,6 +22,7 @@
     "collapsed": false,
     "input": [
      "#format the book\n",
+      "%matplotlib inline\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -33,7 +34,7 @@
        "<style>\n",
        "    div.cell{\n",
        "        width: 850px;\n",
-        "        margin-left: 10% !important;\n",
+        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
        "    h1 {\n",
@@ -237,13 +238,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 16,
       "text": [
-        "<IPython.core.display.HTML at 0xf05590>"
+        "<IPython.core.display.HTML at 0x3793450>"
       ]
      }
     ],
-     "prompt_number": 1
+     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
@@ -327,24 +328,16 @@
     "language": "python",
     "metadata": {},
     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/usr/local/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['monospace'] not found. Falling back to Bitstream Vera Sans\n",
-        "  (prop.get_family(), self.defaultFamily[fontext]))\n"
-       ]
-      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAFyCAYAAAD/MLwxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x27245d0>"
+        "<matplotlib.figure.Figure at 0x3793c90>"
       ]
      }
     ],
-     "prompt_number": 2
+     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
@@ -371,13 +364,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2828bd0>"
+        "<matplotlib.figure.Figure at 0x3793a50>"
       ]
      }
     ],
-     "prompt_number": 3
+     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
@@ -405,13 +398,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2724b90>"
+        "<matplotlib.figure.Figure at 0x3416f10>"
       ]
      }
     ],
-     "prompt_number": 4
+     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
@@ -443,13 +436,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2834610>"
+        "<matplotlib.figure.Figure at 0x3446390>"
       ]
      }
     ],
-     "prompt_number": 5
+     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
@@ -478,13 +471,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2836290>"
+        "<matplotlib.figure.Figure at 0x3415390>"
       ]
      }
     ],
-     "prompt_number": 6
+     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
@@ -532,13 +525,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2c9f850>"
+        "<matplotlib.figure.Figure at 0x3434d10>"
       ]
      }
     ],
-     "prompt_number": 7
+     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
@@ -580,13 +573,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x283c5d0>"
+        "<matplotlib.figure.Figure at 0x3a5f190>"
       ]
      }
     ],
-     "prompt_number": 8
+     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
@@ -669,13 +662,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x299d950>"
+        "<matplotlib.figure.Figure at 0x3427790>"
       ]
      }
     ],
-     "prompt_number": 9
+     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
@@ -715,7 +708,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
@@ -740,13 +733,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x29a3f50>"
+        "<matplotlib.figure.Figure at 0x3445e10>"
       ]
      }
     ],
-     "prompt_number": 11
+     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
@@ -776,13 +769,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2724c10>"
+        "<matplotlib.figure.Figure at 0x341dd10>"
       ]
      }
     ],
-     "prompt_number": 12
+     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
@@ -826,13 +819,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x29a5090>"
+        "<matplotlib.figure.Figure at 0x3412e10>"
       ]
      }
     ],
-     "prompt_number": 13
+     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
@@ -870,13 +863,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x2cb1a10>"
+        "<matplotlib.figure.Figure at 0x3a60e10>"
       ]
      }
     ],
-     "prompt_number": 14
+     "prompt_number": 29
    },
    {
     "cell_type": "code",
@@ -885,7 +878,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 29
    },
    {
     "cell_type": "code",
@@ -894,7 +887,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 29
    },
    {
     "cell_type": "code",
@@ -903,7 +896,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 29
    },
    {
     "cell_type": "code",
@@ -912,7 +905,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
@@ -937,7 +930,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 29
    },
    {
     "cell_type": "code",
@@ -958,7 +951,7 @@
        "<style>\n",
        "    div.cell{\n",
        "        width: 850px;\n",
-        "        margin-left: 10% !important;\n",
+        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
        "    h1 {\n",
@@ -1162,13 +1155,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 15,
+       "prompt_number": 30,
       "text": [
-        "<IPython.core.display.HTML at 0x29a7ad0>"
+        "<IPython.core.display.HTML at 0x37b3ad0>"
       ]
      }
     ],
-     "prompt_number": 15
+     "prompt_number": 30
    }
   ],
   "metadata": {}
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:3d773f480fbe09157a394cf32f8c6a660062f7144c89a064fee3bf3c44734623"
+  "signature": "sha256:42bb8e5d1f668d44e5a23d83f81c99c39dd2677d0be5f61bf2fe32773e18d86f"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -20,6 +20,7 @@
     "collapsed": false,
     "input": [
      "#format the book\n",
+      "%matplotlib inline\n",
      "import book_format\n",
      "book_format.load_style()"
     ],
@@ -31,7 +32,7 @@
        "<style>\n",
        "    div.cell{\n",
        "        width: 850px;\n",
-        "        margin-left: 10% !important;\n",
+        "        margin-left: 0% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
        "    h1 {\n",
@@ -235,13 +236,13 @@
       ],
       "metadata": {},
       "output_type": "pyout",
-       "prompt_number": 1,
+       "prompt_number": 23,
       "text": [
-        "<IPython.core.display.HTML at 0x21bb610>"
+        "<IPython.core.display.HTML at 0x2e9cf10>"
       ]
      }
     ],
-     "prompt_number": 1
+     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
@@ -279,7 +280,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
@@ -297,7 +298,7 @@
     "language": "python",
     "metadata": {},
     "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
@@ -310,7 +311,6 @@
     "cell_type": "code",
     "collapsed": false,
     "input": [
-      "%matplotlib inline\n",
      "from __future__ import print_function, division\n",
      "import matplotlib.pyplot as plt\n",
      "import bar_plot\n",
@@ -322,24 +322,16 @@
     "language": "python",
     "metadata": {},
     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/usr/local/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['monospace'] not found. Falling back to Bitstream Vera Sans\n",
-        "  (prop.get_family(), self.defaultFamily[fontext]))\n"
-       ]
-      },
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x39db350>"
+        "<matplotlib.figure.Figure at 0x321f1d0>"
       ]
      }
     ],
-     "prompt_number": 4
+     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
@@ -368,7 +360,7 @@
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      }
     ],
-     "prompt_number": 5
+     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
@@ -403,7 +395,7 @@
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     ],
-     "prompt_number": 6
+     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
@@ -460,7 +452,7 @@
       ]
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-     "prompt_number": 7
+     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
@@ -510,7 +502,7 @@
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-     "prompt_number": 8
+     "prompt_number": 30
    },
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     "cell_type": "markdown",
@@ -566,7 +558,7 @@
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+     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
@@ -612,7 +604,7 @@
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-     "prompt_number": 10
+     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
@@ -640,7 +632,7 @@
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+     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
@@ -663,13 +655,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x21bb490>"
+        "<matplotlib.figure.Figure at 0x306e690>"
       ]
      }
     ],
-     "prompt_number": 12
+     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
@@ -701,13 +693,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3ad9a10>"
+        "<matplotlib.figure.Figure at 0x3219b10>"
       ]
      }
     ],
-     "prompt_number": 13
+     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
@@ -740,13 +732,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3b04790>"
+        "<matplotlib.figure.Figure at 0x303ce10>"
       ]
      }
     ],
-     "prompt_number": 14
+     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
@@ -790,13 +782,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3d09a50>"
+        "<matplotlib.figure.Figure at 0x3063310>"
       ]
      }
     ],
-     "prompt_number": 15
+     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
@@ -827,13 +819,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3cfcbd0>"
+        "<matplotlib.figure.Figure at 0x3224c50>"
       ]
      }
     ],
-     "prompt_number": 16
+     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
@@ -856,13 +848,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3ad20d0>"
+        "<matplotlib.figure.Figure at 0x32165d0>"
       ]
      }
     ],
-     "prompt_number": 17
+     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
@@ -885,13 +877,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3cf4410>"
+        "<matplotlib.figure.Figure at 0x2f65310>"
       ]
      }
     ],
-     "prompt_number": 18
+     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
@@ -931,9 +923,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3aecd10>"
+        "<matplotlib.figure.Figure at 0x3049610>"
       ]
      },
      {
@@ -945,7 +937,7 @@
       ]
      }
     ],
-     "prompt_number": 19
+     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
@@ -968,13 +960,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x39df910>"
+        "<matplotlib.figure.Figure at 0x303c3d0>"
       ]
      }
     ],
-     "prompt_number": 20
+     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
@@ -1000,13 +992,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3d0b950>"
+        "<matplotlib.figure.Figure at 0x30726d0>"
       ]
      }
     ],
-     "prompt_number": 21
+     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
@@ -1048,13 +1040,13 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x3ceebd0>"
+        "<matplotlib.figure.Figure at 0x322acd0>"
       ]
      }
     ],
-     "prompt_number": 22
+     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
@@ -1093,116 +1085,6 @@
      "**If you can understand this chapter you will be able to understand and implement Kalman filters** I cannot stress this enough. If anything is murky, go back and reread this chapter and play with the code. the rest of this book will build on the algorithms that we use here. If you don't intuitively understand why this histogram filter works, and can at least work through the math, you will have little success with the rest of the material. However, if you grasp the fundamental insight - multiplying probabilities when we measure, and shifting probabilities when we update leads to a converging solution - then you understand everything important you need to grasp the Kalman filter. "
     ]
    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#format the book\n",
-      "from IPython.core.display import HTML\n",
-      "def css_styling():\n",
-      "    styles = open(\"./styles/custom.css\", \"r\").read()\n",
-      "    return HTML(styles)\n",
-      "css_styling()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<link href='http://fonts.googleapis.com/css?family=Fenix' rel='stylesheet' type='text/css'>\n",
-        "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n",
-        "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' type='text/css'>\n",
-        "<style>\n",
-        "    @font-face {\n",
-        "        font-family: \"Computer Modern\";\n",
-        "        src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
-        "    }\n",
-        "    div.cell{\n",
-        "        width:800px;\n",
-        "        margin-left:16% !important;\n",
-        "        margin-right:auto;\n",
-        "    }\n",
-        "    h1 {\n",
-        "        font-family: 'Alegreya Sans', sans-serif;\n",
-        "    }\n",
-        "    h2 {\n",
-        "        font-family: 'Fenix', serif;\n",
-        "    }\n",
-        "    h3{\n",
-        "\t\tfont-family: 'Fenix', serif;\n",
-        "        margin-top:12px;\n",
-        "        margin-bottom: 3px;\n",
-        "       }\n",
-        "\th4{\n",
-        "\t\tfont-family: 'Fenix', serif;\n",
-        "       }\n",
-        "    h5 {\n",
-        "        font-family: 'Alegreya Sans', sans-serif;\n",
-        "    }\t   \n",
-        "    div.text_cell_render{\n",
-        "        font-family: 'Alegreya Sans',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
-        "        line-height: 135%;\n",
-        "        font-size: 120%;\n",
-        "        width:600px;\n",
-        "        margin-left:auto;\n",
-        "        margin-right:auto;\n",
-        "    }\n",
-        "    .CodeMirror{\n",
-        "            font-family: \"Source Code Pro\";\n",
-        "\t\t\tfont-size: 90%;\n",
-        "    }\n",
-        "/*    .prompt{\n",
-        "        display: None;\n",
-        "    }*/\n",
-        "    .text_cell_render h1 {\n",
-        "        font-weight: 200;\n",
-        "        font-size: 50pt;\n",
-        "\t\tline-height: 100%;\n",
-        "        color:#CD2305;\n",
-        "        margin-bottom: 0.5em;\n",
-        "        margin-top: 0.5em;\n",
-        "        display: block;\n",
-        "    }\t\n",
-        "    .text_cell_render h5 {\n",
-        "        font-weight: 300;\n",
-        "        font-size: 16pt;\n",
-        "        color: #CD2305;\n",
-        "        font-style: italic;\n",
-        "        margin-bottom: .5em;\n",
-        "        margin-top: 0.5em;\n",
-        "        display: block;\n",
-        "    }\n",
-        "    \n",
-        "    .warning{\n",
-        "        color: rgb( 240, 20, 20 )\n",
-        "        }  \n",
-        "</style>\n",
-        "<script>\n",
-        "    MathJax.Hub.Config({\n",
-        "                        TeX: {\n",
-        "                           extensions: [\"AMSmath.js\"]\n",
-        "                           },\n",
-        "                tex2jax: {\n",
-        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
-        "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
-        "                },\n",
-        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
-        "                \"HTML-CSS\": {\n",
-        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
-        "                }\n",
-        "        });\n",
-        "</script>\n"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 23,
-       "text": [
-        "<IPython.core.display.HTML at 0x3af2110>"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
    {
     "cell_type": "markdown",
     "metadata": {},